Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Route Three.js and browser game-development requests into the narrowest matching sub-skill of the 19-skill MengTo/Skills game-development family — level authoring, map editing, cameras, enemy systems and AI, monster rigs, action combat, encounter design, inventory, hybrid/Vesperfall assets, VFX, audio feedback, mobile controls, performance tuning, QA, changelogs, and release. Use when a task touches any of those for a playable web game, and fetch the matching sub-skill before doing the work instead of improvising. Route Unity/C# engine work to `unity-gamedev-skill-pack` instead. Triggers on: three.js game, browser game, webgl game, isometric arpg, enemy AI, game camera, game inventory, game VFX, game audio feedback, ship web game, playtest web game, mobile threejs game, game encounter design, monster rig, hybrid game assets.
High-velocity solo development workflow. Idea to production same-day. 10 commands: plan, spike, ship, fix, review, spec-review, focus, done, drop, workflow. Auto-activates on: “plan,” “spec,” “ship,” “spike,” “fix,” “debug,” “repair,” “spec-review,” “review spec,” “analyze spec,” “challenge spec,” “focus,” “what should i do,” “prioritize,” “overwhelmed,” “what should i work on,” “done,” “finish,” “complete,” “drop,” “abandon,” “workflow,” “what's next,” “whats next,” “next step,” “what now.”
Use for script-guided issue tracker triage when managing the current project backlog, making issues ready for Orchestrator by running workflow scripts, inspecting their outputs, and fixing labels, statuses, dependencies, body contracts, estimates, stale tracker state, and explicit human questions without ad hoc exploration.
AFA DTC full-funnel standalone store operations system — the system entry point, first-level router and workflow orchestrator that coordinates the five business lines: brand infrastructure, paid acquisition, organic growth, monetization and retention, and operations scaling. Trigger words: standalone store, DTC, e-commerce, Shopify, full-funnel operations, standalone store diagnosis, standalone store growth, 90-day growth plan, don't know where to start, multiple problems at once, help me plan the whole thing, dropshipping, product testing, test store, ecommerce, online store, DTC brand, store audit, dropshipping.
Manually write a durable page to an ai-memory instance via the MCP (memory_write_page) — for decisions, rules, gotchas, schemas/contracts, or operational constraints the user explicitly wants remembered. Use when the user says 'remember this', 'save this decision', or 'annotate X'. Not for routine notes (auto-capture handles those).
Operate Alibaba Cloud CloudMonitor Service (CMS) AgentLoop ContextStore by using aliyun CLI with api version 2024-03-30. Use when users mention CloudMonitor, CMS, AgentLoop, agent loop, context, memory, experience, ContextStore, context store, context library, memory library, experience library, or ask to create/query/update/delete stores, write/search/update/delete context records, manage store API keys, or debug aliyun cms ContextStore commands.
Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk-of-bias assessment, meta-analysis, APA 7.0 report compilation, editorial and devil's-advocate review, ethics review, and post-research literature monitoring. Use when the request mentions research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, or araştırma, literatür taraması, sistematik derleme, meta-analiz, araştırmamı yönlendir, or 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題. Part of the AlterLab Academic Skills suite.
Sign in to OpenAnt via key-based login (recommended) or email OTP. Use login --key then agents register for new agents. Use when the agent needs to log in, check auth status, get identity, or when any operation fails with "Authentication required" or "not signed in" errors. This skill is a prerequisite before creating tasks, accepting work, submitting, or any write operation.
Manages persistent work state (briefs) for local SEO engagements. Automatically load this skill when starting work on a specific business or location, when a user says "resume," "continue," "pick up where we left off," or references a specific client or location by name. Also load when any tool call is about to be made for a business, that's the trigger to create or update a brief. Load when receiving output from a scheduled task.
Hard discipline for clarifying with the user: one question per turn until shared understanding; walk the decision tree by dependencies; recommend an answer; look up facts, ask only decisions; explore context before asking; do not act until the user confirms clarity (or skips). Referenced by PDCA and perf hosts via frontmatter dependencies. Triggers — 「一次一问」「多轮问清」「澄清提问纪律」「推荐答案提问」「决策树追问」 / clarifying question discipline, decision-tree grilling.
