Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Analyze a codebase to detect its language, framework, conventions, and CI gates. Run this on first use in a project to generate stack context that all coding principle skills reference. Use when the user says "detect stack," "analyze this project," or "what's this codebase using."
During development, save a very brief clue when encountering a non-obvious failure, user correction, or repetitive operation that could be automated. Don’t interrupt the main task or search for evidence; don’t write anything to disk for pure consultation or read-only tasks. Leave deeper investigation to agi-mode retrospectives.
Extended thinking (ultrathink) configuration for Claude API. Activate for complex reasoning tasks, deep analysis, multi-step problem solving, and tasks requiring careful deliberation. Enables Claude's internal reasoning with configurable thinking budgets.
Use when a new Godot system or feature has open design decisions — interrogates them in batched rounds, scope first, each question with a recommended answer, and records the answers before any design or code. Triggers on "grill me", "ask me first", "question me on the design", "what do I need to decide before building". Not for choosing between nodes or APIs, and not for bug fixes.
Manage CloudRobo embodied-agent task dispatch (robo-dispatcher) — create embodied tasks that drive a robot via an exec/model and constraints, list/show tasks in a session, cancel tasks, and retrieve task results. Dispatch orchestrates robots (robot_id from cloudrobo-robot) with inference models (exec_model_id from cloudrobo-infer / cloudrobo-asset) inside a session. Triggers include: dispatch, dispatcher, task dispatch, embodied task, agent task, run task on robot, task scheduling, cancel task, task result, robo-dispatcher, 调度, 智能体调度, 任务下发, 机器人任务, 取消任务, 任务结果, 会话任务.
Interactive loop that builds and tunes an AI prompt for evaluating whether a company fits a client's ICP. Run after any list-building skill (disco-like, blitz-list-builder, google-maps-list-builder, prospeo-full-export) to qualify companies before scaling. Iterates batches of 10 companies with user feedback, stops when 2 consecutive rounds have zero corrections, saves the final prompt for reuse. Always uses Claude Code Task sub-agents — never an external API key.
Score how ready a website, domain, or public MCP endpoint is for AI agents using Is Agentic, and read or act on the resulting report. Use when asked to check a site's agent readiness, get its Is Agentic score, fetch a scored report as JSON, or fix the issues that lower a site's score.
Extract insights from multi-agent interactions, identify patterns, and build collective intelligence through cross-agent learning and knowledge management. Use when synthesizing findings, building knowledge bases, or improving system-wide practices.
A skill for judging the separation of responsibilities among agent / workflow / tool / memory / state, and the boundary between what is left to the LLM and what is deterministic code, when designing, implementing, or refactoring AI services that use Mastra as their runtime. Use it for "design it with Mastra", "create a workflow", "what should state hold", or "do we need memory or a workspace", and apply single-agent first and minimal workflow. For checking, validating, or migrating to the current API specification, use mastra-framework-guide.
(NS) Autonomous execution engine — plans depth, resolves doubts from docs/specs, multi-agent work in an isolated worktree. Use for a local/pasted plan or "run this autonomously" without GitLab. Also Phase 2 under ns-execution-gitlab-issue (do not run standalone then). Not for single-step edits (ns-code-coder) or GitLab issues directly (ns-execution-gitlab-issue).
(NS) Orchestrate partitioned version implementation slice-by-slice — one subagent per slice, commit, auto-advance until done or stop. Use when version-roadmap.md has pending slices and the user asks to orchestrate/execute all slices. Do NOT use for non-partitioned versions, ad-hoc coding (ns-code-coder), or partitioning itself (ns-sdd-version-partitioner).
(NS) Generate or refresh lean architecture-rules.md for AI agents — stack, layout, patterns, constraints. Use when the user asks for architecture rules, .nextstage-harness/rules/architecture-rules.md, agent conventions, or "document how this repo works for the AI". Do NOT use for business specs (ns-harness-codebase-reverse-spec), brownfield maps (ns-harness-bootstrap-brownfield), or one-off Cursor rules without a full architecture pass.
(NS) Onboard an existing codebase into spec-driven planning — map stack, modules, rule adherence, and gaps without modifying code. Use when the user says analyze existing project, brownfield bootstrap, or before first requirements on a repo with code but no requirements. Outputs brownfield-map.md for ns-sdd-requirements-generator. Read-only on source code. Do NOT use for greenfield with no app code, architecture-rules generation, or writing requirements.md.
(NS) Full post-harness-init bootstrap in one session — architecture rules, sync, brownfield map, reverse business spec, project AGENTS.md. Use for "harness prepare", post-install setup, onboard brownfield, or running all prepare skills without separate slash commands. Do NOT use for greenfield with no app code, single-skill runs, or CLI-only baseline AGENTS.md.
