{
  "job_id": "20260507T202641961301Z",
  "queued_at": "2026-05-07T20:26:41.961352+00:00",
  "status": "queued",
  "job_type": "session_memory_analysis",
  "payload": {
    "session_id": "285fc5cf",
    "session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-06T05-21-05-935Z_019dfbbb-8d8e-717d-8fc3-d1f6e0b2a955.jsonl",
    "reason": "before_switch:new",
    "cwd": "/home/sebas",
    "scope": "global",
    "user_messages": 36,
    "assistant_messages": 282,
    "last_user_text": "hace handoff que se lo doy a agente nuevo",
    "last_assistant_text": "Handoff: ## Estado general Workflow nuevo Prisma/local-first quedó andando. ## Ya hecho ### Auth bug prod - encontrado en `/api/account/session` - causa: sign-in usaba lookups “pesados” y explotaba - fix aplicado y pusheado - E2E agregado para: - signup club - logout - signin - entrar a `/clubs/coaches` ### Prisma workflow nuevo Modelo final: - `pnpm dev` siempre usa DB local - `pnpm db:migrate --name <slug>` para schema changes - `pre-push` solo corre al push a `main` - `pre-push main` hace: - `docs:check` - `db:local:up` - `db:deploy` - `db:check-migrations` - `lint` - `typecheck` - `test:contracts` - `build` - CI PR: - Postgres del job - `db:deploy` - `db:generate` - `db:check-migrations` - workflow prod: - `.github/workflows/db-deploy-production.yml` - trigger en `push` a `main` - corre `pnpm db:deploy` contra prod ### Docker / local Postgres - instalados: - `docker.io` - `docker-compose` - scripts locales funcionan - `pnpm db:local:up` ya levanta Postgres por Docker - happy path probado: - `db:local:up` - `db:deploy` - `dev` - `/coaches` responde 200 - prepush completo pasa ### Clone prod -> local Agregado: - `pnpm db:clone-prod-to-local` Hace: - dump de prod - replace DB local - restore - `db:deploy` - `db:generate` Requiere: - `CLONE_LOCAL_DB_CONFIRM=1` ## Prod migration workflow Se resolvió también: - secret primero faltaba - luego apuntaba a DB equivocada - luego faltaba baseline Prisma Se ejecutó baseline inicial en prod: - `prisma migrate resolve --applied 20260506094000_init` Después: - rerun workflow `db-deploy-production` - quedó **success** ## Commits relevantes ya en main - `4a733cd` Enforce Prisma migration workflow - `3094b62` Fix club auth regression and cover it with E2E - `947ac6f` Align Prisma checks and payment route contracts - `c355d30` Simplify Prisma workflow around local dev DB - `06fadbe` Enable local Docker Postgres happy path - `20e4261` Add production-to-local clone command ## Estado pendiente importante Queda encarar **drift viejo**…",
    "branch": [
      {
        "type": "model_change",
        "id": "6b9dfce7",
        "parentId": null,
        "timestamp": "2026-05-06T05:21:07.558Z",
        "provider": "openai-codex",
        "modelId": "gpt-5.4"
      },
      {
        "type": "thinking_level_change",
        "id": "c1149c59",
        "parentId": "6b9dfce7",
        "timestamp": "2026-05-06T05:21:07.558Z",
        "thinkingLevel": "low"
      },
      {
        "type": "message",
        "id": "26e08cb1",
        "parentId": "c1149c59",
        "timestamp": "2026-05-06T05:24:59.274Z",
        "message": {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "Necesito que uses PyLoop con 15 iteraciones con el objetivo de resolver un bug que tenemos en producción (https://ballbox.app/coaches). Puedes probarlo local o probarlo en producción. Donde en Barra Coaches te armas una cuenta que en el momento se persiste automáticamente en la DB, la usas para iniciar sesión y al clicar la sección de profesores te tira error de que no puede continuar y te vuelve para atrás. Necesito que intentas el flow. Te fijes que error tira la UI o Network o Logs y intentará arreglarlo de esa manera.\nSi en el proceso necesitas crear herramientas o skills, hazlo.\nUna que te podría servir mucho es la del browser que menciona Mario acá https://mariozechner.at/posts/2025-11-02-what-if-you-dont-need-mcp/"
            }
          ],
          "timestamp": 1778045099265
        }
      },
      {
        "type": "message",
        "id": "2f1257a0",
        "parentId": "26e08cb1",
        "timestamp": "2026-05-06T05:25:03.114Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Considering next steps**\n\nI need to look into using PyLoop and inspect the repository for details. I might want to create a todo list for this. Also, checking memory could be helpful, but I’m not sure it’s necessary right now. I should consider loading relevant skills if applicable. Additionally, searching the repository could help streamline the process. Let's get organized and create that todo list to keep track of all these tasks!",
              "thinkingSignature": "{\"id\":\"rs_0cedd82c95a270c80169fad0ac27008195a6a19f207e10fa47\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-tCuC_ShgcrLF2vj89BSmLZhTKwNPQPPQxhyWYH6SSYPCGG2u16iDq226OwESUwfIEkuheJMsYaW-J8SEZIqZvt7770aNsfHhKTdxw4-cTKNz_gDxqZkSYUA-o6mRAvrPDNJa-rED6WHaJZTKGeC_yKoiWTxenUGFeF19d36C_yIaDztNt-ZOc0baWJWKRVW51kTSP4D6Wr3QQu0SO4jDSSuXQPAc-JRI4C-TBj-_-FxWn7n6gwmPiW3Mk21QHtnes34dffYsgS1TwulvxiO2s41SiX1A1FmR0N5DfPAjATDyvKFh2mneluQSmNhFT_R9CAZ3ffZ5mKnfGy0HgFcPuyNEGunmoGjIg5j6XxAllOGyg8Fuil_bbv8N6ugMdA0slQ7FmslI9NuYkDraMXlZykyy8M9gvobLgCMEoez4kJ96mTgyln9KyHxBSw7ZXyRZVn_ZzL4cyUJUvXR11z7E2B066edWrMghVkJeLKOjq6vzcGUpOTKk7JuSEqjNrDZd4glWYdohF1wVVH5kxErUN1RVgMswFT06Jma3hNAE61RzEfdEG4iXfxm6QLkLbrP4wtk5vcY9gJZkqywht9rIWJqGIzJr_TLIZzv5OdRrgbdwQdE2MDB_PPOMGNs3tUjdnbZvfOI5fHpUBh32Xze9xTUvBLlI_j3pV3atg6xm1n4PXJQcY8toaRouQ1Y4WUZZT0VLwBpueuRAGKfvwy8LR2L8aVVoggn9ikr9EIQYjuZzaFKG4tmSgaoY2y6MV_Mc8m3fYFshOCPHl8dqBz4FumhFMT57gMCbDDVdn4c0d215N1c7PSR5oQx0rj8bWRiP2fsqWneynkuHKPVYWEsqVGfMRMvKj2-45GDRT8sV5C4H9KHpxqdek4UhNYfZtwzSvxYxQiTKsFpYQ2KvWXkBjDdikfXadXcu_B0mhz11XWa3iES1Xo3BuPyGVcTPMEx7ThH85r-nbH6Qkm_JTyfMK8vZ_TlL-eXt1EFC_2AghkRBYKbwq3rLOjgyAAF481xuQ1wUv-cq29PkUzjV5LntjesyCwWf8tz0CpmBAywloWEpTp1B1Vx9cJh413qt8MMAK1DeZ_TPXoNGdN-4VoAhH-cgpMAXXN6L8RXbXYb_FU4j4QJs32CkOVYmhpZXoGzLxyGnx1QbZE6qVX5_0nO4XiZKya0I3WNmjdV414qhvXw5jU=\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Considering next steps**\\n\\nI need to look into using PyLoop and inspect the repository for details. I might want to create a todo list for this. Also, checking memory could be helpful, but I’m not sure it’s necessary right now. I should consider loading relevant skills if applicable. Additionally, searching the repository could help streamline the process. Let's get organized and create that todo list to keep track of all these tasks!\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_LrCjpTBNdhWNn2lZG7oX7sIo|fc_0cedd82c95a270c80169fad0aea298819592b000f05f9c6be5",
              "name": "todo",
              "arguments": {
                "action": "create",
                "subject": "Repro coaches login/professors bug with PyLoop-style iteration",
                "description": "Use up to 15 iterations to reproduce the production/local bug on /coaches, inspect UI/network/logs, identify cause, implement fix if possible, and verify.",
                "activeForm": "iterating on coaches bug",
                "status": "in_progress"
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 4127,
            "output": 131,
            "cacheRead": 0,
            "cacheWrite": 0,
            "totalTokens": 4258,
            "cost": {
              "input": 0.0103175,
              "output": 0.001965,
              "cacheRead": 0,
              "cacheWrite": 0,
              "total": 0.0122825
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1778045099268,
          "responseId": "resp_0cedd82c95a270c80169fad0ab839c819580e3c09dc86f27d2"
        }
      },
      {
        "type": "message",
        "id": "66f1d178",
        "parentId": "2f1257a0",
        "timestamp": "2026-05-06T05:25:03.119Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_LrCjpTBNdhWNn2lZG7oX7sIo|fc_0cedd82c95a270c80169fad0aea298819592b000f05f9c6be5",
          "toolName": "todo",
          "content": [
            {
              "type": "text",
              "text": "Created #1: Repro coaches login/professors bug with PyLoop-style iteration (pending)"
            }
          ],
          "details": {
            "action": "create",
            "params": {
              "action": "create",
              "subject": "Repro coaches login/professors bug with PyLoop-style iteration",
              "description": "Use up to 15 iterations to reproduce the production/local bug on /coaches, inspect UI/network/logs, identify cause, implement fix if possible, and verify.",
              "activeForm": "iterating on coaches bug",
              "status": "in_progress"
            },
            "tasks": [
              {
                "id": 1,
                "subject": "Repro coaches login/professors bug with PyLoop-style iteration",
                "status": "pending",
                "description": "Use up to 15 iterations to reproduce the production/local bug on /coaches, inspect UI/network/logs, identify cause, implement fix if possible, and verify.",
                "activeForm": "iterating on coaches bug"
              }
            ],
            "nextId": 2
          },
          "isError": false,
          "timestamp": 1778045103118
        }
      },
      {
        "type": "message",
        "id": "9353dad7",
        "parentId": "66f1d178",
        "timestamp": "2026-05-06T05:25:04.788Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "",
              "thinkingSignature": "{\"id\":\"rs_0cedd82c95a270c80169fad0affa408195ac01f158418af413\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-tCwVHybCv2wlEtDvoE7fe73582qkeKWIm_8oHH6nZh1N2oE1xa0eoxjm5sQhZIXAKbIG-7pXLnqYgQYTzLT7YbG6RHlAS5IXjBo9KWZzaty_yA5rD2nTe7OIAnt1nu6E2rmjPQMQ-2QOr63Ep6C4AdfRWEwhMPBSEVa7E25ZxuT3Vzr-o8LcndVKd17Er17JFY5kkdFipxdZ7i2YkKqnzIbktp26YQpGkMbBp6Zpp0HAANDzL_LHtvn8vAIMG0JSNqjx79zpiWGFjdICrM7z34smNbn9ycO_D56yogiFaZqvHYv5YNF6bYaLsRZI3i-nWW3WeYeqp8SfbTaLRNu23ICEid3CF-luEcLpwq3fuJHKcV2qKQyz8uHTgev1LYWmXghYvP8PQ-T6fN5MiknDt1eStKSTMLtgpfJrQdnix-E2RTIzquI3E2MjStj50bVEbYup2YZwxMc1bg0S43HjRcGFBJsatOk7eBQxXxZy7JUm81flSz4kOkqEbu5q7V16ShfOPXgQkdI582goimYlrRtoOtCFRQebPt9v7mf5T_F5IYUWgPJVrpZyPSKjdu8qhIB7PmUBiLjkrHB2Rr9z2e7uPYsDSE45JghAL8n-pEoKxvnUfpZSTodiKi9rURw4cq4aX7QQJOBT0TalPMgRT3N62tLChycZOZ3znWMwBd5Srjgo7epzvuwJBeEgOw0I59UEZmmzYss49m_--d9TmXrYDxpMshqPd5IPUHxJyLKbQFOtSYxX7TOVC9L1yAL78TU-t59b90HYHhGDZ8RaY6ePVEbmZtB9hX0CNkYcESX6HKP5hF9dM1vWK2OpsCNrjJgsoaWWk_xNKya7x5syf016jIdIeS0iVEU_7_l5hrlXkqb5NfwNxdzZpmOrXy0jtr-cyQni2AZQcLXb5u5_tmKtklu1uQnQkTz-OVyLyNaNwMp-BVT5LmyCObLmZee5CA4\",\"summary\":[]}"
            },
            {
              "type": "toolCall",
              "id": "call_v4fTiVcVdOjZa72tulw3KSQV|fc_0cedd82c95a270c80169fad0b076b88195adb0da93d07f3abe",
              "name": "todo",
              "arguments": {
                "action": "update",
                "id": 1,
                "status": "in_progress"
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 705,
            "output": 38,
            "cacheRead": 3584,
            "cacheWrite": 0,
            "totalTokens": 4327,
            "cost": {
              "input": 0.0017625000000000002,
              "output": 0.00057,
              "cacheRead": 0.000896,
              "cacheWrite": 0,
              "total": 0.0032285000000000005
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1778045103119,
          "responseId": "resp_0cedd82c95a270c80169fad0af5a7081959fd56c590f5435cc"
        }
      },
      {
        "type": "message",
        "id": "d81e0348",
        "parentId": "9353dad7",
        "timestamp": "2026-05-06T05:25:04.789Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_v4fTiVcVdOjZa72tulw3KSQV|fc_0cedd82c95a270c80169fad0b076b88195adb0da93d07f3abe",
          "toolName": "todo",
          "content": [
            {
              "type": "text",
              "text": "Updated #1 (pending → in_progress)"
            }
          ],
          "details": {
            "action": "update",
            "params": {
              "action": "update",
              "id": 1,
              "status": "in_progress"
            },
            "tasks": [
              {
                "id": 1,
