**Exploring simulation options**

I need to keep my answer concise. It sounds like I can simulate by using a POST to the dispatcher with an existing session file, which might be easier than asking the user directly. But since the user is asking for a preference, it makes sense to mention that we can safely simulate using prior sessions or replays. However, for a true end-to-end hook, it would be better to create and close one. I might recommend both options to the user.

Sí. Puedo simularlo.

Opciones:

1. **Simulación rápida**
   - reinyecto un `session_file` viejo al dispatcher
   - valida analyzer + ingest + persistencia
   - no valida el hook real de cierre

2. **Prueba real end-to-end**
   - abrís una sesión nueva
   - la cerrás o hacés switch
   - valida hook + dispatcher + analyzer + ingest

Recomiendo:

- primero **simulación rápida**
- si pasa, después **una prueba real**

Así aislamos:
- si falla la sim: problema en analyzer/dispatcher
- si pasa la sim pero falla la real: problema en hook

Si querés, arranco ahora con la simulación.


dale


**Simulating task process**

I need to simulate something, and I'm thinking about using a to-do list. Should I follow up inside the active task or maybe create a new one? I suppose updating the to-do would be fine since the task is done already. I’ll probably need to use an old session file or something recent. Then, I’ll post to the dispatcher with the right payload and inspect the created job and memories afterward.

bash

python3 - <<'PY'
import json, urllib.request
payload = {
  'session_id': 'sim-test-20260505',
  'session_file': '/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl',
  'reason': 'manual-simulation',
  'cwd': '/home/sebas',
  'scope': 'global',
  'user_messages': 11,
  'assistant_messages': 56,
  'last_user_text': 'and yeah harden like you suggested',
  'last_assistant_text': 'simulation trigger',
  'trigger': 'manual-test',
}
req = urllib.request.Request('http://127.0.0.1:17882/api/session-memory', data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'})
with urllib.request.urlopen(req, timeout=10) as r:
    print(r.read().decode())
PY


python3 - <<'PY'
import json, time
from pathlib import Path
job=Path('/home/sebas/pi-config/sessions/session-memory-jobs/20260505T220309977099Z.json')
for i in range(30):
    data=json.loads(job.read_text())
    status=data.get('analysis',{}).get('status')
    if status in {'completed','failed'}:
        print(json.dumps(data, ensure_ascii=False, indent=2))
        break
    time.sleep(1)
else:
    print(json.dumps(data, ensure_ascii=False, indent=2))
PY


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


**Creating a concise report**

I'm aiming to prepare a report that's straightforward and to the point while making sure to highlight any blockers. It’s important to present the information clearly so the user understands what’s at stake and where any obstacles are. I wonder if I’ll need to include any details that might help clarify the situation. Okay, I’ll focus on maintaining clarity and brevity throughout the report. Let’s get to it!

Simulé. Falló.

Resultado:

- dispatcher: OK
- job file: creado
- analyzer: arrancó
- persistencia del job: OK
- fallo real: **auth del modelo**, no del hook

Error:

- `openai-codex` token refresh `401`
- `refresh_token_reused`
- `No API key for provider: openai-codex`

Conclusión:

- el pipeline nuevo quedó conectado
- **ahora mismo no podemos validar ingest real** hasta arreglar auth del provider usado por `pi`

O sea:
- no hace falta cerrar una sesión real todavía
- primero conviene arreglar este blocker
- si no, la prueba real va a fallar igual

Siguiente recomendado:

1. arreglar login/auth de `pi` / `openai-codex`
2. rerun simulación
3. si pasa, hacer cierre real end-to-end

Si querés, sigo directo con el arreglo de auth.