Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Audit the choices an implementing agent made, not its diff — a pure decision audit that traces the session's history into a choices ledger, changes no code, and never blocks an unsupervised run. Working code still embeds architecture the user never chose; surface it because future work inherits it. Use when the user wants to review the decisions the AI made on their behalf, before merging or committing AI-implemented work, when integrating a delegated subagent's pass, or when a fix "works" but might be a point fix.
Create hierarchical project plans optimized for solo agentic development. Use when planning projects, phases, or tasks that Claude will execute. Produces Claude-executable plans with verification criteria, not enterprise documentation. Handles briefs, roadmaps, phase plans, and context handoffs.
Score, evaluate, and iteratively improve any content or strategy using an auto-assembled panel of domain experts. Handles copy, sequences, landing pages, strategy docs, titles, charts, recruiting evaluations, or anything else that needs a quality gate. Recursively iterates until all scores hit 90+ (max 3 rounds). Use when asked to: "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "which version is better", "expert score", "evaluate this copy/strategy/page", or when another skill needs a quality gate on its output. Also triggers on: "score this landing page", "expert panel these email variants", "rate this headline", "panel these charts".
Generate declarative multi-agent systems (MAS) using POMASA pattern language. Use when building agent pipelines, orchestrating multiple AI agents, or creating research automation workflows. Supports patterns like Prompt-Defined Agent, Orchestrated Pipeline, Filesystem Data Bus, and Verifiable Data Lineage
Build a RAG (retrieval-augmented generation) pipeline or a custom search engine on top of Bright Data's Discover API, using intent-ranked web results and parsed page content as the retrieval/ingestion layer for an LLM or vector store. Use when the user wants to “build a RAG pipeline,” “add web search to my LLM/agent,” “ground my model in live web data,” “build a search engine over the web,” “ingest web content into a vector DB / knowledge base,” or “give my chatbot retrieval.” Covers both live retrieval (Discover at query time as a web-grounded retriever) and ingestion (Discover → chunk → embed → vector store → retrieve). Built on the `discover-api` skill. For a one-off written report use `live-research`; for raw markdown of specific known URLs use `scrape`.
Score the PATH an agent took (tool choice, ordering, retries, repeatability, grounding) — not just its final answer — to catch "corrupt success" (right answer via a bad/unsafe path) and flakiness. Deterministic trace schema + pass^k repeatability + config-driven rule checks (forbidden tool, out-of-order, retry-storm, excessive steps, edited-without-read grounding). Parses canonical traces.json, hooklib.audit / code-logger events.jsonl, OR a full Claude Code session transcript (--transcript, richest source: recovers retrieval args + observation + per- step ok). Trigger when the user says "grade the trajectory", "score the path not the answer", "did it cheat / take a bad path", "did it look before it acted / grounding", "corrupt success", "is this flaky / does it repeat", "pass^k", "grade a session transcript", "trace grader", or invokes /trace-grader.
Use when reviewing, designing, or modifying Java enterprise systems that use AI, LLMs, AI agents, RAG, tool calling, workflow automation, or model-based decision support and need EU AI Act regulatory awareness. This should trigger for requests such as Review a Java AI system for EU AI Act controls; Design governance for an AI agent with enterprise tools; Add human oversight and auditability to LLM workflows; Assess RAG or model-driven decision support before production release. Part of Plinth Toolkit
Помогает совместно прояснить задачу, проверить идею, найти корневую причину проблемы или выбрать подход без реализации. Используй, когда пользователь просит обсудить задачу, разобраться в проблеме, сравнить варианты или задаёт вопросы «что думаешь?» и «как лучше сделать?». Не используй, когда пользователь явно просит составить план, реализовать задачу, проверить изменения кода или сохранить знания проекта.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Load this skill when you are required to check source code outside of this codebase, or users give you a GitHub repo spec follows <owner-name>/<repo-name> spec. Use it when the current directory is not the target project, when another local project is needed, or when multiple projects are involved.
Agentic eyes for Godot work: cross-platform screenshot CLI (asset/region/window/screen) to budgeted WebP, TEMP editor viewport bridge under .gdskills/, and Taste Receptor Atlas v2 (~237 micro-receptors × 0–2: hierarchy, contrast, typography×41, affordance, space, color, icon, composition, light/FX, motion-tells, anti-slop, originality, identity). Use when agents must see UI, assets, or the editor — not for in-game screenshot Autoloads. Keywords: agent vision, screenshot, WebP, visual QA, taste receptors, typography, UI review, asset sheet, editor viewport, anti-slop
Shared execution rules for hhzz-cli: handles configuration, authentication, command discovery, permissions, write confirmation, result verification, unified business results, and error handling. Must be read before using any Heihu Zhizao business Skill. Does not replace specific business Skills or the CLI contract.
