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Found 2,420 Skills
Base MCP — gives your AI assistant access to a Base Account via the Base MCP server (mcp.base.org). Wallet, portfolio, sending, swapping, signing, x402 payments, batched contract calls, and transaction history across supported chains.
Universal CLI client for Model Context Protocol (MCP) with persistent sessions, OAuth, tasks, and JSON output for shell scripting
Internal sub-skill for the job-hunt suite. Parses JD information from user-provided screenshots of any job platform (Boss Zhipin, Zhaopin, 51job, Liepin, etc.) and writes structured JD Markdown files to jd-pool. Do NOT invoke directly — use the job-hunt main skill instead.
Two-layer autonomous conductor — design loop produces decision packets, dispatch loop routes to bounded issues with dedupe/cooldown/archive controls. Replaces v1's single-agent persistence with a durable control loop that stops only at real blockers.
Used when executing implementation plans containing independent tasks in the current session
Triage inbound journalist source queries and draft a response only when the user's expertise is a real fit. Runs each query through proven source-request lenses (4-gate fit triage, credential-standing test, deadline read, BLUF/inverted-pyramid drafting), kills weak fits, asks for missing proof, and never auto-sends.
Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.
Explain and write effective instructions for the `/goal` feature — the persistent self-checking agent loop (plan → act → test → review → iterate), available in agents like Codex, Claude Code, and Hermes Agent. Use when the user mentions `/goal`, "goal loop", "Ralph loop", wants to kick off a long-running autonomous agent run, asks how to write a goal prompt, or wants a one-paragraph goal instruction drafted.
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.