                "subject": "Repro coaches login/professors bug with PyLoop-style iteration",
                "status": "in_progress",
                "description": "Use up to 15 iterations to reproduce the production/local bug on /coaches, inspect UI/network/logs, identify cause, implement fix if possible, and verify.",
                "activeForm": "iterating on coaches bug"
              }
            ],
            "nextId": 2
          },
          "isError": false,
          "timestamp": 1778045104789
        }
      },
      {
        "type": "message",
        "id": "a67bccfd",
        "parentId": "d81e0348",
        "timestamp": "2026-05-06T05:25:09.457Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "",
              "thinkingSignature": "{\"id\":\"rs_0cedd82c95a270c80169fad0b1848c81959495a411a6d464bc\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-tC14qT3I-n5uPkc9NFPxsVCn5tLzxRydpUzNFPBx5vN79I0ZSnh1ssEPsMh0woFGX4GtpyrF2iXIhs59c5Wv4F5hBKn5UG9atKTWlJM_u8HuUocqiArFsqdZ53i18gDedNhgrm0mZEj7CF9Z_U2UIaFHwJIO0flDZ9rvYEUYAz3mwgiN_YxOCuu9StZCj87wmGWswHBVqfL0CDFSJZ6BzvNKQH09X3jNk-qCHvJNdsINqBJQMuf1xD2IodF65-JNj8bg08Mp3tCIhdfDNNJ3CNHCVB1XH269Zf4H4ttJ3kVqlK9RODbUIVxII32xSWuaN7Eh_e6vbyqXbsBRNOxEgoHs1YXm0rxfOUHTMwmBkqN0wx7lfBzuFXWd_bnLWXKCZOywKY9ORFrfM4kwXaxlQFZK-tmD7-1Or9-8eXQSXkuaf_NJ_IcAWZUNDrbkmvIrJFUYKp8njeAD0kdBobYGnqmwEWiKQ2mw--X0OPIpErKSm_aw0pXqUElPvSBEC1yrbMyzHCYiK_VClO1rDl543awKcwiB9B4BChYqGENLKDHmexOqcGmBpter1SkR0bF_TJblYCHxxEzv5fshRLpNSUE5vDQ55nABmQ7utHcVLJHPvBzbC-7yf7xv0SNeK1BapxfaWv_xDDTLr73n0mzKBNFi4XqYLL_zhcYxsusO6zOCTAe1uWt-vW9KgaYMix1_sNstAhdxXQuhxsaKsQVmCqjK-2nKZqsOF1-MUe1vvveCvv88T7lFASIIox1J2SAIOnXi80pMyJwb9u2g8XYyYPKw6G6Tz4EOAeWVurEgr9ZAt39nO9NY3lC5eWGEo6ohgNtR6sq9yoQRFuXsGsBq3rtNM7Xe7FEOVbOgIi47ao_D79MkoXBfdAdHrvROKr2oPU2emUt4yDBLRXcfy4M3Jsx3-rZQkq19Oi1fGWJ51auvBJ1irE_0ujo2OGOQOKrCXe7\",\"summary\":[]}"
            },
            {
              "type": "toolCall",
              "id": "call_FcTZZ1svIV1p6vn5El34O9fg|fc_0cedd82c95a270c80169fad0b52b408195b166e06ed6c5ea84",
              "name": "bash",
              "arguments": {
                "command": "pwd && ls -la && find /home/sebas -maxdepth 3 -type d \\( -name .git -o -name node_modules \\) -prune -o -maxdepth 3 -type f \\( -name package.json -o -name pyproject.toml -o -name docker-compose.yml -o -name README.md \\) -print | sed 's#^/home/sebas/##' | head -300",
                "timeout": 10
              }
            },
            {
              "type": "toolCall",
              "id": "call_WWJZ45qrFSGRucfUEmZgMSFM|fc_0cedd82c95a270c80169fad0b52b508195a097373223fa4066",
              "name": "read",
              "arguments": {
                "path": "/home/sebas/.agents/skills/tmux-pi-job-run/SKILL.md"
              }
            },
            {
              "type": "toolCall",
              "id": "call_nvPqh75rkX5oEMpbOUlsnAdH|fc_0cedd82c95a270c80169fad0b52b5c81959f9bbf3329333570",
              "name": "read",
              "arguments": {
                "path": "/home/sebas/.agents/skills/tmux-pi-job-setup/SKILL.md"
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 250,
            "output": 202,
            "cacheRead": 4096,
            "cacheWrite": 0,
            "totalTokens": 4548,
            "cost": {
              "input": 0.000625,
              "output": 0.00303,
              "cacheRead": 0.001024,
              "cacheWrite": 0,
              "total": 0.004679
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1778045104789,
          "responseId": "resp_0cedd82c95a270c80169fad0b103c08195a56d754e5ed33ddf"
        }
      },
      {
        "type": "message",
        "id": "38868a94",
        "parentId": "a67bccfd",
        "timestamp": "2026-05-06T05:25:09.521Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_FcTZZ1svIV1p6vn5El34O9fg|fc_0cedd82c95a270c80169fad0b52b408195b166e06ed6c5ea84",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "/home/sebas\ntotal 356\ndrwxr-xr-x+ 47 sebas sebas  4096 May  6 01:05 .\ndrwxr-xr-x   4 root  root   4096 Jan 27 22:05 ..\ndrwxr-xr-x   5 sebas sebas  4096 Apr 21 23:44 .agents\ndrwxrwxr-x  11 sebas sebas  4096 Apr 22 16:57 agents-database\n-rw-rw-r--   1 sebas sebas   113 Apr 20 15:41 AGENTS.md\ndrwx------   3 sebas sebas  4096 Jan 16 17:38 .ansible\ndrwxr-xr-x  16 sebas sebas  4096 May  5 15:28 ballbox\n-rw-rw-r--   1 sebas sebas     0 Apr 19 17:55 .bash_aliases\n-rw-------   1 sebas sebas 12462 May  6 01:05 .bash_history\n-rw-r--r--   1 sebas sebas   220 Dec  4 11:40 .bash_logout\n-rw-r--r--   1 sebas sebas  5346 May  5 12:33 .bashrc\n-rw-r--r--   1 sebas sebas  5357 Apr 20 10:56 .bashrc.bak.20260420105624\ndrwxrwxr-x   6 sebas sebas  4096 Apr 19 15:56 browser-harness\ndrwxr-xr-x   9 sebas sebas  4096 Feb 15 15:49 bug-bounty\ndrwxr-xr-x   4 sebas sebas  4096 May  1 01:52 .bun\ndrwxr-xr-x  12 sebas sebas  4096 May  6 02:13 .cache\ndrwxr-xr-x  14 sebas sebas  4096 Jan 27 21:52 .claude\n-rw-------   1 sebas sebas  4503 Jan 27 21:52 .claude.json\n-rw-------   1 sebas sebas  4504 Jan 27 21:52 .claude.json.backup\n-rw-r--r--   1 sebas sebas   403 Jan 28 11:38 client_secret_1019422495774-0cvo8lefqh667vgmekec8ruqdnrior71.apps.googleusercontent.com.json\n-rw-rw-r--   1 sebas sebas     0 Dec  4 11:51 .cloud-locale-test.skip\ndrwxr-xr-x   9 sebas sebas  4096 Apr 30 14:45 .codex\ndrwx------  23 sebas sebas  4096 May  5 12:12 .config\ndrwxrwxr-x   2 sebas sebas  4096 Dec  4 11:51 Desktop\ndrwxr-xr-x   2 sebas sebas  4096 Dec  4 11:51 Documents\ndrwxrwxr-x   3 sebas sebas  4096 Apr 18 09:35 downloads\ndrwxr-xr-x   2 sebas sebas  4096 Apr 23 16:37 Downloads\ndrwxr-xr-x   4 sebas sebas  4096 May  5 12:22 eiffel-monitor\n-rw-rw-r--   1 sebas sebas   279 Apr 16 18:19 .gitconfig\n-rw-------   1 sebas sebas    73 Apr 15 18:12 .git-credentials\ndrwxrwxr-x   3 sebas sebas  4096 May  1 01:52 go\ndrwxrwxr-x   9 sebas sebas  4096 Feb 21 10:23 health-system\ndrwxr-xr-x   4 sebas sebas  4096 Apr 23 22:53 .hist\n-rw-r--r--   1 sebas sebas  2312 Apr 20 18:51 inspect-ourvend.spec.ts\ndrwx------   5 sebas sebas  4096 Apr 14 12:54 .local\ndrwxrwxr-x   2 sebas sebas  4096 Apr 21 12:17 logs\ndrwxr-xr-x   5 sebas sebas  4096 May  5 12:33 media\n-rw-r--r--   1 sebas sebas  5983 Apr 21 22:29 memory-audit-report.html\n-rw-r--r--   1 sebas sebas 15426 May  4 18:29 microsd-migration-handoff.md\ndrwxr-xr-x   2 sebas sebas  4096 May  5 12:12 migration-backups\ndrwx------   5 sebas sebas  4096 May  5 12:15 .mozilla\ndrwxr-xr-x   2 sebas sebas  4096 Dec  4 11:51 Music\n-rw-rw-r--   1 sebas sebas   282 Apr 16 12:11 notas\ndrwxr-xr-x   5 sebas sebas  4096 Feb  1 21:56 .npm\ndrwxr-xr-x   4 sebas sebas  4096 Jan 27 11:48 .npm-global\n-rw-------   1 sebas sebas    31 Jan 27 11:47 .npmrc\n-rw-r--r--   1 sebas sebas     0 Mar 18 15:06 .opencode-web.log\n-rw-rw-r--   1 sebas sebas   472 Apr 14 23:17 opencode-web.log\ndrwxrwxr-x   2 sebas sebas  4096 Apr 16 22:38 ops-notes\ndrwxrwxr-x   5 sebas sebas  4096 May  1 16:06 .pi\ndrwxrwxr-x   9 sebas sebas  4096 May  6 02:21 pi-config\ndrwxr-xr-x   2 sebas sebas  4096 Dec  4 11:51 Pictures\n-rw-r--r--   1 sebas sebas  4571 Apr 13 09:56 pihole-unbound-tailscale-blueprint.bck.md\n-rw-r--r--   1 sebas sebas 16865 Apr 13 10:08 pihole-unbound-tailscale-blueprint.md\ndrwxr-xr-x   3 sebas sebas  4096 Apr 30 20:33 pi-jobs\ndrwx------   3 sebas sebas  4096 Jan 17 11:24 .pki\ndrwxr-xr-x   8 sebas sebas  4096 Apr 10 17:00 playwright-browsers\ndrwxrwxr-x   3 sebas sebas  4096 Apr 19 15:41 .pnpm-store\n-rw-rw-r--   1 sebas sebas  2001 Apr 21 10:17 PORTAL-SYSTEM-RUNBOOK.md\n-rw-r--r--   1 sebas sebas   884 May  5 12:33 .profile\ndrwxr-xr-x   2 sebas sebas  4096 Dec  4 11:51 Public\ndrwxrwxr-x   2 sebas sebas  4096 Apr 16 16:32 scripts\n-rw-r--r--   1 sebas sebas  3519 Apr 21 10:27 SERVICES-ARCHITECTURE.md\ndrwx------   2 sebas sebas  4096 Apr 29 17:39 .ssh\n-rw-r--r--   1 sebas sebas     0 Dec  4 11:51 .sudo_as_admin_successful\ndrwxr-xr-x   6 sebas sebas  4096 Jan 15 22:26 telegram-bot-listener-backup-20260118\ndrwxr-xr-x   2 sebas sebas  4096 Dec  4 11:51 Templates\ndrwxr-xr-x   2 sebas sebas  4096 Apr 20 18:54 test-results\n-rw-rw-r--   1 sebas sebas  1274 Apr 21 18:39 .tmux.conf\ndrwxr-xr-x   3 sebas sebas  4096 Apr 22 10:37 .venvs\ndrwxrwxr-x   3 sebas sebas  4096 Apr 18 16:31 venvs\ndrwxr-xr-x   2 sebas sebas  4096 Dec  4 11:51 Videos\n-rw-r--r--   1 sebas sebas   215 Apr 21 16:30 .wget-hsts\n-rw-------   1 sebas sebas    58 May  5 10:07 .Xauthority\n-rw-------   1 sebas sebas  3853 May  5 10:07 .xsession-errors\n-rw-------   1 sebas sebas  3901 May  5 10:06 .xsession-errors.old\ndrwxr-xr-x   2 sebas sebas  4096 Apr 30 14:25 .zed_server\neiffel-monitor/README.md\npi-config/README.md\nballbox/.next/package.json\nballbox/docker-compose.yml\nballbox/README.md\nballbox/package.json\nbrowser-harness/README.md\nbrowser-harness/package.json\nhealth-system/README.md\nagents-database/.pytest_cache/README.md\nagents-database/pyproject.toml\nbug-bounty/README.md\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045109521
        }
      },
      {
        "type": "message",
        "id": "1aa62565",
        "parentId": "38868a94",
        "timestamp": "2026-05-06T05:25:09.522Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_WWJZ45qrFSGRucfUEmZgMSFM|fc_0cedd82c95a270c80169fad0b52b508195a097373223fa4066",
          "toolName": "read",
          "content": [
            {
              "type": "text",
              "text": "---\nname: tmux-pi-job-run\ndescription: Run and inspect a native detached one-prompt pi job\n---\n\n# tmux-pi-job-run\n\n## Workflow\n\n1. Start a job in the target directory:\n   ```bash\n   /home/sebas/pi-config/bin/pi-job-start --cwd /path/to/repo --name short-slug --prompt 'Your one-shot prompt here'\n   ```\n2. Inspect current state whenever needed:\n   ```bash\n   /home/sebas/pi-config/bin/pi-job-check <job-id>\n   ```\n3. Block until it finishes only when useful:\n   ```bash\n   /home/sebas/pi-config/bin/pi-job-wait <job-id> --poll-seconds 10\n   ```\n4. List jobs:\n   ```bash\n   /home/sebas/pi-config/bin/pi-job-list\n   ```\n5. Stop a job if needed:\n   ```bash\n   /home/sebas/pi-config/bin/pi-job-stop <job-id>\n   ```\n\n## Guidance\n\n- caller chooses how long to wait and how often to poll\n- use `pi-job-check` to judge whether the run looks stalled\n- this workflow assumes one prompt and one result only\n- runner uses `pi --no-extensions -p` to avoid extension-side session issues in batch mode\n- logs live in `/home/sebas/pi-jobs/jobs/<job-id>/output.log`\n- runtime state is file-backed; no tmux session is involved for batch jobs\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045109521
        }
      },
      {
        "type": "message",
        "id": "f69aff28",
        "parentId": "1aa62565",
        "timestamp": "2026-05-06T05:25:09.525Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_nvPqh75rkX5oEMpbOUlsnAdH|fc_0cedd82c95a270c80169fad0b52b5c81959f9bbf3329333570",
          "toolName": "read",
          "content": [
            {
              "type": "text",
              "text": "---\nname: tmux-pi-job-setup\ndescription: Set up native detached one-prompt pi jobs with file logs and simple polling\n---\n\n# tmux-pi-job-setup\n\n## Use When\n\n- replacing extension-based side-agent orchestration with simpler native Linux tools\n- you need one-prompt `pi` runs in an exact existing directory\n- you want detached one-shot jobs plus plain file logs\n\n## Workflow\n\n1. Update `/home/sebas/pi-config/SERVICES-ARCHITECTURE.md` first if launcher or job-runtime behavior changes.\n2. Keep runtime storage under `/home/sebas/pi-jobs` unless there is a strong reason not to.\n3. Use or update these scripts in `/home/sebas/pi-config/bin/`:\n   - `pi-job-start`\n   - `pi-job-check`\n   - `pi-job-wait`\n   - `pi-job-list`\n   - `pi-job-stop`\n   - `pi-job-runner`\n4. Keep the model simple:\n   - exact `cwd`\n   - one initial prompt\n   - `pi --no-extensions -p` in a detached background process\n   - file-backed metadata, `pid`, and logs\n   - polling by the caller, not cron\n5. Do not add follow-up key injection unless the user explicitly asks.\n\n## Runtime Layout\n\n```text\n/home/sebas/pi-jobs/jobs/<job-id>/\n  meta.env\n  prompt.txt\n  output.log\n  status\n  pid\n  exit_code\n  started_at\n  finished_at\n```\n\n## Rules\n\n- prefer shell and plain files over extra daemons\n- no fixed stall timeout policy; leave polling cadence to the caller\n- use `pi-job-check` for current state visibility\n- use `pi-job-wait` only when blocking is the right choice\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045109521