Orchestrator drives, Codex codes. Execute an approved plan one task at a time: your agent (Claude Code, etc.) sequences the work, briefs OpenAI Codex to write ALL product code, reviews every diff, runs the test gate BEFORE each commit, commits one task per commit, and opens exactly ONE PR at the end. Use when the user says 'codex-build', 'have codex code it', 'you orchestrate, codex codes', or hands over a plan for step-by-step implementation.
Operate native macOS apps via screenshots, mouse, and keyboard. Use only when a task cannot be completed through APIs, dedicated Skills, or file tools and requires real desktop interaction. For web tasks, prefer browser tools; for file reading/writing and code execution, use the corresponding tools; do not expand the scope of screen access on your own.
Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like "what would different experts think about this", "help me think through this from all sides", "council mode", "mind council", or "deliberate on this". Also trigger when the user faces a dilemma, trade-off, or complex choice with no obvious answer.
Visualize and diagnose OpenClaw context window usage. Generates a terminal-rendered breakdown showing workspace files (status, chars, tokens), installed skills inventory, and token budget allocation across bootstrap components. Use when: (1) user asks about context window health or token usage, (2) debugging agent quality degradation ("agent got dumber"), (3) after editing workspace files to verify impact, (4) auditing bootstrap overhead. NOT for: conversation history analysis, model selection, or cost tracking.
Use this skill when authoring or extending an Unreal Engine toolset, a class of static, AI-callable functions registered with `ToolsetRegistry` and exposed through the unreal-mcp server. Trigger when the user wants to add, expose, or register a new tool method, create a new toolset, or extend an existing one such as `BlueprintTools`, `StaticMeshTools`, `ObjectTools`, `LevelTools`, or `MaterialTools`. Concrete triggers: 'add a tool to X', 'expose this via MCP', 'register a function so Claude/the agent can call it', 'wire this into the toolset registry', 'create a new toolset for Y', 'add a Python toolset', 'make this AI-callable'; adding a `static` method to a `*Tools.cpp/.h/.py` file; editing files under a `Toolsets/` folder; designing tool parameters, return types, or struct schemas for a toolset. SKIP for: invoking existing tools at runtime (use unreal-mcp instead), authoring an Agent Skill (use unreal-skill), generic refactors that happen to touch a toolset file but don't add or redesign a tool, or unrelated uses of the word 'toolset'.
Best practices for using Cursor—rules, commands, skills, subagents, ignore files, Agent security, workflows, and community resources. Use when setting up Cursor, initializing or creating the .cursor folder, writing .cursor/rules or AGENTS.md, creating commands or skills, configuring .cursorignore, working with Agent, discovering rules or MCPs (e.g. cursor.directory), making codebases cursor-compatible, or asking about Cursor workflows, TDD, git commands, or large codebases.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Identify missing skills and recommend installations from AI Cortex or public skill catalogs. Core goal - provide top 1-3 skill matches with install commands for Agent capability gaps. Use when discovering capabilities or suggesting skills to fill gaps.
Primary router skill for broad .NET work. Classify the repo by app model and cross-cutting concern first, then switch to the narrowest matching .NET skill instead of staying at a generic layer. USE FOR: general .NET requests without a narrower framework; C# implementation, debugging, review, or refactoring; routing to framework and tooling skills. DO NOT USE FOR: unrelated stacks; tasks already covered by a narrower .NET skill. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
Microsoft Entra Agent ID (preview) for creating OAuth2-capable AI agent identities via Microsoft Graph beta API. Covers Agent Identity Blueprints, BlueprintPrincipals, Agent Identities, required permissions, sponsors, and Workload Identity Federation. Includes Microsoft Entra SDK for AgentID (containerized sidecar) for polyglot agent authentication (Docker/Kubernetes), 3P agent integration, autonomous and interactive agent patterns. Triggers: "agent identity", "agent id", "Agent Identity Blueprint", "BlueprintPrincipal", "entra agent", "agent identity provisioning", "Graph agent identity", "entra sidecar", "agent id sidecar", "auth sidecar", "3P agent", "third-party agent identity", "polyglot agent auth".
