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Found 2,404 Skills
Use when building or editing any AI feature in n8n: AI Agents, Text Classifier, Information Extractor, Sentiment Analysis, Summarization Chain, Basic LLM Chain, embeddings, vector stores, single one-shot LLM calls, or AI media generation (image / audio / video) via the native LangChain provider nodes. Triggers on any `@n8n/n8n-nodes-langchain.*` node, "agent", "chat assistant", "LLM with tools", "tool calling", "fromAi", "system prompt", "memory window", "structured output", "outputParser", "function calling", "RAG", "vector store", "embeddings", "classify with AI", "extract fields with LLM", "sentiment analysis", "summarize with LLM", "single LLM call", chat triggers with files, AI image / video / audio generation, or any multi-turn or one-shot LLM behavior.
Use when handling files, images, attachments, or binary data in n8n, OR when an AI agent needs to take a user-uploaded file as tool input or return a generated file. For Data Tables (schemas, dedup, persistent state), see the separate n8n-data-tables-official skill. Triggers on "file", "image", "PDF", "attachment", "binary", "upload", "download", chat trigger with files, agent tool that needs a file, vision/multimodal, or any handling of non-JSON file data.
Implement an approved spec or focused unambiguous task through stale-safe source edits. Use when the user wants code written — "implement this", "cook this spec", "/cook .cheese/specs/<slug>.md", or "fix this bug" when the fix is clear; also when the user just says "go" or "ship it" with a spec or clear acceptance criteria in scope. Runs standalone on an unambiguous task — a spec helps but is not required. Do NOT use for fuzzy planning (`/mold`), no-write discussion (`/culture`), or review-only work (`/age`).
Applies general engineering conventions optimized for AI agents. Use when creating or refactoring codebases and you need strict file discipline, clear module boundaries, naming/layout rules, and anti-pattern avoidance.
Requirements Discovery Specification, applicable to exploratory scenarios, helps users identify high-ROI functional directions when they are confused through role-playing. Automatically triggered, purely conversational inspiration.
Persistent memory architecture for AI agents across sessions. Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context). Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory.
Public leaderboard ranking OpenClaw instances by autonomous earnings — with proof.
Handling user intent and automating memory tasks.
Create and manage AI agent sessions with multiple backends (SDK, Claude CLI, Codex, Cursor). Also supports multi-agent workflows with shared context, @mention coordination, and collaborative voting. Use for "start agent session", "create worker", "run agent", "multi-agent workflow", "agent collaboration", "test with tools", or when orchestrating AI conversations programmatically.
Write, create, and improve CLAUDE.md project memory files for Claude Code. Use when: (1) Creating or bootstrapping a new CLAUDE.md, (2) Improving, refactoring, or splitting a bloated CLAUDE.md, (3) Questions about CLAUDE.md structure, imports, or modular rules, (4) After significant codebase exploration—cache discoveries to avoid re-crawling.
An AI Agent Skill that enforces a 'Risk Triage -> Align -> Act' protocol. Triggers when requests contain vague verbs ('optimize', 'improve', 'fix', 'refactor', 'add feature'), missing context (no file paths, unknown dependencies), or high-impact actions (deploy, delete, migrate). Prevents 'silent assumptions' through proactive audit.
Persistent shared memory for AI agents backed by PostgreSQL (fts + pg_trgm, optional pgvector). Includes compaction logging and maintenance scripts.