Skills
97,725 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Используй для ЖИЗНИ вимарши и работы с ПОЛЕМ вопрошания — не постановка (это writing), а жизненный цикл: якорение, разрешение, закрытие, принятие как есть, вытеснение, кристаллизация, привязка к превращению. Триггеры: «разбери вимарши», «поле вопрошания», «закрой/оставь вимаршу», «разреши вопрошание», «триаж вимарш», work the field of inquiry, resolve the question, много висящих вимарш в orient, закрытие сессии. Не проход по всему графу (assembly). Нужны тулы iskron_*.
Используй для задач интеграции и целостности превращения. Триггеры: «интеграция», «интегрировать X с Y», «спецификация интеграции», integrate, integration spec — режим 0: сначала граф; «область интеграции изменения», integration scope, cold review — режим 1: холон + steward + узлы изменения; «проверь бяньхуа на целостность», «что затронет превращение», «кого заденет», integrity check — волновой фронт; «подкреплено ли графом», «не театр ли», claim audit — обратный аудит прозы. Distinct from assembly и design. Нужны iskron_*.
Source the next ML experiment proposal from the user via one of three entry points selected by `AskUserQuestion`: (a) a scientific article URL the agent must read and synthesize, (b) a resource link or path (GitHub issue / spec file / reference repo), or (c) free-text the user types directly. In every branch, the agent reads the source, synthesizes its understanding of what to implement, and confirms with the user *before* returning the Proposal block. Hand the confirmed Proposal back to `iterate-ml-experiment`, which writes it into `journal/NN_short_name.md` and seeks the user's design-note approval. Stops at "Proposal returned, user-confirmed"; never writes a design note, never authors acceptance criteria. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user picks `user` from the menu; the user volunteers a concrete idea ("I want to try X"); the user pastes or links a scientific article, GitHub issue, spec file, or reference repo and asks us to read it. SKIP when: the user wants to mine the previous report (use `iterate-from-skore`); the user is asking for a symbol lookup or pipeline mechanics (use the `python-api` skill); the work is evaluation mechanics on a single report (route to `evaluate-ml-pipeline`). HOW TO USE: open with an `AskUserQuestion` for the entry point — article-link / resource-link / free-text. In each branch: gather the source material with the appropriate tool (`WebFetch`, `gh issue view`, `Read`), synthesize what you understand into the three shaping questions, and **confirm with the user via plain text** (one or two sentences: "you'd like to implement X by changing src/<pkg>/<file>.py — right?") before returning the Proposal. Do not write any design note. Do not author acceptance criteria.
Use this skill to drive Compose HotSwan from an AI agent (Claude Code, Cursor, any MCP client) so the agent can edit a Kotlin file, trigger a hot reload, capture a device screenshot, evaluate the result against a design intent, and iterate without a human in the loop. Covers the seven HotSwan MCP tools (hotswan_get_status, hotswan_reload, hotswan_take_screenshot, hotswan_start_snapshot, hotswan_stop_snapshot, hotswan_select_variant, hotswan_build_and_install), the canonical edit-reload-screenshot loop, snapshot-based rollback, and when to fall back to a full install for schema changes. Use when the developer says "get the AI to tune this screen until it matches a mock", asks "can the AI see what changed?" or "can the AI screenshot the device?", sets up a Claude Code or Cursor workflow that needs MCP tool access, or wants AI-driven UI iteration.
Install and configure MCP servers across desktop and CLI clients (Claude, Cline, Windsurf, Cursor, VSCode, Gemini CLI, Codex, Trae, Antigravity, etc.) on macOS/Windows/Linux, preferring @smithery/cli when supported and otherwise performing manual JSON config updates and path discovery.
