openloomi
OpenLoomi is an open-source AI coworker. It connects your work tools, understands what you’re working on, and tells you what needs your attention, why it matters, and what to do next. It’s your open-source Attention Agent.
- 1k estrellas
- 45 forks
- 20 issues abiertos
- Lenguaje: TypeScript
- Licencia: Apache-2.0
- actualizado
Instalación
git clone https://github.com/melandlabs/openloomi && cp openloomi/*.md ~/.claude/agents/- Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
- Start a new Claude Code session to load the agents.
- Delegate work to them with the Task/Agent tool or by name.
24 items en este repositorio
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Use 1000+ external apps via Composio - either directly through the CLI or by building AI agents and apps with the SDK
Walk the user through installing the optional composio skill and turning on screen memory
Persist learning across turns by actively maintaining MENTAL_MODEL.md in the workspace. Use for iterative coding, debugging, benchmarking, or any feedback-driven task; read and update the file with actual filesystem writes before every response, never just hidden/internal memory.
Create an end-to-end Continual Learning Bench task. Use when adding a new src/tasks/<name> benchmark task, including design interview, concrete Jinja prompt templates, JSON action contracts, observation templates, debug schedule, behavioral E2E tests, validation, smoke/debug runs with icl and icl_notepad, and README documentation.
Drive a native macOS app via the cua-driver CLI (default) or MCP server — snapshot its AX tree, click/type/scroll by element_index, verify via re-snapshot. Use when the user asks you to operate, drive, automate, or perform a GUI task in a real macOS application on the host (e.g. "open a file in TextEdit", "navigate to /Applications in Finder", "click the Save button in Numbers").
High-performance data analysis using Polars - load, transform, aggregate, visualize and export tabular data. Use for CSV/JSON/Parquet processing, statistical analysis, time series, and creating charts.
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Discover and install skills from the open agent skills ecosystem. Use when: (1) user asks "how do I do X" where X might have an existing skill, (2) user says "find a skill for X" or "is there a skill for X", (3) user asks "can you do X" where X is a specialized capability, (4) user wants to extend agent capabilities, (5) user wants to search for tools, templates, or workflows, (6) user mentions they wish they had help with a specific domain (design, testing, deployment, etc.).
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
Show OpenLoomi Loop dashboard state (pending decisions, connectors, last tick) and force-refresh connector health — thin doorway into the openloomi-loop sub-skill
Search OpenLoomi memory (files + knowledge base + insights) — thin doorway into the openloomi-memory sub-skill
OpenLoomi entrypoint for skill-only agent runtimes without a plugin mechanism. Use when the user mentions OpenLoomi or Loomi, wants first-use setup guidance, wants to use local OpenLoomi memory, connectors, Loop, knowledge base, RAG search, or asks how to install OpenLoomi skills in runtimes such as WorkBuddy or other skill-capable agents.
openloomi HTTP API reference (local-first, served from the OpenLoomi Desktop app at http://localhost:3414). Use when working with openloomi backend routes — auth, AI, files, integrations, RAG, memory, Loop, pet, workspace, platform callbacks. Triggers: API endpoints, backend routes, /api/*, local API, port 3414, integrations REST, OAuth start, RAG search, loop state, memory search, pet state, audit logs
openloomi Connectors tools - manage the native 7 messaging integrations and pair with the composio skill for the 1000+ apps OAuth layer (Slack, Discord, X, Gmail, Outlook, Google Calendar/Drive/Docs, GitHub, Notion, Linear, HubSpot, LinkedIn, Jira, Asana). Triggers: connect platform, integration status, list accounts, disconnect, list-accounts, status, connect, send-reply, native vs composio, 1000+ apps, list connections.
Use this when users ask about openloomi features, capabilities, or how to use it. Examples: 'openloomi 怎么用', '你能做什么', 'What can you do?', 'How does openloomi work?', 'Tell me about openloomi features', 'What platforms does openloomi support?', 'How do I use scheduled tasks?', 'What is Loop?', 'How does the attention agent work?', 'What is a Decision Card?', 'How do connectors work?', 'How do I extend Loop with custom types?', 'What is a classifier rule?', 'How do I plug openloomi into Claude Code / Codex?'
Inspect OpenLoomi Goals from a skill-capable agent. Use when listing active Goals or checking a Goal's ordered steps and current progress.
OpenLoomi × Claude Code hooks installer. Use when the user wants Claude Code's lifecycle (SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop, SubagentStart, SubagentStop, Notification) to mirror onto the Loomi Pet, or wants to auto-archive every Stop into OpenLoomi memory. Triggers: install hooks, /openloomi:hooks, hooks install, hooks uninstall, hooks status, mirror claude on pet, auto-archive stop.
OpenLoomi install & first-use setup helper for Codex. Use when the user wants to install OpenLoomi, configure it, or troubleshoot `INSTALL_REQUIRED` / `SOURCE_FOUND_APP_NOT_BUILT` / `SESSION_INITIALIZATION_REQUIRED` after running setup-status. Triggers: install openloomi, configure openloomi, setup openloomi, openloomi not installed, openloomi not finalized, install_required, install missing, guest session.
Identify whether a user is asking OpenLoomi to generate a lifestyle image, portrait, personal visual, or lifestyle scene. Use this skill for chat messages that may request image generation for a user's lifestyle, identity, persona, personal brand, social profile, memories, interests, or real-life scene; distinguish true generation intent from ordinary image understanding, prompt-writing, editing, or negated requests.
openloomi's Loop — the proactive execution brain that runs inside the OpenLoomi desktop app. Use this skill to inspect state, run a tick, schedule / cancel decision actions, tune preferences, and extend Loop with user-defined decision types, Composio-backed signal channels, or deterministic classifier rules. Triggers: 'openloomi loop', 'loop tick', 'loop schedule', 'loop inbox', 'loop run', 'proactive decisions', 'signal → decision → execute', 'pull signals', 'decision queue', 'register loop type', 'add loop decision type', 'register custom channel', 'add composio channel', 'add loop rule', 'register classifier rule', 'force loop type', 'dry-run loop rule', 'list my loop extensions', 'remove loop type', 'delete loop channel'
openloomi Memory tools - search and manage the holistic context (people, projects, decisions, knowledge base, chat insights). Triggers: memory search, knowledge base, search documents, list insights, who is John, what did we decide about X, tiered memory, knowledge graph, people/projects/decisions, search-all, conversation memory
Resumen de Subagents
Lo que la gente pregunta sobre openloomi
¿Qué es melandlabs/openloomi?
melandlabs/openloomi es subagents para el ecosistema de Claude AI. OpenLoomi is an open-source AI coworker. It connects your work tools, understands what you’re working on, and tells you what needs your attention, why it matters, and what to do next. It’s your open-source Attention Agent. Tiene 1k estrellas en GitHub y su última actualización registrada es del 2026-09-24.
¿Cómo se instala openloomi?
Puedes instalar openloomi clonando el repositorio (https://github.com/melandlabs/openloomi) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar melandlabs/openloomi?
Nuestro agente de seguridad ha analizado melandlabs/openloomi y le ha asignado un Trust Score de 92/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene melandlabs/openloomi?
melandlabs/openloomi es mantenido por melandlabs. La última actividad registrada en GitHub es del 2026-09-24, con 20 issues abiertos.
¿Hay alternativas a openloomi?
Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.