Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Enable code intelligence (go-to-definition, find-references, hover, type info) for any programming language by installing and configuring an LSP server for Copilot CLI. Detects the OS, installs the right server, and generates the JSON configuration (user-level or repo-level). Use when you need deeper code understanding and no LSP server is configured, or when the user asks to set up, install, or configure an LSP server.
Run the ECC engineering process retroactively against an existing project slice. Audit current state, reconstruct past decisions, produce a four-axis gap register (thinking, coding practice, missing features, technology selection), then execute fixes forward through the plan-TDD-review pipeline. Use when adopting ECC mid-project, taking over an inherited codebase, or asking what was missed and what would have been built differently from day one.
Route a plain-language QA situation to the next 1-2 skills, then carry out the requested workflow with evidence and a readable report. Use ONLY when the request does not match any other skill's trigger phrases. Use when: "which skill should I use," "where do I start," "validate this app," "run QA and report," "/qa-do," or any vague QA situation that doesn't map to one skill. Not for: bootstrapping a brand-new project with no QA — use qa-start. Not for: capturing project setup/context — use qa-project-context. If the request clearly matches another skill, invoke that skill directly instead of routing through here. Related: qa-start, qa-project-context, test-strategy.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
Runs an autonomous review-and-fix improvement loop over any code target — a skill, plugin, module, or directory — using a reviewer the user names: any installed skill or agent. Keeps a cross-round findings ledger, escalates when fixes stop converging, and guards scope mechanically. Use when asked to 'improve this code until review passes', 'run an improvement loop with <reviewer>', or to iterate review-and-fix with a specific reviewer. For skills prefer the skill-improver entry; for a branch prefer pr-improver.
Security vetting protocol before installing any AI agent skill. Red flag detection for credential theft, obfuscated code, exfiltration. Risk classification LOW/MEDIUM/HIGH/EXTREME. Produces structured vetting reports. Never install untrusted skills without running this first.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Byzantine fault-tolerant consensus and distributed coordination. Queen-led hierarchical swarm management with multiple consensus strategies. Use when: distributed coordination, fault-tolerant operations, multi-agent consensus, collective decision making. Skip when: single-agent tasks, simple operations, local-only work.
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Plan and execute entire application builds. Generates phased delivery roadmaps, then executes them autonomously — phase by phase, committing at milestones, deploying, testing, and continuing until done or stuck. Modes: plan (generate roadmap), start (begin executing), resume (continue from where you left off), status (show progress). Triggers: 'roadmap', 'start building', 'resume the build', 'keep going', 'build the whole thing', 'execute the roadmap', 'what phase are we on'
Claims-based authorization for agents and operations. Grant, revoke, and verify permissions for secure multi-agent coordination. Use when: permission management, access control, secure operations, authorization checks. Skip when: open access, no security requirements, single-agent local work.
Govern stable domain context with a narrowly scoped CONTEXT.md when needed, and route hard-to-reverse decisions to ECC's existing architecture-decision-records skill. Use when domain terms require repeated explanation, one term has multiple meanings, unclear language causes module-boundary mistakes, or domain semantics must remain separate from architecture decisions.
Set up the Google Workspace CLI (gws) from scratch. Guides you through creating a GCP project, setting up OAuth credentials and authentication, and installing 90+ agent skills for Claude Code. Use when the user wants to set up gws for the first time, configure Google Workspace API access, install the Google Workspace CLI, or troubleshoot gws authentication issues.
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use when generating SHA-256 integrity manifests for agent plugins or tool packages; verifying installed plugins against their published manifests; detecting tampered, modified, or untracked files in agent tool directories; auditing dependency pinning and version policies for agent components; building provenance chains for agent plugin promotion (dev → staging → production); or handling requests such as “verify plugin integrity,” “generate manifest,” “check supply chain,” or “sign this plugin.”
Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.
Manage a file-based epic → story → sprint development loop without requiring an external project management tool. Creates and tracks epics, breaks them into user stories, assigns them to sprints, and drives implementation story by story. Use when starting a new feature initiative or when the team has no Jira/Linear/Taiga but needs structured delivery tracking.
Use before claiming that work is complete, fixed, or tests have passed, and before committing or creating a PR. You must run verification commands and confirm their output before claiming success; always support claims with evidence.
Audit MCP (Model Context Protocol) server configurations for security issues. Use this skill when: - Reviewing .mcp.json files for security risks - Checking MCP server args for hardcoded secrets or shell injection patterns - Validating that MCP package-runner dependencies use exact reviewed versions, not bare names, @latest, or ranges - Detecting mutable npm/npx, bunx, pnpm dlx, yarn dlx, and npm exec references in MCP configurations - Auditing which MCP servers a project registers and whether they're on an approved list - Checking for environment variable usage vs. hardcoded credentials in MCP configs - Any request like "is my MCP config secure?", "audit my MCP servers", or "check .mcp.json" keywords: [mcp, security, audit, secrets, shell-injection, supply-chain, governance]
Interactive session to craft a system prompt for an AI agent powered by the Sanity Context MCP server. Use this skill when users want to define agent personality, set tone and voice, establish boundaries and guardrails, configure refusal behaviors, or control how their agent communicates. Trigger when users mention their agent sounds wrong, needs to refuse certain requests, has the wrong tone, or they want to set communication rules and policies
Run FIRST (before any other skill or task) when workspace root has no AGENTS.md and the repo is an AEM Cloud Service project. Creates AGENTS.md and CLAUDE.md (tailored via pom.xml). Applies on every user request until AGENTS.md exists — not only the first chat. Never overwrites existing files
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.