        }
      },
      {
        "type": "message",
        "id": "a592fe81",
        "parentId": "f69aff28",
        "timestamp": "2026-05-06T05:25:15.097Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "",
              "thinkingSignature": "{\"id\":\"rs_0cedd82c95a270c80169fad0b66ac4819588d969fe21da04a1\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-tC6fQlPHkOK4PeaKNdLWzr8J-LMUK3BHihUFhAwu9XfLSxU_zl2-B3cbJO9TH5yfuqdVrhVIPTNInWgFB8nevlF0w6UDHYumbWs0jhyHDNsdq6CDWwjMncPOd82YuEUGmMYbF07DlTDEkASt9Ch8N1xKY2joZWzlw1-s3KPf4T4pPPsktlYwN_KNtYhn290T3ZAJtzfnGMMqoyT0Z4yG3vVtF9WkO63v0mp6gAjtT1eOxgUHPiuzPIDojuLCkEaNUr80tag31LOMCrzwKTPt4NiL0AE2Kb4vnyEpeYbwBKxKWZubXlj-kEcByKPztHeQ_SJXQ8neMUSOKYussOwEAU45top9LL_STyaxGrOFzY1wR6207Z2hRr2Sw3eTXG_Yp6viUHERh64t41braoFCnDc01K-qIFQ-NCeegcqiB5CUxbhPuDjifet6sv7VypiIvnwaC8k7BOKUyYY4jQrOcW-KFp5y_yBiSljYHBFbVDTxYCHVJ5Trk4RiHAoWqte-S3RyUyX30BDu6tXxtNSlkGCZyKQ0dWUsi1inUrLwOl_hziGmlGkJoMNdOGO2SmbL7n9yvV_407s4JrdQndqjLY97o4XJuLkNf8EHPNAIyy8ltimTzmSozxl5l4uYf6Br5IzHD22sJsIw7okcl1c7ABHvpEpzdnrVwQDnfIH8e0t6NQHWdA2i1ckvCTmxj_9Eb4la3zggkeTUVofrcpXe7MpRZe7GWhQkaTWEyj63exkIhZyj0A3Oe1ROzfDTny4JV5te8plhFQtBHKNV4Nhq_cLu6pFgh9NF-1o2bDDdK9AtaO96RVm-93EnE0VZxGNdP8msSXyWaSJk4Andq8VYUUXU09EVOqw_3weEm9IbXKc3KeP23wg1-zIXs4AlglVzzEXmnOOL_PmmL66iMUbCsDYbQhDZqdCxnBwLhbZxHdEDZIm9_aYNFZ_6fXqkHRwPIBxfWuLkBiiYrj4LIK6epLYt5hOv48O7b1_4jQx8hGO3Yq0k0Km20uzdiDnOz0NCnVgRkRnRUPZCZ75ZYbCC0eThA==\",\"summary\":[]}"
            },
            {
              "type": "toolCall",
              "id": "call_pfCC2HJog0x4wu2b1KJbuEiQ|fc_0cedd82c95a270c80169fad0ba9994819596fee2f35e4d1129",
              "name": "bash",
              "arguments": {
                "command": "cd /home/sebas/ballbox && rg -n \"PyLoop|pyloop|piloop|loop\" -S . | head -200",
                "timeout": 10
              }
            },
            {
              "type": "toolCall",
              "id": "call_QdUv4q1sFjlkcIJWHjF110WS|fc_0cedd82c95a270c80169fad0ba99a48195a99a5f2755785ee2",
              "name": "read",
              "arguments": {
                "path": "/home/sebas/ballbox/package.json"
              }
            },
            {
              "type": "toolCall",
              "id": "call_QcfKtkPNySUQffKFJnLwOkI0|fc_0cedd82c95a270c80169fad0ba99ac8195be184ab2e853a635",
              "name": "read",
              "arguments": {
                "path": "/home/sebas/ballbox/README.md"
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 3432,
            "output": 139,
            "cacheRead": 4096,
            "cacheWrite": 0,
            "totalTokens": 7667,
            "cost": {
              "input": 0.00858,
              "output": 0.002085,
              "cacheRead": 0.001024,
              "cacheWrite": 0,
              "total": 0.011689000000000001
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1778045109522,
          "responseId": "resp_0cedd82c95a270c80169fad0b5c65081958afcaf92ba8c2d6f"
        }
      },
      {
        "type": "message",
        "id": "4626c3b1",
        "parentId": "a592fe81",
        "timestamp": "2026-05-06T05:25:15.168Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_pfCC2HJog0x4wu2b1KJbuEiQ|fc_0cedd82c95a270c80169fad0ba9994819596fee2f35e4d1129",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "./README.md:27:- `/classes/me` ya muestra historial demo de solicitudes por email para seguimiento liviano del loop\n./PLAN.md:77:- `/classes/me` now ships as a lightweight read-only student follow-up view (email-filtered request history) so demos can show the request loop without claiming full reservation confirmation.\n./PLAN.md:257:- Expand ATC scope beyond public-read only after the request loop proves it is needed.\n./PLAN.md:278:  7. MVP PRD for one workflow loop\n./components/screen-player.tsx:138:            loop\n./components/hero.tsx:102:                  loop\n./app/player/[screenId]/route.ts:102:        <video id=\"player-video\" class=\"video\" autoplay muted loop playsinline controls></video>\n./app/classes/(authenticated)/me/page.tsx:57:        <p className=\"text-xs uppercase tracking-[0.24em] text-[#c4d600]\">Demo loop</p>\n./docs/coaches-discovery-stage.md:13:- The next sports step is not \"invent the module\"; it is to validate which coach workflow deserves a product loop.\n./docs/coaches-discovery-stage.md:49:   - one workflow loop only, with success metric and exclusions\n./docs/coaches-discovery-stage.md:59:- MVP PRD scoped to one loop, not a generic \"SaaS para coaches de padel\" bundle.\n./docs/coaches-discovery-execution-plan.md:70:- Goal: one workflow loop, one user, one success metric.\n./docs/atc-integration-discovery.md:11:- The open question is not whether ATC matters strategically; it is how much ATC access Ballbox needs for the first valuable loop.\n./docs/streetcast-parity.md:4:- Manifest polling: covered by `/player/[screenId]` lightweight runtime refresh loop.\n./docs/streetcast-parity.md:36:3. Confirm the video starts and loops without a visible reload gap while the diagnostics stay on `blob`.\n./docs/coaches-validation-checklist-2-weeks.md:38:- Define what Ballbox would improve in one loop.\n./docs/coaches-validation-checklist-2-weeks.md:42:- Keep ATC central to the Ballbox thesis even if the first validated loop starts with lighter integration depth.\n./docs/coaches-validation-checklist-2-weeks.md:51:- Draft the MVP PRD for one workflow loop.\n./docs/atc-mock-integration-plan.md:61:- once a wedge is chosen, define the exact minimum ATC client surface needed for that loop before touching implementation\n./docs/coaches-opportunity-brief.md:56:  - focuses on one recurring operational loop instead of trying to solve scheduling, CRM, payments, and growth all at once\n./docs/coaches-opportunity-brief.md:72:  - the workflow can be narrowed into one MVP loop\n./docs/coaches-coordination-gap-analysis.md:9:- Ballbox does not yet have the actual coordination loop; it only has the roster/event foundation.\n./docs/coaches-coordination-gap-analysis.md:122:4. add the smallest write path needed for the class state update loop\n./docs/coaches-solution-thesis.md:53:  - narrow enough for one loop\n./docs/coaches-jtbd-workflow-map.md:57:- Why they are insufficient: fragmented tools do not provide one operational loop for repeated classes\n./docs/coaches-jtbd-workflow-map.md:62:- Do player or club records help? likely yes later, unclear for first loop\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045115167
        }
      },
      {
        "type": "message",
        "id": "0347aac9",
        "parentId": "4626c3b1",
        "timestamp": "2026-05-06T05:25:15.168Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_QdUv4q1sFjlkcIJWHjF110WS|fc_0cedd82c95a270c80169fad0ba99a48195a99a5f2755785ee2",
          "toolName": "read",
          "content": [
            {
              "type": "text",
              "text": "{\n  \"name\": \"my-v0-project\",\n  \"version\": \"0.1.0\",\n  \"private\": true,\n  \"scripts\": {\n    \"build\": \"node scripts/next-build-safe.mjs\",\n    \"build:e2e\": \"pnpm build\",\n    \"build:prod-local\": \"dotenv -e .env.production -- next build\",\n    \"docs:check\": \"node scripts/check-doc-references.mjs\",\n    \"db:generate\": \"dotenv -e .env.local -- prisma generate\",\n    \"db:backfill-atc-clubs\": \"dotenv -e .env.local -- tsx scripts/backfill-atc-clubs.ts\",\n    \"db:local:down\": \"docker compose down\",\n    \"db:local:logs\": \"docker compose logs -f postgres\",\n    \"db:local:up\": \"docker compose up -d postgres\",\n    \"db:push\": \"dotenv -e .env.local -- prisma db push\",\n    \"db:revalidate-atc-clubs\": \"dotenv -e .env.local -- tsx scripts/revalidate-atc-clubs.ts\",\n    \"db:seed\": \"dotenv -e .env.local -- tsx prisma/seed.ts\",\n    \"db:studio\": \"dotenv -e .env.local -- prisma studio\",\n    \"dev\": \"next dev --port 3010\",\n    \"prepare\": \"node scripts/install-git-hooks.mjs\",\n    \"lint\": \"eslint .\",\n    \"postinstall\": \"prisma generate\",\n    \"pretypecheck\": \"rm -rf .next/types .next/dev/types\",\n    \"test:contracts\": \"vitest run tests/contracts\",\n    \"test:unit\": \"vitest run tests/unit\",\n    \"test:integration\": \"vitest run tests/integration --passWithNoTests\",\n    \"test:e2e\": \"playwright test\",\n    \"typecheck\": \"tsc --noEmit --incremental false\",\n    \"start\": \"next start\",\n    \"start:e2e\": \"next start --port 3020\",\n    \"start:prod-local\": \"dotenv -e .env.production -- next start\"\n  },\n  \"dependencies\": {\n    \"@hookform/resolvers\": \"^3.10.0\",\n    \"@prisma/client\": \"^5.22.0\",\n    \"@radix-ui/react-accordion\": \"1.2.2\",\n    \"@radix-ui/react-alert-dialog\": \"1.1.4\",\n    \"@radix-ui/react-aspect-ratio\": \"1.1.1\",\n    \"@radix-ui/react-avatar\": \"1.1.2\",\n    \"@radix-ui/react-checkbox\": \"1.1.3\",\n    \"@radix-ui/react-collapsible\": \"1.1.2\",\n    \"@radix-ui/react-context-menu\": \"2.2.4\",\n    \"@radix-ui/react-dialog\": \"1.1.4\",\n    \"@radix-ui/react-dropdown-menu\": \"2.1.4\",\n    \"@radix-ui/react-hover-card\": \"1.1.4\",\n    \"@radix-ui/react-label\": \"2.1.1\",\n    \"@radix-ui/react-menubar\": \"1.1.4\",\n    \"@radix-ui/react-navigation-menu\": \"1.2.3\",\n    \"@radix-ui/react-popover\": \"1.1.4\",\n    \"@radix-ui/react-progress\": \"1.1.1\",\n    \"@radix-ui/react-radio-group\": \"1.2.2\",\n    \"@radix-ui/react-scroll-area\": \"1.2.2\",\n    \"@radix-ui/react-select\": \"2.1.4\",\n    \"@radix-ui/react-separator\": \"1.1.1\",\n    \"@radix-ui/react-slider\": \"1.2.2\",\n    \"@radix-ui/react-slot\": \"1.1.1\",\n    \"@radix-ui/react-switch\": \"1.1.2\",\n    \"@radix-ui/react-tabs\": \"1.1.2\",\n    \"@radix-ui/react-toast\": \"1.2.4\",\n    \"@radix-ui/react-toggle\": \"1.1.1\",\n    \"@radix-ui/react-toggle-group\": \"1.1.1\",\n    \"@radix-ui/react-tooltip\": \"1.1.6\",\n    \"@vercel/analytics\": \"1.3.1\",\n    \"autoprefixer\": \"^10.4.20\",\n    \"class-variance-authority\": \"^0.7.1\",\n    \"clsx\": \"^2.1.1\",\n    \"cmdk\": \"1.0.4\",\n    \"date-fns\": \"4.1.0\",\n    \"embla-carousel-react\": \"8.5.1\",\n    \"input-otp\": \"1.4.1\",\n    \"lucide-react\": \"^0.454.0\",\n    \"next\": \"16.2.1\",\n    \"next-themes\": \"^0.4.6\",\n    \"react\": \"19.2.0\",\n    \"react-day-picker\": \"9.8.0\",\n    \"react-dom\": \"19.2.0\",\n    \"react-hook-form\": \"^7.60.0\",\n    \"react-resizable-panels\": \"^2.1.7\",\n    \"recharts\": \"2.15.4\",\n    \"sonner\": \"^1.7.4\",\n    \"tailwind-merge\": \"^3.3.1\",\n    \"tailwindcss-animate\": \"^1.0.7\",\n    \"vaul\": \"^1.1.2\",\n    \"zod\": \"3.25.76\"\n  },\n  \"devDependencies\": {\n    \"@playwright/test\": \"^1.58.2\",\n    \"@tailwindcss/postcss\": \"^4.1.9\",\n    \"@testing-library/dom\": \"^10.4.1\",\n    \"@testing-library/react\": \"^16.3.2\",\n    \"@types/node\": \"^22\",\n    \"@types/react\": \"^19\",\n    \"@types/react-dom\": \"^19\",\n    \"baseline-browser-mapping\": \"^2.10.9\",\n    \"dotenv-cli\": \"^11.0.0\",\n    \"eslint\": \"^9.39.4\",\n    \"eslint-config-next\": \"16.2.1\",\n    \"jsdom\": \"^29.0.1\",\n    \"postcss\": \"^8.5\",\n    \"prisma\": \"^5.22.0\",\n    \"tailwindcss\": \"^4.1.9\",\n    \"tsx\": \"^4.20.5\",\n    \"tw-animate-css\": \"1.3.3\",\n    \"typescript\": \"^5\",\n    \"vitest\": \"^4.1.0\"\n  }\n}\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045115168
        }
      },
      {
        "type": "message",
        "id": "7cd8aebd",
        "parentId": "0347aac9",
        "timestamp": "2026-05-06T05:25:15.171Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_QcfKtkPNySUQffKFJnLwOkI0|fc_0cedd82c95a270c80169fad0ba99ac8195be184ab2e853a635",
          "toolName": "read",
          "content": [
            {
              "type": "text",