Find, install, and configure OpenCode plugins from the catalog or community. Use proactively when user asks about plugins, requests new capabilities, or mentions extending OpenCode functionality. Examples: - user: "Is there a plugin for Tailwind CSS?" → list catalog, read tailwind plugin details, install if available - user: "How do I add a custom slash command?" → suggest command-creator skill or guide through opencode.json setup - user: "What plugins are available for database work?" → list catalog, filter for database-related plugins - user: "Install the playwright plugin" → read plugin file, add to opencode.json, verify installation
Prompt engineering and agentic orchestration patterns. Use when crafting prompts for reasoning models, implementing chain-of-thought or Tree-of-Thoughts, designing ReAct loops, building few-shot examples, optimizing prompt performance, structuring system prompts, using extended thinking, or designing tool use workflows. Use for prompt templates, multi-step agent workflows, structured thinking protocols, and multimodal prompting.
For /swarm, requests for multi-agent collaboration, deciding whether to spin up a team, or complex tasks that clearly need work split across people. First choose a single person, subagent, workflow, or team mechanism, then orchestrate roles, execution and acceptance.
Implement Beads or beads_rust work items from an issue id. Use when the user runs $beads-implement <bead-id> or asks to implement a bead/epic; if it is an epic, implement all sub-tasks, committing after each one and only interrupting when blocked or a decision is needed.
Designs new software features and multi-component changes for the current repo into implementation-ready BDD specs via autonomous codebase research, then commits the design for review (runs to completion without mid-design questions). Scope covers new features, greenfield design, and multi-component changes. Not for hardware/physical-system design, out-of-repo questions, single-file refactors, or known-root-cause bugs (those go to direct edits or systematic-debugging).
Parallelize an approved spec across 5+ independent behavioural curds into reviewable PRs, running the whole pipeline fanned out at once. Use when the user has such a spec — phrases like "/cheese-factory .cheese/specs/<slug>.md", "send through the factory", "parallelize this spec", "many curds", "fan out the implementation", "cheese-factory this". Runs inline and spawns full-peer general-purpose sub-agents per phase (decomposer, per-curd workers, wiring, post-merge review, PR planner), ending in 1–N reviewable PRs via `/pr-stack`. Use even when the user mentions `/fromagerie` — `/cheese-factory` is the portable harness-agnostic sibling. Supports `--hard` propagation and `--resume <slug>` to continue a crashed pipeline. Do NOT use for single coherent specs (`/cook`, `/ultracook`), fuzzy planning (`/mold`), review-only work (`/age`), or specs with fewer than 5 curds (`/ultracook`).
Create and optimize CLAUDE.md memory files or .claude/rules/ modular rules for Claude Code projects. Comprehensive guidance on file hierarchy, content structure, path-scoped rules, best practices, and anti-patterns. Use when working with CLAUDE.md files, .claude/rules directories, setting up new projects, or improving Claude Code's context awareness.
Browser automation guidance and patterns. Trigger this skill BEFORE starting any browser interaction (navigating pages, logging in, scraping, filling forms, testing UI) to learn the correct workflow and avoid common pitfalls. Also trigger when browser MCP tools misbehave: element not found, click does nothing, page crashes, timeout, "target closed", snapshot returns empty, or any unexpected browser behavior. This skill teaches you HOW to use the browser MCP tools effectively — without it you will hit avoidable failures like stale UIDs, heavy-page crashes, and silent navigation failures.
Auto-invoked at session start when swain-preflight detects issues. Also user-invocable for on-demand health checks. Validates project health: governance rules, tool availability, memory directory, settings files, script permissions, .agents directory, and .tickets/ validation. Auto-migrates stale .beads/ directories to .tickets/ and removes them. Remediates issues across all swain skills. Idempotent — safe to run any time.
