futurediffusion/claudebot

Última atividade de origem registrada: 19 de abril de 2026 às 20:16

Catálogo do SkillsMP atualizado: 17 de outubro de 2026 às 00:09

skills coletadas: 25

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README do repositório

Arquivo de origem do README: README.md

# Claudebot Workspace Workspace reorganized to keep the active system separate from older experiments. ## What To Use - **Referencia primaria de gobernanza**: [docs/PROJECT_CONSTITUTION.md](D:/IA/CODE/claudebot/docs/PROJECT_CONSTITUTION.md). - **Nombre operativo**: **Sistema local CLI-first para automatización personal en PC**. - **Prioridades**: operación local primero, CLI-first, routing pragmático. - [orchestrator/README.md](D:/IA/CODE/claudebot/orchestrator/README.md): núcleo activo de operación local CLI-first con routing pragmático. - [tools/](D:/IA/CODE/claudebot/tools): external tooling and support repos. - [.claude/](D:/IA/CODE/claudebot/.claude): local editor and agent config. - [run_browser.py](D:/IA/CODE/claudebot/run_browser.py): direct browser automation wrapper. - [run_windows.py](D:/IA/CODE/claudebot/run_windows.py): direct Windows automation wrapper. - [run_worker.py](D:/IA/CODE/claudebot/run_worker.py): full `worker-core` wrapper. - [run_agent.py](D:/IA/CODE/claudebot/run_agent.py): high-level recommended entrypoint with locked agent selection. - [skills_bridge.py](D:/IA/CODE/claudebot/skills_bridge.py): shared bridge to discover, read, and execute local skills from one CLI. - [self_model/README.md](D:/IA/CODE/claudebot/self_model/README.md): shared self-model used by Claude, Gemini, Codex, and wrappers. - [self_model_cli.py](D:/IA/CODE/claudebot/self_model_cli.py): inspect or simulate the shared self-model from the repo root. - [episodic_memory/README.md](D:/IA/CODE/claudebot/episodic_memory/README.md): shared episodic memory for concrete attempts, failures, and fixes. - [episodic_memory_cli.py](D:/IA/CODE/claudebot/episodic_memory_cli.py): inspect similar past episodes from the repo root. - [world_model/README.md](D:/IA/CODE/claudebot/world_model/README.md): shared desktop world model for live environment state. - [world_model_cli.py](D:/IA/CODE/claudebot/world_model_cli.py): inspect the current world-state slice from the repo root. - [orchestrator/docs/CAPABILITIES_MATRIX.md](D:/IA/CODE/claudebot/orchestrator/docs/CAPABILITIES_MATRIX.md): matriz de capacidades reales vs experimentales del sistema. - [orchestrator/docs/EXECUTION_HIERARCHY.md](D:/IA/CODE/claudebot/orchestrator/docs/EXECUTION_HIERARCHY.md): pirámide oficial de ejecución, escalado por niveles y fallback extremo mouse/visión. - [docs/SKILLS_CATALOG.md](D:/IA/CODE/claudebot/docs/SKILLS_CATALOG.md): catálogo de skills operativas (dominio, IO, costo, fallbacks y owner). - [docs/DEPRECATION_POLICY.md](D:/IA/CODE/claudebot/docs/DEPRECATION_POLICY.md): estados oficiales (`active`, `discouraged`, `deprecated`, `archived`) y checklist de migración. ## Qué NO es - No es AGI. - No es mouse-first. **Status: deprecated** - No es multimodelo por defecto. **Status: discouraged** ## Valor práctico hoy Este workspace aporta valor cuando te resuelve tareas diarias de forma directa y repetible: - **Limpieza**: ordenar carpetas de trabajo, mover archivos temporales y dejar resultados en rutas consistentes. - **Organización**: crear resúmenes, notas operativas y salidas en `tasks/output/` con estructura clara. - **Instalación**: ejecutar pasos de setup y validaciones básicas de herramientas desde flujos guiados por CLI. - **Reparación de entorno**: diagnosticar fallos típicos (dependencias, rutas, comandos) y aplicar correcciones verificables. - **Automatización web**: abrir sitios, extraer datos concretos y guardar resultados sin depender de interacción manual constante. > **Si esto te ahorra tiempo real, estás en el camino correcto.** ## Archived - [legacy/](D:/IA/CODE/claudebot/legacy): previous prototypes not used by the current flow. **Status: archived** ## Minimal Structure ```text claudebot/ |-- orchestrator/ active project |-- tools/ external utilities and helper repos |-- legacy/ archived prototypes |-- self_model/ shared routing self-model |-- episodic_memory/ shared execution episodes |-- world_model/ shared desktop world state `-- .claude/ local config ``` ## Natural Language Automation The main orchestrator now auto-routes plain language desktop and browser tasks. You do not need to remember prefixes like `browser:` or `windows:`. Examples: ```bash cd D:/IA/CODE/claudebot/orchestrator run_orchestrator.bat "Abre Chrome y ve a https://example.com" run_orchestrator.bat "Abre Notepad y escribe hola mundo" run_orchestrator.bat "Abre https://example.com y guarda un resumen en tasks/output/resumen.txt" ``` Behind the scenes: - browser tasks go to `worker-core:browser` - Windows desktop tasks go to `worker-core:windows` - multi-step automation flows go to `worker-core:orchestrator` The skills taxonomy used by these wrappers is documented in [docs/SKILLS_CATALOG.md](D:/IA/CODE/claudebot/docs/SKILLS_CATALOG.md). Recommended high-level entrypoint: ```bash python run_agent.py --agent claude_code --tool auto "Abre https://example.com y extrae el titulo" python run_agent.py --agent codex_cli --tool browser "Abre https://example.com y extrae el titulo" python run_agent.py --agent minimax_cli --tool worker "Abre https://example.com y guarda un resumen en tasks/output/resumen.txt" ``` You can still call the low-level wrappers directly if you want