Entry point for the CodeStable skill engineering loop. Triggers: writing or modifying a cs skill, evaluating skill effectiveness, quantifying across models/agents, optimizing skill prompts, or consolidating conclusions back into a skill. Internally drives author, eval, optimize, release.
Use when the user wants proof that finished work meets its acceptance criteria, not just that tests pass: 'verify it is actually done', 'check it against the spec', 'evaluate the run'. Runs mechanical checks, a semantic review per criterion, and multi-model consensus when uncertain. With execution.auto_evolve on (the default), a rejected result with a Seed continues into a Ralph fix loop; pass auto_evolve: false to verify only.
Manage DAG execution lifecycles including start, stop, pause, resume, and cleanup. Activate on 'execution lifecycle', 'stop execution', 'abort DAG', 'graceful shutdown', 'kill process'. NOT for cost estimation, DAG building, or skill selection.
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.
Multi-agent orchestrator for Claude Code. Use when user mentions gastown, gas town, gt commands, bd commands, convoys, polecats, crew, rigs, slinging work, multi-agent coordination, beads, hooks, molecules, workflows, the witness, the mayor, the refinery, the deacon, dogs, escalation, or wants to run multiple AI agents on projects simultaneously. Handles installation, workspace setup, work tracking, agent lifecycle, crash recovery, and all gt/bd CLI operations.
Fix a numbered GitHub issue end to end from an isolated clone: understand the issue, create a dedicated branch and linked draft pull request, push progress checkpoints, implement and test the fix, update the changelog, mark the pull request ready, monitor and repair CI until it passes, and merge the successful PR. Use when the user invokes `/gh-fix OWNER/REPO#ISSUENUMBER`, `/gh-fix ISSUENUMBER`, `$gh-fix OWNER/REPO#ISSUENUMBER`, or asks for this complete GitHub issue-to-merge workflow.
Delegate an implementation task to the Kiro CLI and report exactly what it changed — verified against a git baseline, never on kiro's SELF-REPORT. Gathers the context a fresh agent needs, rephrases the request into a self-contained, injection-safe prompt with XML boundary tags, injects the task's kiro convention skills (`--skill`), launches kiro via the bundled helper with trust scoped to the mode (`implement` = least-privilege write, `review` = read-only), then diffs the working tree against a pre-run baseline. Never auto-installs kiro or pipes a remote installer to a shell, never passes raw user input through the shell or defaults to `--trust-all-tools`, and reports partial completion and errors honestly rather than trusting kiro's own summary. Use only when the user explicitly asks to delegate implementation to kiro.
This skill should be used when self-hosting, installing, configuring, operating, updating, troubleshooting, or learning to use NousResearch Hermes Agent, including CLI usage, gateway and Slack setup, plugins, tools, skills, profiles, memory, MCP, cron, Kanban, delegate_task subagents, dashboards, and tmux-managed Hermes sessions.
Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues.
Structured retrospective and knowledge sediment for completed work (the Act phase of PDCA). Run a disciplined post-work review (Keep-Problem-Try / Stop-Start-Continue / After-Action Review by context), judge which lessons are worth solidifying, pick the right carrier, validate the sediment gets reused, and feed improvements back. Use whenever finished work needs reflection — as a PDCA host closeout/review stage via frontmatter dependencies, or standalone on any completed task. Triggers — 「回顾总结」「复盘」「经验沉淀」「总结经验教训」「改进闭环」「沉淀规则」「项目复盘」 / learn and improve, lessons learned, retrospective, post-mortem, debrief.
Install, find, update, and manage agent skills. Use when the user wants to add a new skill, search for skills that do something, check if skills are up to date, or update existing skills. Triggers on: install skill, add skill, get skill, find skill, search skill, update skill, check skills, list skills.
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).
Guide to creating and managing Model Context Protocol (MCP) servers. How to add custom tools. When to use: (1) when you want to add custom tools to Claude Code, (2) when integrating external services, (3) when you want to build project-specific automation tools, (4) when you want to know how to configure an MCP server. Trigger examples: "create an MCP server", "add a custom tool", "MCP config", "turn an external API into a tool", "test runner MCP".
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.