Tool lifecycle UI components for React/Next.js from ui.inference.sh. Display tool calls: pending, progress, approval required, results. Capabilities: tool status, progress indicators, approval flows, results display. Use for: showing agent tool calls, human-in-the-loop approvals, tool output. Triggers: tool ui, tool calls, tool status, tool approval, tool results, agent tools, mcp tools ui, function calling ui, tool lifecycle, tool pending
List, select, and install Volcengine skills from the volcengine-skills marketplace. Use when a user asks which Volcengine skill or plugin handles a task, wants to browse every available skill, needs an optional skill that is not currently installed, or asks to install or check the installation status of a Volcengine skill. Also use to discover any core skill, including this finder. Use as a fallback during a Volcengine operation when the currently loaded or installed skills cannot cover the required product, tool, or workflow; inspect the full catalog, select the missing capability, and install it.
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
Creating and maintaining CLAUDE.md project memory files and .claude/rules/ rule files that provide non-obvious codebase context. Use when (1) creating a new CLAUDE.md for a project, (2) adding architectural patterns or design decisions to existing CLAUDE.md, (3) capturing project-specific conventions that aren't obvious from code inspection, (4) organising instructions into path-scoped rule files.
File Beads or beads_rust epics/issues from a finalized plan/spec AND do the polish pass (clarity, acceptance criteria, sizing, deps). Use when asked to create Beads from a plan/spec (OpenSpec, PRD, design doc), convert an external plan into issue tracker structure, or review/refine an existing Beads set.
Receive and verify Anthropic Claude Managed Agents (CMA) webhooks. Use when setting up Claude Managed Agents webhook handlers, debugging signature verification, or handling agent session and vault events like session.status_idled, session.status_terminated, session.thread_created, vault.created, or vault_credential.refresh_failed.
Create Claude Code custom slash commands with proper structure, frontmatter, and best practices. Use this skill whenever the user wants to create a new command, add a slash command, build a custom command, or mentions "create-command", "new command", "add command", or "make a command" for Claude Code. Also trigger when the user wants to turn a workflow into a reusable command.
Use when receiving vague or unclear ERPNext/Frappe development requests that need interpretation. Transforms requirements like 'make invoice auto-calculate' or 'add approval workflow' into concrete technical specifications. Determines which Frappe mechanisms to use and maps to the full 61-skill catalog. Keywords: vague requirement, clarify scope, translate business need, technical spec, implementation plan, what does this mean, unclear requirement, translate to code, how to build this.
Enable the evidence-chain rule (R19) for the CHAT portion — every reasoning chain must terminate in observable evidence (observed/tool-record/graph-edge/web/parametric/absence), unlike fable5 (general reasoning discipline, no citation requirement). Use when the user says 'graph-mode', 'bật graph mode', 'evidence mode', or invokes /graph-mode. Token-heavy (forces more citations/file opens per answer) so it is NOT auto-injected every session — enable only when invoked manually. Once invoked it stays active for the rest of the session (toggle off with 'tắt graph mode' / 'graph-mode off' / 'normal mode').
Reduce unresolved user-owned judgments before planning or implementation. Research discoverable facts, resolve model-owned technical choices, order remaining decisions by dependency, ask only the material current frontier, and route questions that conversation cannot settle to the smallest deciding observation. Use explicitly for `$grill-me` or implicitly only when a non-discoverable user-owned decision materially blocks the requested outcome. Do not seize direct implementation when safe reversible defaults suffice.
Migrate Python LangChain Deep Agents applications to Pydantic AI and Pydantic AI Harness. Use when the source uses the upstream `deepagents` package or demonstrably reproduces its middleware, backends, skills, memory, subagents, or sandbox contracts. Use `migrating-langchain-to-pydantic-ai` for plain LangChain, LangGraph, or LCEL migrations and `pydantic-ai-harness` for greenfield Harness usage.
How to drive Muxy 2 from a shell with the `muxy` command — start terminals that keep running, type into them, read and wait on their output, and manage projects and Git worktrees. Use this when you are an agent running inside a Muxy terminal (or any script on the same computer) and want work to run in its own terminal instead of blocking yours, or to drive the desktop app's built-in browser.
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.