              "text": "# Ballbox\n\nHost app de Ballbox en Next.js.\n\n## Qué hay hoy\n\n- Marketing site en `/`\n- Admin compartido en `/admin`\n- Surface estudiante en `/classes`\n- Módulo TVs operativo en Ballbox:\n  - `/admin/tvs`\n  - `/admin/screens`\n  - `/player/[screenId]`\n  - `/api/tvs/*`\n- Integración de pagos parcial:\n  - Ballbox recibe callbacks aprobados\n  - Ballbox recibe webhooks de MercadoPago en `/notification/mercadopago`\n  - Ballbox persiste `PaymentSession`\n  - migración de QR/order lifecycle hacia host app en progreso\n- Network con lectura/escritura real desde Postgres:\n  - `/admin/network`\n  - `/api/network`\n  - `/api/network/[clubId]`\n- Players ya tiene API/admin CRUD base sobre Postgres\n- Sports ya tiene API/admin CRUD base para coaches, open matches y classes\n- `/classes` ya tiene MVP autenticado con offers locales, request local y contexto ATC publico resiliente\n- `/classes/me` ya muestra historial demo de solicitudes por email para seguimiento liviano del loop\n\n## Stack\n\n- Next.js 16\n- React 19\n- Prisma\n- PostgreSQL\n- pnpm\n\n## Variables de entorno\n\nCopiar `.env.example` a `.env.local` y completar:\n\n- `DATABASE_URL`: Postgres local de desarrollo o Postgres remoto de Ballbox\n- `BALLBOX_PAYMENTS_URL`: URL base de `ballbox-payment-integration`\n- `BALLBOX_PAYMENTS_SHARED_SECRET`: bearer shared secret canónico para callback inbound y requests outbound hacia `ballbox-payment-integration`\n- `BALLBOX_PAYMENTS_CALLBACK_TOKEN`: fallback legacy; todavía aceptado si no existe `BALLBOX_PAYMENTS_SHARED_SECRET`\n- `BALLBOX_MERCADOPAGO_ACCESS_TOKEN`: access token de MercadoPago para fetch de order details\n- `BALLBOX_MERCADOPAGO_WEBHOOK_SECRET`: secreto webhook de MercadoPago para validar `x-signature`\n- `BALLBOX_MERCADOPAGO_EXTERNAL_POS_ID`: `external_pos_id` por defecto para creación de orders QR (se puede override por request)\n- `BALLBOX_ADMIN_PASSWORD`: shared password para `/admin`\n- `NEXT_PUBLIC_SITE_URL`: URL pública de Ballbox; usada para callback URLs y metadata\n- `BALLBOX_SEED_COACH_PASSWORD`: password para coaches seeded\n- `BALLBOX_SEED_CLUB_PASSWORD`: password para clubs seeded\n\n## Comandos\n\n```bash\npnpm db:local:up\npnpm db:local:down\npnpm db:local:logs\npnpm dev\npnpm lint\npnpm typecheck\npnpm test:contracts\npnpm test:unit\npnpm test:integration\npnpm test:e2e\npnpm build\npnpm db:generate\npnpm db:push\npnpm db:seed\n```\n\nNota:\n- Para Prisma local usar siempre `pnpm db:generate`, `pnpm db:push`, `pnpm db:seed` y `pnpm db:studio`.\n- Esos scripts cargan `.env.local` via `dotenv-cli`.\n- Evitar `pnpm prisma ...` directo si el shell no tiene `DATABASE_URL` exportada, porque ahi Prisma no ve `.env.local` automaticamente.\n\n## Postgres local\n\nPara desarrollo local ya existe `docker-compose.yml` con Postgres 16.\n\n1. `pnpm db:local:up`\n2. `pnpm db:push`\n3. `pnpm db:seed`\n4. `pnpm dev`\n\nCredenciales demo que deja el seed:\n\n- Coaches:\n  - `juan.silva@ballbox.demo`\n  - `valentina.acosta@ballbox.demo`\n- Clubs:\n  - `club.sanisidro@ballbox.demo`\n  - `club.palermo@ballbox.demo`\n- Password por default:\n  - `demo-pass`\n\n## Gate actual\n\nAntes de handoff:\n\n```bash\npnpm lint\npnpm typecheck\npnpm test:contracts\npnpm test:e2e\npnpm build\n```\n\nPush protection local:\n- Hay hook `pre-push` versionado en `.githooks/pre-push`.\n- Solo corre cuando el push incluye `main`.\n- Ejecuta `docs:check`, `lint`, `typecheck`, `test:contracts` y `build` antes de permitir el push.\n- En branches no-main, la validación ocurre en CI al abrir/actualizar PR.\n\n## Estado por dominio\n\n### TVs\n\n- Persistencia real en Ballbox Postgres\n- Manifests, heartbeats e impressions activos\n- Seed demo todavía usado como bootstrap local\n- Contrato formal de APIs TVs protegido por tests de contrato\n\n### Payments\n\n- Callback aprobado implementado\n- Webhook MercadoPago implementado en host app (`/notification/mercadopago`)\n- Creación de orders QR implementada en host app (`/api/integrations/payments/orders`)\n- Sesiones aprobadas listables desde Ballbox\n- Callback inbound endurecido con bearer shared secret + validación de `payment:{vendingMachineId}:{machineTxnId}`\n- Mapping `vendingMachine/slot/club/venue` pendiente\n- Bridge externo en transición (compatibilidad temporal)\n- Falta validación manual end-to-end con configuración real de MercadoPago y webhook público\n\n### Network\n\n- CRUD administrativo para clubs, venues y vending machines\n- Shared core canónico para TVs y payments\n- Naming canónico ya aplicado en schema, APIs y surface admin principal\n- Clubs nuevos quedan arriba en admin para feedback inmediato\n\n### Players\n\n- API DB-backed para listado de players\n- Modelos Prisma `Player` y `Membership` ya activos\n- CRUD admin base activo\n- Sin benefits/activity reales\n\n## Playwright\n\n- Cleanup determinístico before/after suite sobre fixtures `PW E2E` / `pw-e2e-*`, incluyendo `PaymentSession`\n- Cobertura actual:\n  - TVs runtime offline\n  - active nav en admin\n  - smoke mobile en `/admin/network` y `/admin/tvs`\n  - CRUD completo de `network`\n  - CRUD/reconciliation smoke de `payments`\n  - CRUD de `players`\n  - CRUD de `sports`\n\n### Sports\n\n- Admin/UI DB-backed para coaches, open matches y classes\n- Modelos Prisma `Coach`, `OpenMatch`, `Class` ya activos\n- MVP `/classes` con modelos Prisma `ClassOffer` y `ClassRequest`\n- `/classes` es publico; si falla ATC public-read la UI cae a unavailable sin romper la pagina\n- Validacion de experiencia de entrenadores en curso via formulario online; todavia sin suficiente evidencia de workflow real para fijar el wedge\n- Sin ATC write/admin ni inbox operador todavia\n\n## Docs\n\n- Strategic company/discovery mirror for the current coaches track also lives in `~/ballbox-company-agent/projects/coaches-os/`; source of truth for this repo remains `PLAN.md` plus local product docs\n\n- `PLAN.md`: estado y próximos pasos\n- `docs/index.md`\n- `docs/next-steps-queue.md`\n- `docs/repo-map.md`\n- `docs/coaches-discovery-stage.md`\n- `docs/coaches-form-current.md`\n- `docs/atc-integration-discovery.md`\n- `docs/coaches-discovery-execution-plan.md`\n- `docs/coaches-icp-segmentation.md`\n- `docs/coaches-wedges-matrix.md`\n- `docs/coaches-interview-script.md`\n- `docs/coaches-jtbd-workflow-map.md`\n- `docs/coaches-solution-thesis.md`\n- `docs/atc-access-request-checklist.md`\n- `docs/coaches-design-partner-outreach.md`\n- `docs/atc-integration-spec-skeleton.md`\n- `docs/coaches-coordination-gap-analysis.md`\n- `docs/classes-subroute-plan.md`\n- `docs/classes-mvp-rfc.md`\n- `docs/coaches-opportunity-brief.md`\n- `docs/coaches-research-repository.md`\n- `docs/coaches-validation-checklist-2-weeks.md`\n- `docs/atc-mock-integration-plan.md`\n- `docs/demo-iteration-mode.md`\n- `docs/implemented-host-app.md`\n- `docs/implemented-network.md`\n- `docs/implemented-tvs.md`\n- `docs/implemented-payments.md`\n- `docs/implemented-players.md`\n- `docs/implemented-sports.md`\n- `docs/ballbox-db-plan.md`\n- `docs/domain-model-target.md`\n- `docs/streetcast-parity.md`\n- `docs/screen-runtime-delivery-investigation.md`\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045115168
        }
      },
      {
        "type": "message",
        "id": "626fd53d",
        "parentId": "7cd8aebd",
        "timestamp": "2026-05-06T05:25:19.420Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "",
              "thinkingSignature": "{\"id\":\"rs_0cedd82c95a270c80169fad0bc19c881958917490137c0a943\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-tC_mBTW3n6U_E7xc5u4DyYX54giuI96syBNU0aiWViUBupBi2Vwf0IDcDP1Zt8wyYSOyOcOiDRbt4hTqm9b5efI61CY0Opl5qlHvCGtkhwn1Z2aTDrJYXJf1Ij5gd5vTdVRJR8hjdd8uRwLtd1rykrgMXlbAYCfQjBg2wkasrXMTuhWammY5M1o5haRSwN6gF_lRtQQD0ar4PwuU8CC0TZE9RzFLTu8AWJKIJLrf3DWopW7wY8uPqMXMkt2dVEjV486z8DyarG-O-WIUzvbHrrPbRo8ai75iZODCzlZncdXO13Pgd-pVAm3R8Q7Pm1p8ieY52mM_CTCJDFLs1SUOcnpE5WRZxSHGn9U2Mbsr2t8W5FP5LmYDaX54WAmlMEwnlQyvIOlzLzS_qD7fcuw_Y-TauxOOfJL89lRePxtXOZhkI--e-jGbbfvNfS3hPINeh-oSWrlo0xFwO2Hm-8UDpgB7WBO_akrRw8PPAYp5DbengjQWPNY8NfWc4khIpARlcWKOxHKGeFDSYEQGoWYqdf4XxuZxhkNkHxSacRQQqD2CJM24LtTI-uDXa9lJNuWiEVgA7Zfl8dKzM7A_k64hLwN2GRR8e_58X8In2-kk-JaTO2kAZzfLLT6sCvQujk7hvO4ajChcvbZE-O_2FxF0hFgpepmD9HHlTIka9yWVGBrcR05IfEraWTmutd9VToTdtOtZxi8OEY1ZYYBpyIVnqVVJajKDh8sH3Z5AzbcV1M8aQS2Vqye1291I8ZqzWtx97tWFq3ud3iTh4rj8ltfqjSVDXoba1nrtw8IibQSyep1stKGwIPZJj1bjUnLHfpH5ncFITrwYIMkFvVHGNUeWBkz829aoMBqvTK2nDjOeq7dgSJXiFoG1wltijrWbBm07NtvqxbXHbXaZKTrUaSX4yL41IscpZI0nDZbQzLRUzWhR-EGBE8-ncnx6YKxx5Vfci88AY8L57DFAT4PqwlFkzPr9cBkc7SiN4H4j4uAIDu7vrApEd8IwtmquLOnHw-uR5QzpTyGMxTqv-Kh6L6goMfs4RxlFdIz2lZGR9sjlJ7gemL3FVCOOoA7jLHOB-H8RA4hDe2ajhNpl8bwY-pllZIjIA==\",\"summary\":[]}"
            },
            {
              "type": "toolCall",
              "id": "call_DaNdEcaPBQIYGLL0M6uK5poH|fc_0cedd82c95a270c80169fad0bf2b908195905e75c70974f6ae",
              "name": "bash",
              "arguments": {
                "command": "cd /home/sebas/ballbox && find app -path '*coaches*' -o -path '*profes*' | sed 's#^#/#' | sort | head -300",
                "timeout": 10
              }
            },
            {
              "type": "toolCall",
              "id": "call_EeA3Oe9pwXkitP2Vzgw97glK|fc_0cedd82c95a270c80169fad0bf2ba08195bfa6e17b861a7c02",
              "name": "bash",
              "arguments": {
                "command": "cd /home/sebas/ballbox && rg -n \"profesor|profesores|coach|coaches\" app components lib tests prisma -S | head -300",
                "timeout": 10
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 4522,
            "output": 166,
            "cacheRead": 7168,
            "cacheWrite": 0,
            "totalTokens": 11856,
            "cost": {
              "input": 0.011305,
              "output": 0.00249,
              "cacheRead": 0.001792,
              "cacheWrite": 0,
              "total": 0.015587000000000002
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1778045115168,
          "responseId": "resp_0cedd82c95a270c80169fad0bb6d2c8195a84698f7165edb45"
        }
      },
      {
        "type": "message",
        "id": "94422b38",
        "parentId": "626fd53d",
        "timestamp": "2026-05-06T05:25:19.444Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_DaNdEcaPBQIYGLL0M6uK5poH|fc_0cedd82c95a270c80169fad0bf2b908195905e75c70974f6ae",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "/app/api/admin/sports/coaches\n/app/api/admin/sports/coaches/[coachId]\n/app/api/admin/sports/coaches/[coachId]/route.ts\n/app/api/admin/sports/coaches/route.ts\n/app/api/clubs/coaches\n/app/api/clubs/coaches/[coachId]\n/app/api/clubs/coaches/[coachId]/route.ts\n/app/api/clubs/coaches/route.ts\n/app/api/coaches\n/app/api/coaches/accounts\n/app/api/coaches/accounts/route.ts\n/app/api/coaches/availability\n/app/api/coaches/availability/route.ts\n/app/api/coaches/clubs\n/app/api/coaches/clubs/route.ts\n/app/api/coaches/coach-route-helpers.ts\n/app/api/coaches/invites\n/app/api/coaches/invites/[token]\n/app/api/coaches/invites/[token]/route.ts\n/app/api/coaches/logout\n/app/api/coaches/logout/route.ts\n/app/api/coaches/me\n/app/api/coaches/me/route.ts\n/app/api/coaches/session\n/app/api/coaches/session/route.ts\n/app/api/coaches/students\n/app/api/coaches/students/[bookingId]\n/app/api/coaches/students/[bookingId]/route.ts\n/app/api/coaches/students/players\n/app/api/coaches/students/players/[playerId]\n/app/api/coaches/students/players/[playerId]/route.ts\n/app/api/coaches/students/players/route.ts\n/app/api/coaches/students/route.ts\n/app/clubs/coaches\n/app/clubs/coaches/page.tsx\n/app/coaches\n/app/coaches/page.tsx\n/app/coaches/settings\n/app/coaches/settings/page.tsx\n"
            }
          ],
          "isError": false,
          "timestamp": 1778045119442
        }
      },
      {
        "type": "message",
        "id": "103dfb34",
        "parentId": "94422b38",
        "timestamp": "2026-05-06T05:25:19.444Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_EeA3Oe9pwXkitP2Vzgw97glK|fc_0cedd82c95a270c80169fad0bf2ba08195bfa6e17b861a7c02",
          "toolName": "bash",
          "content": [
            {
              "type": "text",

