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Found 2,319 Skills
Deep-dive diagnosis of how your AI agent behaves in production. Explores LangWatch analytics and traces end to end to map failure patterns, dissatisfied users, token cost hotspots, edge cases, behavior changes, and outliers, then delivers an HTML report where every finding links to real example traces. Use when you want to truly understand what your agent is doing in production.
Gather external knowledge the spec needs and distill it into §R — the durable research log — so build grounds in facts instead of hallucinating library behavior. Each finding cites a source; unsourced claims are flagged, never written as fact. Triggers when a spec decision hinges on a library/API/best practice the agent is unsure of, when the user says "research this", "what's the best lib for…", "check current best practice", or invokes /ck:research. Defers the §R write to the spec skill.
Add persistent, structured long-term memory to AI agents using Maximem Synap. Use this skill whenever the user is building, debugging, or evaluating an AI agent and mentions any of: "memory", "long-term memory", "persistent memory", "agent memory", "remember across sessions", "context window", "agent forgets", "user preferences", "personalization", "RAG over conversations", "multi-tenant memory", "memory layer", "Mem0", "Zep", "Letta", "SuperMemory", "Cognee", or asks how to integrate memory into LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, Pydantic AI, CrewAI, AutoGen, Google ADK, Haystack, Agno, Semantic Kernel, Microsoft Agent Framework, NVIDIA NeMo, LiveKit, Pipecat, Claude Agent SDK, Mastra, Vercel AI SDK, or MCP (no-code). Also trigger on direct mentions of "Synap", "Maximem", "maximem-synap", or `synap-*` package names. Covers SDK setup, scoping (User/Customer/Client), ingestion, retrieval, and one drop-in package per framework.
Use when generating agent-native onboarding docs, coverage maps, health baselines, and agent adapters for a repo.
Use when scaffolding the agent knowledge layer (ARCHITECTURE.md, QUALITY_SCORE.md, docs/) for a repo.
Use when an agent is asked to define, review, or write acceptance criteria for a request or plan. Derives acceptance criteria from the current request context, confirms them with the user, and writes them into the plan file or a standalone acceptance_criteria.md file.
Complete guide to deploying OpenClaw on Alibaba Cloud servers, covering Alibaba Cloud Bailian model configuration, full-platform access for Feishu/DingTalk/QQ/Discord, Qoder CLI integration, as well as advanced configuration and O&M.
AI 에이전트를 위한 한국 서비스 자동화 스킬 모음 — SRT/KTX 예매, KBO, 로또, 카카오톡, 지하철, HWP, 우편번호 등
Build MCP (Model Context Protocol) servers including tool definition, schema design, authentication, error handling, and Claude Code integration. Use this skill when the user needs to create an MCP server, expose APIs or databases to AI agents, design tool schemas, or integrate with Claude Code — even if they say 'build an MCP server', 'connect Claude to our database', 'expose our API to AI', or 'create a tool for Claude Code'.
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
Meta-skill: helps create an AGENTS.md for a new project by guiding the user through selecting the right profile from the agentic library and running the compose command. Also helps create a custom AGENTS.md from scratch when no profile fits. Invoked when the user asks to set up agent instructions, create AGENTS.md, or configure agents for a project.
Persistent memory system for Claude Code. Two-layer architecture (hot cache + knowledge wiki), safety hooks, /close-day end-of-day synthesis. Zero external dependencies.