Search and query past Claude Code and Codex session history. Reactive: when the user asks "how did I fix X", "what did we do last time", "find the session where", "上次怎么修的", "之前的session", "历史记录". Proactive: when the user references past work you lack context for, when you're about to modify a file with complex edit history, when the user says "继续之前的" or "continue where we left off", or when understanding prior decisions would improve your current response. Memory: when the user says "记住这个", "remember this", "写入记忆", "save this conclusion", or when you determine a retrieval result contains a conclusion worth persisting.
Create, test, evaluate, optimize, and package OpenCode skills with the opencode-skill-creator plugin. Use when users explicitly mention opencode-skill-creator, OpenCode Skill Creator, creating an OpenCode skill, editing an OpenCode SKILL.md, running skill evals, benchmarking skill performance, or optimizing an OpenCode skill description. Do not use for generic Claude Code or Superpowers skill creation unless the user asks to port that workflow to OpenCode.
Autonomous multi-project orchestration engine. Audits all registered projects, structures work into dependency-wired tasks, dispatches parallel agents (subagents or Agent Teams), audits completions, and ships PRs. Registry-driven — works for any user with a configured project registry.
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like “improve this prompt”, “write a system prompt”, “optimize my instructions”, “help me prompt engineer”, “audit this prompt”, “review my prompt”, or when building agentic systems that need structured prompts.
Deep multi-perspective reasoning via parallel isolated subagents + Codex deliberation host, with final Markdown + HTML + PDF report output. Use for complex questions, tech decisions, social topics, architectural choices, hard analysis, or any question worth thinking harder about. Triggers include 深度分析, 多角度推理, 验证答案, heavyskill, think harder, 让我们深入思考, 推敲一下, 讨论一下, 分析利弊. Do not use for simple factual lookups or routine tasks.
Use when recursive-mode work may benefit from delegated audit, review, or bounded implementation support. This skill prioritizes analyst, planner, code-reviewer, memory-auditor, tester, and implementer roles, with mandatory self-audit fallback when subagents are unavailable.
Create evidence-backed repository intent docs for AI coding agents, including certainty levels, evidence anchors, open questions, and review workflow. Trigger on document repo intent, REPO_INTENT.md, or what this repo is for. Do NOT use for ordinary README polishing.
Unified multi-dimensional code review with automated fix orchestration. Routes to session-based (git changes), module-based (path patterns), or fix mode. Triggers on "workflow:review-cycle", "workflow:review-session-cycle", "workflow:review-module-cycle", "workflow:review-cycle-fix".
Save session wrap-up documentation before clearing context by running the session-documenter workflow and writing `.agents/sessions/YYYY-MM-DD.md`. Use when ending a session, preparing to run `/clear`, or asked to document what changed before context is reset.
A complete guide to creating or updating skill packages. Useful when users want to distill methods, experience, and preferences from a conversation into reusable skills, or to structurally optimize existing skills (including scripts, workflows, resource organization, and packaging for release).
Create, update, and maintain skills in the canonical .skills/internal/ directory. Includes step-by-step directives for agents to work with users, validate skill structure, and sync changes across agent directories. Use when users want to create new skills, update existing ones, or need guidance on skill authoring.
Monitors task execution for skill improvement opportunities. Use this skill during ANY multi-step task, agentic workflow, or substantive work session where Claude is using tools and producing deliverables. It captures patterns, user corrections, workflow insights, and methodology worth preserving as reusable skills. Also triggers during post-task feedback discussions and when the user explicitly mentions skill observations, improvements, the observation log, skill taxonomy, or asks Claude to watch for skill opportunities. Also known as "One Skill to Rule Them All" — trigger on this phrase too. IMPORTANT: this skill should be invoked at the start of every task-oriented session — if you are about to use tools to produce deliverables, invoke this skill first.
Prevent request timeouts in Claude Code sessions by chunking long operations, implementing progress checkpoints, using background processes, and optimizing tool usage patterns. Use when performing bulk operations, processing large datasets, running long-running commands, downloading multiple files, or executing tasks that exceed 2-minute timeout limits. Implements strategies like task decomposition, incremental processing, parallel execution, checkpoint-resume patterns, and efficient resource management to ensure reliable completion of complex workflows.
Always-loaded discipline for every AppFollow toolkit skill. Read this before calling any AppFollow MCP tool. Enforces the preview→approval→confirm write ritual, the shared MCP error-code vocabulary, and the public-tools-only posture. Other skills reference this file rather than restating the rules.
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.