[Showing lines 1-538 of 20640 (50.0KB limit). Use offset=539 to continue.]


Warning: No models match pattern "gemini-3-flash-preview:minimal"
Warning: No models match pattern "gemini-3-flash-preview:medium"
Warning: No models match pattern "claude-3-7-sonnet:low"
Warning: No models match pattern "claude-3-7-sonnet:medium"
Warning: No models match pattern "claude-3-7-sonnet:high"
Warning: No models match pattern "o3-mini:low"
Warning: No models match pattern "o3-mini:medium"
Warning: No models match pattern "o3-mini:high"
Warning: No models match pattern "minimax-m2.5-free:minimal"
Warning: No models match pattern "qwen/qwen3-coder:free:minimal"
Warning: No models match pattern "google/gemma-3-27b-it:free:minimal"
[openai-codex] Token refresh failed: 401 {
  "error": {
    "message": "Your refresh token has already been used to generate a new access token. Please try signing in again.",
    "type": "invalid_request_error",
    "param": null,
    "code": "refresh_token_reused"
  }
}
No API key for provider: openai-codex


**Evaluating architecture changes**

I'm considering the simplest architecture, where the extension can queue jobs immediately, although it's optional. I think a new timer service could run every 6 hours to scan sessions modified since a specific watermark and queue any that haven't been analyzed yet. It might be a good idea to ensure the analyzer doesn't depend solely on the PI by default, using a better fallback heuristic to avoid silent failures. I'll need to check if systemd timers are okay.

bash

python3 - <<'PY'
import os
for k in ['OPENAI_API_KEY','ANTHROPIC_API_KEY','GOOGLE_API_KEY','OPENROUTER_API_KEY']:
    print(k, 'set' if os.environ.get(k) else 'unset')
PY


OPENAI_API_KEY unset
ANTHROPIC_API_KEY unset
GOOGLE_API_KEY unset
OPENROUTER_API_KEY unset


**Implementing session sweeper**

I need to create a session sweeper script that discovers session files in `pi-config/sessions`. It should maintain a state JSON with processed file paths and modification times. For each recent unprocessed session with user and assistant messages, I’ll create a job payload by reading the session summary. I'm considering calling the analyzer directly without writing job JSON, possibly using a `--payload-file`. I might implement a timer that runs every six hours, so it will be a one-shot service. Additionally, I’ll look at updating the extension.

