Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Составляет однозначный план реализации без написания кода. Используй, когда пользователь явно просит составить, подготовить или расписать план реализации, внедрения либо исправления. Не используй, когда пользователь хочет обсудить варианты и ещё не выбрал решение, просит зафиксировать только желаемый результат без шагов, сразу реализовать задачу или провести ревью изменений.
Sets up and configures Convex agents for chat-based AI interactions. Use when initializing agent instances, creating conversation threads, and generating basic text or structured responses from LLMs. Essential foundation for any Convex agent implementation.
Use when integrating, upgrading, debugging, or reviewing @qfei-design/make-ai-assistant in a Make App: Agent discovery, multi-session chat, streaming, file/image input, Service gateway routes, and package-owned UI. Does not own generic dialogs, model behavior, authentication or permission policy, DSL, or publishing.
End-to-end, phase-gated SDLC for AI coding agents: relentless design interview → reviewed spec → dependency-graphed task board → safe-wave TDD execution → verification → converge. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations. Triggers on “absolute work”, “build this end-to-end”, “plan and build”, “break this into tasks”, “grill me on this plan”, “pick up this ticket”, “run this migration”.
A complete Agent Skill for using the ask-bridge CLI. Use ask-bridge to delegate low-risk, exploratory AI research, summarization, document analysis, code snippet analysis, error-message organization, option comparison, first-draft generation, or delegable background research to the ChatGPT, Gemini, or Claude websites. Use when Codex needs to call web-based AI through the local ask-bridge command, use ChatGPT/Gemini/Claude web quotas, attach files or images, switch models, set timeouts, retrieve or save responses, download generated images, manage browser sessions, update ask-bridge, or look up all options and subcommand usage.
Multi-agent workflow orchestrator for software projects. Invoke when the user says "Hey Buddy", asks to work on a task, wants to check Linear tasks, start continuous monitoring for new issues, or needs a full develop-test-review cycle on any software change. Orchestrates specialized agent roles in a loop until the task is complete.
Manage Claude Code session transcripts from local JSONL storage, including listing candidate sessions, exporting full or selected sessions to organized Markdown, inspecting archives, and summarizing tool-call history. Use when the user asks to scan, parse, archive, inspect, recover, summarize, manage, or convert Claude Code sessions, `~/.claude/projects` data, `.jsonl` transcripts, tool-call history, or hard-to-read Claude Code conversation logs.
Full-power feature implementation using parallel subagents for backend, frontend, testing, and security, with worktree isolation and quality verification in one workflow. Chains with /ork:cover for tests and /ork:verify for validation. Use when asked to build, add, create, scaffold, or set up a new feature, endpoint, component, or UI capability. Not for fixing a bug, reviewing, explaining, testing, or comparing existing code.
Guide developers through creating and updating MCP Apps. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/views, debugging, running dev servers, deploying and connecting apps to ChatGPT. Use when a user wants to create or update a MCP App, MCP server or use the Skybridge framework.
Authoritative reference for Pi (@earendil-works/pi-coding-agent): installation, configuration, execution, and extension. Use for the CLI and configuration, providers and models, extensions, skills, MCP, codemode, the SDK/RPC, security and trust, design philosophy, and the pi repository's internals and contribution rules
Writes, rewrites, diagnoses, and improves any LLM prompt with minimal, high-signal edits. Use when the user wants to create a new prompt from scratch, review or fix a prompt that produces poor output, simplify or tighten instructions, restructure a long prompt, or expand an existing prompt. Covers system prompts, agent instructions, CLAUDE.md rules, SKILL.md prompt bodies, chat templates, structured-output prompts, RAG context templates, and prompt strings embedded in code. Also use when editing any file whose primary content is LLM instructions. Do not use for skill scaffolding, packaging, or evaluation runs; rewriting an existing skill's prompt body is in scope.
Orders scheduler. Reads .noodle/mise.json and writes .noodle/orders-next.json. Schedules work orders based on backlog state, plan phases, session history, and task type schedules. Triggers when orders are empty, after backlog changes, when the loop re-evaluates, or when /schedule is invoked.
Analyze an existing web application, identify safe user-visible capabilities across routes, forms, server actions, handlers, and schemas, then implement first-party WebMCP tools and validate discovery and invocation with Stagehand. Use when the user asks to make a codebase agent-ready, expose website features as WebMCP tools, or add WebMCP directly to an app rather than generating a standalone injection script from a URL.
Maps and documents codebases of any size by orchestrating parallel subagents. Creates docs/CODEBASE_MAP.md with architecture, file purposes, dependencies, and navigation guides. Updates CLAUDE.md with a summary. Use when the user says "map this codebase", "cartographer", "/cartographer", "create codebase map", "document the architecture", "understand this codebase", or when onboarding to a new project. Automatically detects if map exists and updates only changed sections.
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.
ONLY for Pi Agents — all other agents have their own web tools. How Pi accesses the web via the pi-web-access package — search, fetch URLs/PDFs/YouTube/GitHub. Use whenever a Pi task needs current info, docs, news, prices, or content from a specific URL.
Use this skill when the user asks to “create an agent,” “add an agent,” “write a subagent,” “agent frontmatter,” “when to use description,” “agent examples,” “agent tools,” “agent colors,” or “autonomous agent,” or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on “evaluate”, “test quality”, “is this skill working”, or QA of AI workflows.
Use when the user hands over a dsh plugin repository (or a real migration commit / version corridor) and wants its upgrade experience extracted into one auto-graded Harbor benchmark exam task — produce tasks/<id>/ (fixture + instruction.md + task.toml + judge.mjs + solution) that harbor run can grade 0–1 and that folds into the dsh-plugin-upgrade-skill benchmark suite; also applies to turning existing version cards (references/v*.md) into executable exam tasks. Not for writing upgrade cards; not for running the benchmark.
Foundation for the olares-cli skill suite: choose the right domain skill, understand the Olares platform model each task depends on, and decide whether the active profile can proceed or needs login recovery. Use first for runtime skills, and directly for Olares ID, profile, login, 2FA/TOTP, refresh token, keychain, auth errors, or uncertainty about which Olares skill owns a task.
Process large codebases (>100 files) using the Recursive Language Model pattern. Orchestrates parallel sub-agents to map-reduce across files without context rot. Use when: analyzing large repositories; auditing security or auth across many files; finding patterns across 50+ files; processing large log files or data dumps.
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.