Check installed pstack personas, optionally register native agents, configure which models pstack uses per role and at what reasoning budget, and set up a repository PR template. Use for /setup-pstack, "configure pstack models", "pstack budget", "set up pstack PR templates", or changing pstack's model choices.
Use this skill when the user wants to “create a skill,” “add a skill to a plugin,” “write a new skill,” “improve a skill description,” or “organize skill content,” or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow
Guide for writing PostHog agent skills — job-to-be-done templates that teach agents how to use MCP tools to achieve a goal. Use when adding new product functionality that agents should know how to work with, creating a new skill, or updating existing skills in products/*/skills/ or .agents/skills/.
Install and configure OpenClaw with DingTalk, Feishu, Discord, and additional channels with Bailian/DashScope models on Linux hosts. Use when provisioning a new OpenClaw node, troubleshooting gateway/channel startup, standardizing openclaw.json mapping, or automatically discovering extra channels from https://docs.openclaw.ai/channels.
codeck entry point. Scans local files for materials, shows pipeline dashboard with diagnostic intelligence, guides user to the next step. Use when the user says "codeck", "new deck", "make a presentation", "make a deck", "new slides", "build a presentation", "export", "speech", "script", or wants to start or continue a presentation project. /codeck is the user-facing entry; legacy sub-skills are internal modules.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques, compression strategies, memory architectures, multi-agent patterns, evaluation, tool design, and project development.
Explain Like I'm 5 mode. Simplifies explanations with a patient, friendly parent voice while still compressing output tokens. Three levels: easy (full analogies, beginner), chill (plain language, default), quick (minimal, advanced). Use when user says "eli5", "explain like I'm 5", "simple mode", or invokes /eli5. Also triggers on explicit requests like "explain simply" or "make it simpler".
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Use when the user wants to manage Pipa configuration across profile setup, connectors, company brain or memory, permissions, automations, triggers, loops, or access audits. Route profile setup to pipa-setup and connector setup/status to pipa-connectors. Do not use for business work or an action in an already-connected app.
Guide for Vercel AI SDK v6 implementation patterns including generateText, streamText, ToolLoopAgent, structured output with Output helpers, useChat hook, tool calling, embeddings, middleware, and MCP integration. Use when implementing AI chat interfaces, streaming responses, agentic applications, tool/function calling, text embeddings, workflow patterns, or working with convertToModelMessages and toUIMessageStreamResponse. Activates for AI SDK integration, useChat hook usage, message streaming, agent development, or tool calling tasks.
Complete Windows file path troubleshooting knowledge for Claude Code on Git Bash and Windows environments. PROACTIVELY activate for: (1) file path errors on Windows, (2) backslash vs forward slash issues, (3) Edit/Write/Read tool failures, (4) MINGW path resolution, (5) cross-platform path conversion, (6) MSYS_NO_PATHCONV usage, (7) double-slash escape patterns (//c/foo), (8) UNC and long path limits, (9) WSL vs native Windows interop. Provides: path-conversion rules, troubleshooting flowchart, MSYS env-var recipes, double-slash escape patterns, and Windows file-operation best practices.
Decide where a learning, gotcha, or decision should live so it is not lost, and capture it there while context is fresh. Use when the user says they just discovered, learned, or found out something non-obvious, asks where a fact should be recorded, written down, or go, wants a setup gotcha, workaround, or decision kept for the next person or the next agent session, or asks whether something belongs in CLAUDE.md, a README, an ADR, tests, a glossary, or a reusable skill. Routes each fact to its durable owner and decides whether it is reusable or project-local. For the prose of the document itself see technical-writing; for recording a domain term see ubiquitous-language.
MindOS: local knowledge assistant and shared KB. Keeps decisions, notes, SOPs, debugging lessons, research findings, and preferences across sessions and agents. Core features: save notes, search KB, organize files, run workflows, review, append CSV, hand off context, and distill lessons. NOT for app source or paths outside KB. Triggers: save/record, search notes, update files, organize, run workflow, capture decisions, append CSV, hand off context, check past discussions, distill lessons. Proactive: (1) search first for stored context, (2) offer to save after valuable work, (3) suggest persisting key decisions.
Use when a solo founder is building their GTM motion, wants to scale without hiring, or needs to design an AI agent team for go-to-market. Also use when the user mentions “solo founder,” “one-person startup,” “solopreneur,” “bootstrapped,” “no team,” “AI agents as team,” “scaling without hiring,” “founder-led sales,” “lean GTM,” “one-person company,” or “no employees.” This skill covers the complete solo founder GTM playbook, from stack selection through agent team design, revenue-stage transitions, time allocation, and when to finally hire.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