explicit control: ```bash python run_browser.py "Abre https://example.com y extrae el titulo" python run_windows.py "Abre Notepad y escribe hola mundo" python run_worker.py "Abre https://example.com y guarda un resumen en tasks/output/resumen.txt" ``` ## Shared Skill Bridge The workspace now includes a local bridge that can discover and reuse skills across: - `gemini_skills/` for compatible executable skills, - `skills/` and `skills/.codex/skills/` for `SKILL.md`-based skills, - `legacy/local-agent/SKILLS/`, - packaged skills under `tools/browser-use/skills/` and related tooling. Examples: ```bash python skills_bridge.py list gemini --limit 10 python skills_bridge.py suggest "Necesito automatizar browser y extraer datos" python skills_bridge.py show gemini.mouse python skills_bridge.py run gemini.mouse -- --request-json "{\"x\":0.5,\"y\":0.8,\"coordinate_space\":\"normalized\",\"action\":\"move\"}" python gemini_bridge.py skills gemini --limit 10 python gemini_bridge.py skill-info gemini.chatgpt --content python gemini_bridge.py skill-run gemini.vision_world ``` Operational rule: - `instruction` skills are discoverable and readable through the bridge. - `executable` skills can be launched through the bridge. - The orchestrator now injects relevant skill matches into model context alongside self-model, episodic memory, and world model. For direct coordinate-driven desktop control with calibration and verification: ```bash python run_mouse.py 960 540 --action click --label "boton enviar" --verify cursor python run_mouse.py 640 360 --action click --coordinate-space image --image-path screenshot.png --verify screen_change python run_mouse.py --request-json "{\"x\":0.5,\"y\":0.8,\"coordinate_space\":\"normalized\",\"action\":\"move\"}" ``` `run_mouse.py` stores a learned calibration profile per app and resolution, retries around the predicted point, and records successes/failures into the shared self-model and episodic memory. Status: **deprecated** as default strategy; keep only as explicit fallback for cases where CLI/browser/OS routes are not viable. ## Telegram Voice Notes `telegram_bridge.py` now accepts Telegram `voice` and `audio` messages. The bridge downloads the note, transcribes it locally with Whisper, and then sends the transcript through the same Gemini CLI flow used by plain text messages. Recommended local runtime: - `faster-whisper` on CTranslate2. - Default bridge config is `tiny` + `cpu` + `int8` to keep GPU impact close to zero while still supporting Spanish and other multilingual voice notes. - If you want a newer and more accurate model later, set `TELEGRAM_WHISPER_MODEL=turbo`, but expect much higher memory usage. Install: ```bash pip install faster-whisper ``` Optional `.env` overrides: ```bash TELEGRAM_WHISPER_MODEL=tiny TELEGRAM_WHISPER_DEVICE=cpu TELEGRAM_WHISPER_COMPUTE_TYPE=int8 TELEGRAM_WHISPER_LANGUAGE=es TELEGRAM_WHISPER_BEAM_SIZE=1 TELEGRAM_WHISPER_DOWNLOAD_ROOT=D:/IA/CODE/claudebot/models_stt/whisper TELEGRAM_WHISPER_VAD_FILTER=true ``` ## Capacidades avanzadas (secundario) Estos componentes son útiles, pero no son el núcleo del valor diario: - `self_model/` para conocimiento acumulado de routing. - `episodic_memory/` para reusar aprendizajes de ejecuciones previas. - `world_model/` para estado operativo compartido entre agentes. Si tu caso de uso principal es productividad cotidiana, empieza por el flujo CLI-first y añade estas capas cuando ya tengas una rutina estable. ## Shared Self-Model The repo now includes a live `self_model/` directory at the root. This is the explicit model of how the system understands itself. It is shared across: - Claude-style orchestrator runs - Gemini bridge runs - Codex or other CLI agents using the root wrappers Examples: ```bash python self_model_cli.py summary python self_model_cli.py plan "Fix this broken project and validate the output" python gemini_bridge.py auto "Abre Chrome y ve a https://example.com" python run_browser.py --agent codex_cli "Abre https://example.com y extrae el titulo" ``` The key point is that routing decisions and outcomes now update the same structured state instead of living only in ad-hoc logs. ## Episodic Memory The repo now also includes a separate `episodic_memory/` layer. This is not the self-model. - `self_model/` answers "what am I generally good or bad at?" - `episodic_memory/` answers "what happened last time in a similar task, app, page, or failure?" Each episode can store: - attempted task - executed steps - app, web, and screen context - exact failure - working fix or successful route Examples: ```bash python episodic_memory_cli.py summary python episodic_memory_cli.py find "Abre Chrome y ve a https://example.com y revisa el login" --task-type browser_automation ``` Claude-style orchestrator runs, Gemini bridge runs, and the root wrappers all write into the same episodic layer. ## World Model The repo now also includes a separate `world_model/` layer. This is the live desktop/browser/filesystem state that agents can consult before acting. It tracks things like: - open apps - active window - files created or touched - downloads in progress - browser tabs associated with tasks - pending objectives Examples: ```bash python world_model_cli.py summary --refresh python world_model_cli.py focus "Abre Chrome y ve a https://example.com y guarda un resumen" ``` The main orchestrator, Gemini bridge, and root wrappers all update the same shared world state.

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