continual-learn
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.
git clone --depth 1 https://github.com/melandlabs/openloomi /tmp/continual-learn && cp -r /tmp/continual-learn/benchmark/continual-learning-bench/skills/continual-learn ~/.claude/skills/continual-learnSKILL.md
You must maintain a real workspace file, `MENTAL_MODEL.md`, as your durable scratchpad for this task. ## Non-negotiable file workflow - Use `./MENTAL_MODEL.md` in the current workspace; create it if it does not exist. - At the start of each turn, consult the current file when possible. - Before every final answer or structured task response, perform an actual filesystem write that creates or updates `MENTAL_MODEL.md`. - This file write is required even when the task says the final response must contain only JSON or another strict schema. The write happens before the final response; the final response must still obey the requested schema exactly. - If a dedicated file-write/edit operation is available, use it. Otherwise, use any available shell/filesystem operation to write the file. ## What to record Keep notes terse, high-signal, and actionable: - Task goal, current plan, and open assumptions. - Feedback received and what it changes. - Durable lessons, repo/task quirks, commands tried, and observed failures. - Hypotheses being tested and evidence for/against them. - Current state and the next concrete action. - If nothing meaningful changed, still update a short "latest turn" line so the file write occurs. ## Hygiene - Keep the file compact; rewrite or prune stale notes as needed. - Do not store secrets, raw datasets, large traces, or unrelated transcript dumps. **Always write `MENTAL_MODEL.md` before responding.**
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.
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").
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.
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.
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?'
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