**Reducing dependencies in analysis**

I'm looking to reduce dependencies. The prompt can still queue, but I want the analyzer fallback to work similarly. If `call_pi` fails, I’ll implement a `fallback_analysis(job)` that generates 0-3 candidates based on deterministic heuristics from the session branch. Instead of detecting work rule patterns from final answers, I could create an `episode` memory inbox summarizing the session if the assistant's final text isn't empty and there are more than N messages. This summary would include a title derived from the current working directory and some user context, which could be helpful for handoff.

**Considering timer implementation**

I’m looking to add a timer, and it seems like I need to implement a session memory sweeper. I think the relevant service files will be in the `.config` directory, specifically under `pi-config/scripts`. It’s a bit tricky since naming conventions matter, and I’ll want to keep things simple. I might need to create new files under `.config/systemd/user` and ensure everything is tracked properly. There’s no need for user input since it’s a machine configuration change. Let's get this implemented!

write

#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import subprocess
import sys
import time
from pathlib import Path
from typing import Any

BASE_DIR = Path(__file__).resolve().parent.parent
SESSIONS_DIR = Path(os.environ.get("PI_SESSION_DIR", BASE_DIR / "sessions"))
JOB_DIR = Path(os.environ.get("PI_SESSION_MEMORY_JOB_DIR", SESSIONS_DIR / "session-memory-jobs"))
STATE_FILE = Path(os.environ.get("PI_SESSION_MEMORY_SWEEP_STATE", BASE_DIR / "runtime" / "session-memory-sweeper-state.json"))
ANALYZER = Path(os.environ.get("PI_SESSION_MEMORY_ANALYZER", BASE_DIR / "scripts" / "session-memory-analyzer.py"))
LOOKBACK_DAYS = int(os.environ.get("PI_SESSION_MEMORY_LOOKBACK_DAYS", "21"))
MAX_SESSIONS_PER_RUN = int(os.environ.get("PI_SESSION_MEMORY_MAX_SESSIONS_PER_RUN", "12"))
MIN_TOTAL_MESSAGES = int(os.environ.get("PI_SESSION_MEMORY_MIN_TOTAL_MESSAGES", "6"))
MIN_ASSISTANT_TEXT = int(os.environ.get("PI_SESSION_MEMORY_MIN_ASSISTANT_TEXT", "120"))

SKIP_PREFIXES = (
    "you are iteration ",
    "continue the bounded ralph-loop",
)

def load_state() -> dict[str, Any]:
    if not STATE_FILE.exists():
        return {"processed": {}, "last_run_at": None}
    try:
        return json.loads(STATE_FILE.read_text(encoding="utf-8"))
    except Exception:
        return {"processed": {}, "last_run_at": None}

def save_state(state: dict[str, Any]) -> None:
    STATE_FILE.parent.mkdir(parents=True, exist_ok=True)
    STATE_FILE.write_text(json.dumps(state, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

def iter_session_files() -> list[Path]:
    cutoff = time.time() - LOOKBACK_DAYS * 86400
    paths = [p for p in SESSIONS_DIR.rglob("*.jsonl") if p.is_file() and p.stat().st_mtime >= cutoff]
    return sorted(paths, key=lambda p: p.stat().st_mtime)

def parse_session(path: Path) -> dict[str, Any]:
    session_id = None
    cwd = None
    branch: list[dict[str, Any]] = []
    user_messages = 0
    assistant_messages = 0
    last_user_text = ""
    last_assistant_text = ""
    first_user_text = ""

    with path.open("r", encoding="utf-8", errors="ignore") as handle:
        for line in handle:
            try:
                entry = json.loads(line)
            except Exception:
                continue
            if entry.get("type") == "session":
                session_id = entry.get("id") or session_id
                cwd = entry.get("cwd") or cwd
                continue
            if entry.get("type") != "message":
                continue
            message = entry.get("message") or {}
            role = message.get("role")
            if role not in {"user", "assistant"}:
                continue
            text_parts = []
            for part in message.get("content") or []:
                if part.get("type") == "text" and isinstance(part.get("text"), str):
                    text_parts.append(part["text"])
            text = "\n".join(text_parts).strip()
            branch.append(entry)
            if role == "user":
                user_messages += 1
                if text and not first_user_text:
                    first_user_text = text
                if text:
                    last_user_text = text
            elif role == "assistant":
                assistant_messages += 1
                if text:
                    last_assistant_text = text

    return {
        "session_id": session_id,
        "session_file": str(path),
        "cwd": cwd or str(Path.home()),
        "scope": infer_scope(cwd or str(Path.home())),
        "reason": "periodic_sweep",
        "trigger": "periodic_sweeper",
        "branch": branch,
        "user_messages": user_messages,
        "assistant_messages": assistant_messages,
        "last_user_text": truncate(last_user_text, 2000),
        "last_assistant_text": truncate(last_assistant_text, 2000),
        "first_user_text": truncate(first_user_text, 400),
    }

def infer_scope(cwd: str) -> str:
    home = str(Path.home())
    try:
        resolved = str(Path(cwd).resolve())
    except Exception:
        resolved = cwd
    if resolved == home or resolved.startswith(str(Path(home) / ".pi")):
        return "global"
    return "project"

def truncate(text: str, max_len: int) -> str:
    text = " ".join(text.split())
    if len(text) <= max_len:
        return text
    return text[: max_len - 1] + "…"

def should_skip(meta: dict[str, Any]) -> tuple[bool, str | None]:
    if meta["user_messages"] <= 0 or meta["assistant_messages"] <= 0:
        return True, "no_dialogue"
    first = (meta.get("first_user_text") or "").strip().lower()
    if first.startswith(SKIP_PREFIXES):
        return True, "loop_session"
    total_messages = meta["user_messages"] + meta["assistant_messages"]
    if total_messages < MIN_TOTAL_MESSAGES and len(meta.get("last_assistant_text") or "") < MIN_ASSISTANT_TEXT:
        return True, "too_small"
    return False, None

def existing_job_for_session(session_file: str) -> bool:
    for path in JOB_DIR.glob("*.json"):
        try:
            payload = json.loads(path.read_text(encoding="utf-8"))
        except Exception:
            continue
        if payload.get("payload", {}).get("session_file") == session_file:
            return True
    return False

def write_job(payload: dict[str, Any]) -> Path:
    JOB_DIR.mkdir(parents=True, exist_ok=True)
    job_id = time.strftime("%Y%m%dT%H%M%S", time.gmtime()) + f"{int(time.time_ns()%1_000_000):06d}Z"
    job_file = JOB_DIR / f"{job_id}.json"
    job = {
        "job_id": job_id,
        "queued_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
        "status": "queued",
        "job_type": "session_memory_analysis",
        "payload": payload,
        "analysis": {
            "status": "queued",
            "save": None,
            "memory_id": None,
        },
    }
    job_file.write_text(json.dumps(job, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
    return job_file

def run_analyzer(job_file: Path) -> int:
    proc = subprocess.run([sys.executable, str(ANALYZER), "--job-file", str(job_file)], capture_output=True, text=True)
    log_file = job_file.with_suffix(".log")
    log_file.write_text((proc.stdout or "") + (proc.stderr or ""), encoding="utf-8")
    return proc.returncode

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--once", action="store_true")
    args = parser.parse_args()
    state = load_state()
    processed = state.setdefault("processed", {})
    results = []

    for session_file in iter_session_files():
        if len(results) >= MAX_SESSIONS_PER_RUN:
            break
        stat = session_file.stat()
        key = str(session_file)
        marker = f"{stat.st_mtime_ns}:{stat.st_size}"
        if processed.get(key) == marker:
            continue
        meta = parse_session(session_file)
        skip, reason = should_skip(meta)
        if existing_job_for_session(key):
            processed[key] = marker
            results.append({"session_file": key, "status": "already_queued"})
            continue
        if skip:
            processed[key] = marker
            results.append({"session_file": key, "status": "skipped", "reason": reason})
            continue
        job_file = write_job(meta)
        exit_code = run_analyzer(job_file)
        processed[key] = marker
        results.append({"session_file": key, "status": "analyzed" if exit_code == 0 else "failed", "job_file": str(job_file)})

    state["last_run_at"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
    save_state(state)
    print(json.dumps({"processed_now": len(results), "results": results}, ensure_ascii=False))
    return 0

if __name__ == "__main__":
    raise SystemExit(main())


Successfully wrote 7750 bytes to /home/sebas/pi-config/scripts/session-memory-sweeper.py


**Considering execution options**

I might not need to make the executable necessary. It could be sensible for me to edit the analyzer as a fallback instead. This could streamline my approach while ensuring that I'm still effective. I want to be cautious and think this through, as I always want to find the best solutions. Are there any other alternatives I should be considering in this situation? Let’s see what options I have!

edit

from pathlib import Path
from typing import Any
from urllib import request

FALLBACK_TITLE_MAX = 72

def call_pi(prompt: str) -> str:
    cmd = [
        resolve_pi_bin(),
        "-p",
        "--provider",
        "openai-codex",
        "--model",
        PI_MODEL,
        "--thinking",
        PI_THINKING,
        "--no-tools",
        "--no-session",
        prompt,
    ]
    proc = subprocess.run(cmd, capture_output=True, text=True)
    if proc.returncode != 0:
        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
    return proc.stdout.strip()
+
+
+def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
+    meta = session_meta(job)
+    last_user = (meta.get("last_user_text") or "").strip()
+    last_assistant = (meta.get("last_assistant_text") or "").strip()
+    first_user = (meta.get("first_user_text") or last_user or "").strip()
+    if not first_user or not last_assistant:
+        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
+    title_base = first_user.splitlines()[0].strip()
+    if len(title_base) > FALLBACK_TITLE_MAX:
+        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
+    cwd = meta.get("cwd") or ""
+    cwd_name = Path(cwd).name or "session"
+    return {
+        "memories": [
+            {
+                "type": "episode",
+                "subtype": "session_summary",
+                "scope": meta.get("scope") or "project",
+                "title": f"{cwd_name}: {title_base}",
+                "content": f"User asked: {first_user}\nResult: {last_assistant}",
+                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
+                "evidence_ref": meta.get("session_file"),
+                "confidence": 0.72,
+                "freshness": 0.95,
+                "importance": 0.56,
+                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
+            }
+        ],
+        "reason": f"fallback-analysis:{error}",
+    }

        fallback_error = None
        try:
            raw = call_pi(build_prompt(job))
            analysis = extract_json(raw)
        except Exception as exc:
            fallback_error = exc
            analysis = fallback_analysis(job, exc)
            raw = json.dumps(analysis, ensure_ascii=False)

        job["analysis"]["raw"] = raw
        job["analysis"]["result"] = analysis
        job["analysis"]["status"] = "completed"
        if fallback_error is not None:
            job["analysis"]["fallback_error"] = str(fallback_error)


Successfully replaced 4 block(s) in /home/sebas/pi-config/scripts/session-memory-analyzer.py.

