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Found 13,293 Skills
Master context engineering principles for building production-grade AI agent systems with effective context management, multi-agent architectures, and memory systems.
Interactive workflow to generate a full-lifecycle AGENTS.md using semantic AST/LSP analysis and chained user interviews.
INVOKE THIS SKILL when creating, running, or operating a Managed Deep Agent against the LangSmith /v1/deepagents private-preview REST API. Covers the agent → MCP server → thread → streamed run flow, tool/interrupt configuration, and the agent file tree (AGENTS.md, skills/, subagents/, tools.json).
LLM-first SEO analysis skill with 16 sub-skills, 10 specialist agents, and 89 evidence collection scripts for comprehensive SEO audits
Deployable Flue agent harness lane for HTTP, CI, Node, Cloudflare, and sandbox-backed agents.
Research Methodology guides the agent through the complete scientific research lifecycle: hypothesis generation from literature gaps, experimental design with proper controls, systematic literature review, data collection protocols, and peer review preparation.
Use when the user wants to store, retrieve, search, or manage files in agent-fs — an agent-first filesystem backed by S3. Triggers on: "save this to agent-fs", "find that file", "store this document", "search agent-fs", "list my files", "show version history", "revert file", "set up agent-fs", "get a signed url", "share this file", "manage members", "invite user", "list members", "remove member", "update role", file persistence for agents, shared agent filesystem, or any mention of the agent-fs CLI. Also use when the user needs to manage drives, manage org/drive members, generate presigned URLs, check recent activity, or use semantic search across stored files. Also use when the user wants to run SQL over stored data files ("query this csv", "sql over my files", "duckdb", "aggregate the parquet file", "query the sqlite db", "join these spreadsheets"). Also use when the user wants to mount or unmount agent-fs as a Linux FUSE filesystem ("mount agent-fs", "fuse mount", "fuse", "remote mount", "sandbox mount", "expose drives as files", "use cat/grep/mv on my agent-fs files", "umount the drive", "mount a remote drive", "mount from sprite", "mount from e2b", "mount from hetzner"). Also use when the user wants to use agent-fs as a just-bash filesystem. Also use when the user wants to set up agent-fs without Docker or S3 ("local filesystem backend", "filesystem storage", "no docker", "onboard --filesystem", "store files on disk"). If the user mentions agent-fs in any context, always consult this skill.
Troubleshoots Google Cloud Gemini Enterprise Agent Platform issues (Agent Gateway, Registry, Identity, Policies, Model Armor, Identity-Aware Proxy (IAP)). Use when agent requests fail with 403 (especially unauthorized egress), Agent Runtime queries return 500, or gateway/IAP logs show permission errors. Don't use for general Google Cloud Identity and Access Management (IAM) debugging or networking issues unrelated to the Agent Platform stack.
Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
Diagnose the existing .claude/ system (CLAUDE.md, Agents, Rules, Skills, hooks), present the differences from the ideal state, and supplement/enhance it after user approval. Use it with commands like ".claude enhance", "CLAUDE.md enhance", "Agent maintain", "claude system update", "Rules add", etc. Use init-claude for new setups. Features include supplementing missing components via npx skills add, verifying prerequisites for implement-issue-tree, and differential updates without destroying existing assets.
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
Documenting agents. Applicable when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.