     ...
  10 from pathlib import Path
  11 from typing import Any
  12 from urllib import request
  13
+ 14 FALLBACK_TITLE_MAX = 72
+ 15
  14 AGENTS_DB_API = os.environ.get("AGENTS_DB_API", "http://100.116.176.16:8091")
  15 PI_MODEL = os.environ.get("PI_SESSION_MEMORY_MODEL", "gpt-5.4-mini")
  16 PI_THINKING = os.environ.get("PI_SESSION_MEMORY_THINKING", "off")
  17 PI_BIN = os.environ.get("PI_SESSION_MEMORY_PI_BIN")
     ...
  62     proc = subprocess.run(cmd, capture_output=True, text=True)
  63     if proc.returncode != 0:
  64         raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
  65     return proc.stdout.strip()
+ 68 +
+ 69 +
+ 70 +def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
+ 71 +    meta = session_meta(job)
+ 72 +    last_user = (meta.get("last_user_text") or "").strip()
+ 73 +    last_assistant = (meta.get("last_assistant_text") or "").strip()
+ 74 +    first_user = (meta.get("first_user_text") or last_user or "").strip()
+ 75 +    if not first_user or not last_assistant:
+ 76 +        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
+ 77 +    title_base = first_user.splitlines()[0].strip()
+ 78 +    if len(title_base) > FALLBACK_TITLE_MAX:
+ 79 +        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
+ 80 +    cwd = meta.get("cwd") or ""
+ 81 +    cwd_name = Path(cwd).name or "session"
+ 82 +    return {
+ 83 +        "memories": [
+ 84 +            {
+ 85 +                "type": "episode",
+ 86 +                "subtype": "session_summary",
+ 87 +                "scope": meta.get("scope") or "project",
+ 88 +                "title": f"{cwd_name}: {title_base}",
+ 89 +                "content": f"User asked: {first_user}\nResult: {last_assistant}",
+ 90 +                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
+ 91 +                "evidence_ref": meta.get("session_file"),
+ 92 +                "confidence": 0.72,
+ 93 +                "freshness": 0.95,
+ 94 +                "importance": 0.56,
+ 95 +                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
+ 96 +            }
+ 97 +        ],
+ 98 +        "reason": f"fallback-analysis:{error}",
+ 99 +    }
  66
  67
  68 def ingest(memory: dict[str, Any]) -> dict[str, Any]:
  69     req = request.Request(
     ...
 226     }
 227     save_job(job_file, job)
 228
 229     try:
-230         raw = call_pi(build_prompt(job))
-231         analysis = extract_json(raw)
+264         fallback_error = None
+265         try:
+266             raw = call_pi(build_prompt(job))
+267             analysis = extract_json(raw)
+268         except Exception as exc:
+269             fallback_error = exc
+270             analysis = fallback_analysis(job, exc)
+271             raw = json.dumps(analysis, ensure_ascii=False)
 232         candidates = normalize_memories(analysis)[:MAX_CANDIDATES]
 233         saved_items: list[dict[str, Any]] = []
 234
 235         for index, candidate in enumerate(candidates, start=1):
     ...
 240
 241         job["analysis"]["raw"] = raw
 242         job["analysis"]["result"] = analysis
 243         job["analysis"]["status"] = "completed"
+284         if fallback_error is not None:
+285             job["analysis"]["fallback_error"] = str(fallback_error)
 244         job["analysis"]["save"] = bool(saved_items)
 245         job["analysis"]["saved"] = bool(saved_items)
 246         job["analysis"]["saved_count"] = len(saved_items)
 247         job["analysis"]["memory_ids"] = [item.get("id") for item in saved_items if item.get("id")]
     ...


write

[Unit]
Description=Pi session memory sweeper
After=network-online.target
Wants=network-online.target

[Service]
Type=oneshot
WorkingDirectory=/home/sebas/pi-config
Environment=AGENTS_DB_API=http://100.116.176.16:8091
Environment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi
Environment=PI_SESSION_MEMORY_LOOKBACK_DAYS=21
Environment=PI_SESSION_MEMORY_MAX_SESSIONS_PER_RUN=12
ExecStart=/usr/bin/env python3 /home/sebas/pi-config/scripts/session-memory-sweeper.py --once


Successfully wrote 480 bytes to /home/sebas/pi-config/scripts/session-memory-sweeper.service


[Unit]
Description=Run Pi session memory sweeper periodically

[Timer]
OnBootSec=10m
OnUnitActiveSec=6h
Persistent=true
Unit=session-memory-sweeper.service

[Install]
WantedBy=timers.target


Successfully wrote 190 bytes to /home/sebas/pi-config/scripts/session-memory-sweeper.timer


**Installing systemd timer**

I need to install something into the `/.config/systemd/user` directory. I'll use a bash command to copy the necessary files and then run a daemon reload to enable and start the timer. It could be a good idea to check the permissions as well to ensure everything is set up correctly. I might even need to update the README for clarity afterward. Let's make sure everything goes smoothly!

edit

- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
- The canonical memory pipeline lives in `agents-database` (`MemoryService` / `MemoryEngine`). Repo-local helpers should only capture session context and hand off to that service.
- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
- The dispatcher/analyzer are restart-safe because queued jobs live on disk under `sessions/session-memory-jobs/`.
- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.

- Session close hook can still queue memory analysis before switch/fork/exit.
- Session memory also has a periodic sweeper so Telegram-heavy periods do not depend on session-end hooks.
- The external local dispatcher listens on `127.0.0.1:17882`; it captures session identity/context and enqueues metadata under `sessions/session-memory-jobs/`.
- `scripts/session-memory-sweeper.py` is the reliable catch-up path: it scans recent session files, skips obvious loop noise, and analyzes anything not yet processed.
- The analyzer first tries cheap `pi` model analysis; if that is unavailable, it falls back to a deterministic episode-summary candidate instead of failing silently.
- The canonical memory pipeline lives in `agents-database` (`MemoryService` / `MemoryEngine`). Repo-local helpers should only capture session context and hand off to that service.
- Use `./scripts/install-session-memory-dispatcher.service` for the dispatcher and `./scripts/session-memory-sweeper.{service,timer}` for periodic catch-up.


Could not find the exact text in /home/sebas/pi-config/README.md. The old text must match exactly including all whitespace and newlines.


bash

rg -n "Session close hook|Session memory now|dispatcher" /home/sebas/pi-config/README.md -n -C 2


12-- `scripts/apply-pi-shutdown-hook-patch.py`
13-- `scripts/session-memory-analyzer.py`
14:- `scripts/session-memory-dispatcher.py`
15:- `scripts/install-session-memory-dispatcher.service`
16-- `bin/pi-loop` repeated multi-session task runner
17-- `docs/pi-loop.md` usage guide for repeated multi-session loops
--
44-
45-- Direct, compressed, low-noise communication is the default style.
46:- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
47:- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
48-- The canonical memory pipeline lives in `agents-database` (`MemoryService` / `MemoryEngine`). Repo-local helpers should only capture session context and hand off to that service.
49-- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
50-- For unattended `pi` scripts and jobs, prefer explicit provider/model selection. Current safe default on this machine: `--provider openai-codex --model gpt-5.4-mini`.
51:- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.
52-- The old `session_before_shutdown` runtime patch remains as fallback only if you choose to keep it enabled.
53-- Reapply the runtime patch after Pi upgrades only if you still use that fallback: `./scripts/apply-pi-shutdown-hook-patch.py`


edit

- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
- Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
- The canonical memory pipeline lives in `agents-database` (`MemoryService` / `MemoryEngine`). Repo-local helpers should only capture session context and hand off to that service.
- The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
- For unattended `pi` scripts and jobs, prefer explicit provider/model selection. Current safe default on this machine: `--provider openai-codex --model gpt-5.4-mini`.
- Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.

- Session close hook can still queue memory analysis before switch/fork/exit.
- Session memory uses an external local dispatcher on `127.0.0.1:17882`; the hook captures session identity/context and enqueues metadata under `sessions/session-memory-jobs/`.
- `scripts/session-memory-sweeper.py` is the reliable catch-up path for Telegram-heavy periods: it scans recent session files, skips obvious loop noise, and analyzes anything not yet processed.
- The analyzer first tries cheap `pi` model analysis (`gpt-5.4-mini`, thinking off); if that path is unavailable, it falls back to a deterministic episode-summary candidate instead of failing silently.
- The canonical memory pipeline lives in `agents-database` (`MemoryService` / `MemoryEngine`). Repo-local helpers should only capture session context and hand off to that service.
- For unattended `pi` scripts and jobs, prefer explicit provider/model selection. Current safe default on this machine: `--provider openai-codex --model gpt-5.4-mini`.
- Use `./scripts/install-session-memory-dispatcher.service` for the dispatcher and `./scripts/session-memory-sweeper.{service,timer}` for periodic catch-up.


Successfully replaced 1 block(s) in /home/sebas/pi-config/README.md.

     ...
  42
  43 ## Notes
  44
  45 - Direct, compressed, low-noise communication is the default style.
- 46 - Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
- 47 - Session memory now uses an external local dispatcher on `127.0.0.1:17882`; the hook only captures session identity/context, the dispatcher enqueues metadata, and a background analyzer decides whether anything durable is worth saving.
+ 46 - Session close hook can still queue memory analysis before switch/fork/exit.
+ 47 - Session memory uses an external local dispatcher on `127.0.0.1:17882`; the hook captures session identity/context and enqueues metadata under `sessions/session-memory-jobs/`.
+ 48 - `scripts/session-memory-sweeper.py` is the reliable catch-up path for Telegram-heavy periods: it scans recent session files, skips obvious loop noise, and analyzes anything not yet processed.
+ 49 - The analyzer first tries cheap `pi` model analysis (`gpt-5.4-mini`, thinking off); if that path is unavailable, it falls back to a deterministic episode-summary candidate instead of failing silently.
  48 - The canonical memory pipeline lives in `agents-database` (`MemoryService` / `MemoryEngine`). Repo-local helpers should only capture session context and hand off to that service.
- 49 - The analyzer uses `pi` in background with a cheap model (`gpt-5.4-mini`) and thinking off when possible.
  50 - For unattended `pi` scripts and jobs, prefer explicit provider/model selection. Current safe default on this machine: `--provider openai-codex --model gpt-5.4-mini`.
- 51 - Use `./scripts/install-session-memory-dispatcher.service` to keep the dispatcher alive under systemd user service if desired.
+ 52 - Use `./scripts/install-session-memory-dispatcher.service` for the dispatcher and `./scripts/session-memory-sweeper.{service,timer}` for periodic catch-up.
  52 - The old `session_before_shutdown` runtime patch remains as fallback only if you choose to keep it enabled.
  53 - Reapply the runtime patch after Pi upgrades only if you still use that fallback: `./scripts/apply-pi-shutdown-hook-patch.py`
  54 - **Pending validation:** confirm the default communication mode auto-loads in every new Pi session.
  55 - Top-level `/tmp` files older than 3 days are cleaned every 3 days via `bin/tmp-cleanup`.
     ...


**Considering documentation update**

It seems I need to update some documentation, just to make sure everything's clear. I might also need to install certain files and run some tests to confirm everything works well. First, I’ll check the syntax. Then, I’ll copy the service and timer configurations to the .config directory. After that, I should perform a daemon-reload and enable the timer before starting the service. It's a process, but I'm on it!

bash

python3 -m py_compile /home/sebas/pi-config/scripts/session-memory-analyzer.py /home/sebas/pi-config/scripts/session-memory-sweeper.py

cp /home/sebas/pi-config/scripts/session-memory-sweeper.service /home/sebas/.config/systemd/user/session-memory-sweeper.service && cp /home/sebas/pi-config/scripts/session-memory-sweeper.timer /home/sebas/.config/systemd/user/session-memory-sweeper.timer && systemctl --user daemon-reload && systemctl --user enable --now session-memory-sweeper.timer && systemctl --user start session-memory-sweeper.service && systemctl --user status session-memory-sweeper.timer session-memory-sweeper.service --no-pager


  File "/home/sebas/pi-config/scripts/session-memory-analyzer.py", line 68
    +
     ^
SyntaxError: invalid syntax

Command exited with code 1


Created symlink '/home/sebas/.config/systemd/user/timers.target.wants/session-memory-sweeper.timer' → '/home/sebas/.config/systemd/user/session-memory-sweeper.timer'.
● session-memory-sweeper.timer - Run Pi session memory sweeper periodically
     Loaded: loaded (/home/sebas/.config/systemd/user/session-memory-sweeper.timer; enabled; preset: enabled)
     Active: active (waiting) since Sun 2026-05-24 06:14:10 -03; 3s ago
 Invocation: 6c24261dbf5f45c6a1b23dc5a0193bd3
    Trigger: Sun 2026-05-24 12:14:10 -03; 5h 59min left
   Triggers: ● session-memory-sweeper.service

May 24 06:14:10 ballbox-first systemd[1289]: Started session-memory-sweeper.timer - Run Pi session memory sweeper periodically.

○ session-memory-sweeper.service - Pi session memory sweeper
     Loaded: loaded (/home/sebas/.config/systemd/user/session-memory-sweeper.service; static)
     Active: inactive (dead) since Sun 2026-05-24 06:14:14 -03; 18ms ago
 Invocation: 128677232ff24170b5da8934e281b5ce
TriggeredBy: ● session-memory-sweeper.timer
    Process: 4128901 ExecStart=/usr/bin/env python3 /home/sebas/pi-config/scripts/session-memory-sweeper.py --once (code=exited, status=0/SUCCESS)
   Main PID: 4128901 (code=exited, status=0/SUCCESS)
        CPU: 3.587s

May 24 06:14:10 ballbox-first systemd[1289]: Starting session-memory-sweeper.service - Pi session memory sweeper...
May 24 06:14:14 ballbox-first env[4128901]: {"processed_now": 12, "results": [{"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-04T21-17-14-403Z_019df4da-3522-776c-986b-78e057101a55.jsonl", "status": "failed", "job_file": "/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091411438822Z.json"}, {"session_file": "/home/sebas/pi-config/sessions/--tmp-pi-tau-mux-test-project-a--/2026-04-29T16-49-04-845Z_019dda24-e74d-72aa-8f07-5b90064db8f0.jsonl", "status": "failed", "job_file": "/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091411109175Z.json"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T02-53-43-386Z_019df60e-4459-772c-a00f-05bb1f067639.jsonl", "status": "failed", "job_file": "/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091412068506Z.json"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-04T20-31-12-064Z_019df4b0-0ebf-777a-8efb-4a9a002cf97c.jsonl", "status": "failed", "job_file": "/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091412473113Z.json"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-38-23-930Z_019df85c-7c39-73e8-aa53-3b548239898e.jsonl", "status": "failed", "job_file": "/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091412893766Z.json"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl", "status": "already_queued"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T14-28-30-272Z_019df88a-5bbf-76ca-ac41-3144553f965a.jsonl", "status": "failed", "job_file": "/home/sebas/pi-config/sessions/session-memory-jobs/20260524T091413807808Z.json"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-01T20-20-59-838Z_019de533-a33e-7348-809e-ac73ede080fb.jsonl", "status": "already_queued"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T20-49-56-569Z_019df9e7-9358-723f-a864-a7a0e0a21f31.jsonl", "status": "skipped", "reason": "too_small"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T20-50-22-086Z_019df9e7-f705-738a-a034-b7207fc60e64.jsonl", "status": "skipped", "reason": "too_small"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T20-50-39-515Z_019df9e8-3b1a-754b-96e9-b19040fd4fc4.jsonl", "status": "skipped", "reason": "too_small"}, {"session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T20-50-40-843Z_019df9e8-404a-71e6-bfd7-15275a48cf48.jsonl", "status": "skipped", "reason": "too_small"}]}
May 24 06:14:14 ballbox-first systemd[1289]: Finished session-memory-sweeper.service - Pi session memory sweeper.
May 24 06:14:14 ballbox-first systemd[1289]: session-memory-sweeper.service: Consumed 3.587s CPU time.

Command exited with code 3


**Fixing syntax issues**

I need to address a syntax problem related to plus signs that are leftover in a file from an edit. I should read through the lines around 60 to 80 to identify where these issues are coming from. It's important to ensure that everything is cleaned up properly since those kinds of errors can create bigger problems down the line. Let's get to it and make sure everything is functioning as it should!


        "openai-codex",
        "--model",
        PI_MODEL,
        "--thinking",
        PI_THINKING,
        "--no-tools",
        "--no-session",
        prompt,
    ]
    proc = subprocess.run(cmd, capture_output=True, text=True)
    if proc.returncode != 0:
        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
    return proc.stdout.strip()
+
+
+def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
+    meta = session_meta(job)
+    last_user = (meta.get("last_user_text") or "").strip()
+    last_assistant = (meta.get("last_assistant_text") or "").strip()
+    first_user = (meta.get("first_user_text") or last_user or "").strip()
+    if not first_user or not last_assistant:
+        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
+    title_base = first_user.splitlines()[0].strip()
+    if len(title_base) > FALLBACK_TITLE_MAX:
+        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
+    cwd = meta.get("cwd") or ""
+    cwd_name = Path(cwd).name or "session"
+    return {
+        "memories": [
+            {
+                "type": "episode",
+                "subtype": "session_summary",
+                "scope": meta.get("scope") or "project",
+                "title": f"{cwd_name}: {title_base}",
+                "content": f"User asked: {first_user}\nResult: {last_assistant}",
+                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
+                "evidence_ref": meta.get("session_file"),
+                "confidence": 0.72,
+                "freshness": 0.95,
+                "importance": 0.56,
+                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
+            }
+        ],
+        "reason": f"fallback-analysis:{error}",
+    }

def ingest(memory: dict[str, Any]) -> dict[str, Any]:
    req = request.Request(
        f"{AGENTS_DB_API}/api/ingest",
        method="POST",
        data=json.dumps(memory).encode(),
        headers={"Content-Type": "application/json"},
    )
    with request.urlopen(req, timeout=30) as resp:
        return json.loads(resp.read().decode("utf-8"))

def extract_json(text: str) -> dict[str, Any]:
    raw = text.strip()
    if raw.startswith("```"):
        raw = raw.strip("`")
        raw = raw.split("\n", 1)[-1] if "\n" in raw else raw
    start = raw.find("{")
    end = raw.rfind("}")
    if start >= 0 and end > start:
        raw = raw[start : end + 1]
    return json.loads(raw)

[181 more lines in file. Use offset=125 to continue.]


edit

    if proc.returncode != 0:
        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
    return proc.stdout.strip()
+
+
+def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
+    meta = session_meta(job)
+    last_user = (meta.get("last_user_text") or "").strip()
+    last_assistant = (meta.get("last_assistant_text") or "").strip()
+    first_user = (meta.get("first_user_text") or last_user or "").strip()
+    if not first_user or not last_assistant:
+        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
+    title_base = first_user.splitlines()[0].strip()
+    if len(title_base) > FALLBACK_TITLE_MAX:
+        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
+    cwd = meta.get("cwd") or ""
+    cwd_name = Path(cwd).name or "session"
+    return {
+        "memories": [
+            {
+                "type": "episode",
+                "subtype": "session_summary",
+                "scope": meta.get("scope") or "project",
+                "title": f"{cwd_name}: {title_base}",
+                "content": f"User asked: {first_user}\nResult: {last_assistant}",
+                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
+                "evidence_ref": meta.get("session_file"),
+                "confidence": 0.72,
+                "freshness": 0.95,
+                "importance": 0.56,
+                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
+            }
+        ],
+        "reason": f"fallback-analysis:{error}",
+    }

    if proc.returncode != 0:
        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
    return proc.stdout.strip()

def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
    meta = session_meta(job)
    last_user = (meta.get("last_user_text") or "").strip()
    last_assistant = (meta.get("last_assistant_text") or "").strip()
    first_user = (meta.get("first_user_text") or last_user or "").strip()
    if not first_user or not last_assistant:
        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
    title_base = first_user.splitlines()[0].strip()
    if len(title_base) > FALLBACK_TITLE_MAX:
        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
    cwd = meta.get("cwd") or ""
    cwd_name = Path(cwd).name or "session"
    return {
        "memories": [
            {
                "type": "episode",
                "subtype": "session_summary",
                "scope": meta.get("scope") or "project",
                "title": f"{cwd_name}: {title_base}",
                "content": f"User asked: {first_user}\nResult: {last_assistant}",
                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
                "evidence_ref": meta.get("session_file"),
                "confidence": 0.72,
                "freshness": 0.95,
                "importance": 0.56,
                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
            }
        ],
        "reason": f"fallback-analysis:{error}",
    }


Successfully replaced 1 block(s) in /home/sebas/pi-config/scripts/session-memory-analyzer.py.

     ...
  64     proc = subprocess.run(cmd, capture_output=True, text=True)
  65     if proc.returncode != 0:
  66         raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
  67     return proc.stdout.strip()
- 68 +
- 69 +
- 70 +def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
- 71 +    meta = session_meta(job)
- 72 +    last_user = (meta.get("last_user_text") or "").strip()
- 73 +    last_assistant = (meta.get("last_assistant_text") or "").strip()
- 74 +    first_user = (meta.get("first_user_text") or last_user or "").strip()
- 75 +    if not first_user or not last_assistant:
- 76 +        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
- 77 +    title_base = first_user.splitlines()[0].strip()
- 78 +    if len(title_base) > FALLBACK_TITLE_MAX:
- 79 +        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
- 80 +    cwd = meta.get("cwd") or ""
- 81 +    cwd_name = Path(cwd).name or "session"
- 82 +    return {
- 83 +        "memories": [
- 84 +            {
- 85 +                "type": "episode",
- 86 +                "subtype": "session_summary",
- 87 +                "scope": meta.get("scope") or "project",
- 88 +                "title": f"{cwd_name}: {title_base}",
- 89 +                "content": f"User asked: {first_user}\nResult: {last_assistant}",
- 90 +                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
- 91 +                "evidence_ref": meta.get("session_file"),
- 92 +                "confidence": 0.72,
- 93 +                "freshness": 0.95,
- 94 +                "importance": 0.56,
- 95 +                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
- 96 +            }
- 97 +        ],
- 98 +        "reason": f"fallback-analysis:{error}",
- 99 +    }
 100
 101
+ 70 def fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
+ 71     meta = session_meta(job)
+ 72     last_user = (meta.get("last_user_text") or "").strip()
+ 73     last_assistant = (meta.get("last_assistant_text") or "").strip()
+ 74     first_user = (meta.get("first_user_text") or last_user or "").strip()
+ 75     if not first_user or not last_assistant:
+ 76         return {"memories": [], "reason": f"fallback-no-summary:{error}"}
+ 77     title_base = first_user.splitlines()[0].strip()
+ 78     if len(title_base) > FALLBACK_TITLE_MAX:
+ 79         title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
+ 80     cwd = meta.get("cwd") or ""
+ 81     cwd_name = Path(cwd).name or "session"
+ 82     return {
+ 83         "memories": [
+ 84             {
+ 85                 "type": "episode",
+ 86                 "subtype": "session_summary",
+ 87                 "scope": meta.get("scope") or "project",
+ 88                 "title": f"{cwd_name}: {title_base}",
+ 89                 "content": f"User asked: {first_user}\nResult: {last_assistant}",
+ 90                 "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
+ 91                 "evidence_ref": meta.get("session_file"),
+ 92                 "confidence": 0.72,
+ 93                 "freshness": 0.95,
+ 94                 "importance": 0.56,
+ 95                 "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
+ 96             }
+ 97         ],
+ 98         "reason": f"fallback-analysis:{error}",
+ 99     }
+100
+101
 102 def ingest(memory: dict[str, Any]) -> dict[str, Any]:
 103     req = request.Request(
 104         f"{AGENTS_DB_API}/api/ingest",
 105         method="POST",
     ...


**Inspecting markers and logs**

I need to use `py_compile` and then try running the sweeper again, since it seems like the markers might already be set for the failed sessions. It's possible that the analyzer writes out the failed jobs but still processes them. To test this, I could run the analyzer on one of the failed jobs first. Maybe I should inspect one of the new logs before proceeding with the compilation.

bash

python3 /home/sebas/pi-config/scripts/session-memory-analyzer.py --job-file /home/sebas/pi-config/sessions/session-memory-jobs/20260524T091411438822Z.json && echo OK || true && cat /home/sebas/pi-config/sessions/session-memory-jobs/20260524T091411438822Z.json


(no output)