Skills
97,905 packaged capabilities and playbooks for agents. Install any of them with one command.
Development
Run and interpret the OnionCry Rust CLI for architecture-boundary checks in JavaScript and TypeScript repositories. Use when the user asks to check architecture boundaries, generate or review .onioncryrc.jsonc, run onioncry check or explain, adopt a violation baseline, inspect llm-mode reports, render boundary graphs, add repo/package scripts, or automate OnionCry in agent workflows. Do NOT use for generic Rust implementation work.
Use ownership and borrowing instead of reaching for clone, Rc, RefCell, or Arc<Mutex<_>> to silence the borrow checker. Every clone must be explainable. Use when code clones to make an error go away, when a borrow-checker fight is being resolved by copying data, when reviewing Rust dense with .clone() or Rc<RefCell<_>>, or when the user asks whether a clone is necessary.
Optimize software performance through user-journey baselines, profiling, representative benchmarks, controlled experiments, and field verification. Use when asked to make an application, service, workflow, or hot path faster or more efficient, or to build the measurement loop first. Do not use for a reliability incident, capacity forecast, generic product A/B test, or operating a named telemetry or load-testing tool without an optimization objective.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Diagnoses 3D PhysX collision and trigger problems. Use when OnCollisionEnter or OnTriggerEnter doesn't fire, objects pass through each other, raycasts miss, or a MeshCollider misbehaves. Also answers 2D and DOTS physics best-effort if no dedicated skill is available.
Write the release notes for a Playwright release - the Node.js notes in docs/src/release-notes-js.md together with the Python, Java and .NET translations, and the GitHub markdown for each language's release. Use when asked to write, translate or publish release notes
Generate real instrumentation code from the tracking plan and instrumentation guide. Produces typed SDK wrapper functions, identity management, and integration guidance. Outputs files in a tracking/ directory. Use when the user wants to generate or regenerate tracking code, implement the delta plan, turn the instrumentation guide into working code, 'implement tracking,' 'generate code,' 'create tracking module,' or 'build analytics SDK wrapper.'
Update the tracking plan when a feature ships, changes, or is removed. Assesses whether new events are needed, extends existing events with properties where possible, and produces a versioned mini-delta with changelog entry. Updates .telemetry/tracking-plan.yaml, delta.md, and changelog.md. Use when the user ships a new feature, modifies existing functionality, wants to keep tracking coherent as the product evolves, "feature shipped," "new feature tracking," "update tracking for [feature]," "what tracking does this feature need," or "instrument new feature."
Build a structured product model by scanning the codebase and talking to the user. Produces .telemetry/product.md — a description of what the product does, who uses it, how value flows, and what entities exist. Use when starting telemetry work on a new codebase, when the user asks to 'model this product,' 'understand this product,' 'what does this product do,' 'map the product,' 'product model,' or when no .telemetry/product.md exists yet. This is the entry point for the telemetry lifecycle.
Use when adding service-wide Spring Boot 3 Actuator, Micrometer metrics, distributed tracing, OpenTelemetry export, structured logging, health probes, dashboards, or alerts. Combine with ai-observability only for Spring AI-specific token, prompt, and model telemetry.
Wave-based comprehensive project documentation generator with dynamic task decomposition. Analyzes project structure and generates appropriate documentation tasks, computes optimal execution waves via topological sort, produces complete documentation suite including architecture, methods, theory, features, usage, and design philosophy.
Deploys the complete SDD architecture with engram persistence and ai-context/ memory layer in the current project. Also runs an opt-in interactive wizard mode (/project-setup --interactive) that bootstraps only a project CLAUDE.md with stop-between-question confirmation. Trigger: /project-setup, /project-setup --interactive, initialize new project, setup SDD, configure claude project, bootstrap claude, init project claude, create project CLAUDE.md.
Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Use when adapting Gymnasium/PettingZoo environments to published PufferLib 3.0.0 or working with the redesigned native 4.0 source line.
Use when a Python change is about to be called done (before commit or PR) and whenever asked to review, audit, sanity-check, or clean up Python code or judge whether it meets standards. Also use at decision points the defaults cover: choosing retry/timeout/idempotency policy, config loading, pydantic placement, module boundaries, or a concurrency model.
Python project scaffolding and development with modern tooling. Use when creating new Python projects, setting up virtual environments, configuring dependencies, or working with Flask web applications. Triggers on mentions of Python setup, uv, Flask, pytest, or project initialization.
React Three Fiber (R3F) and Poimandres ecosystem best practices. Use when writing, reviewing, or optimizing R3F code. Triggers on tasks involving @react-three/fiber, @react-three/drei, zustand, @react-three/postprocessing, @react-three/rapier, or leva.
React performance optimization guidelines from Vercel Engineering. This skill should be used proactively when writing, reviewing, or refactoring React code to ensure optimal performance patterns. Triggers on tasks involving React components, bundle optimization, or performance improvements.
Migrate from @trpc/react-query (classic) to @trpc/tanstack-react-query. Run npx @trpc/upgrade CLI for automated codemod. Manually migrate remaining patterns: hook-based to options-factory, utils.invalidate to queryClient.invalidateQueries with queryFilter, provider changes.
Decide whether an existing site should be improved in place, redesigned, partially rebuilt, or fully rebuilt after an evidence-backed audit. Use when the project needs a governed improve/redesign/rebuild recommendation balancing UX, SEO/LLM, migration risk, technical debt, timeline, and implementation cost.
Find, rank, and safely remove accidental codebase complexity by proving real consumers, dynamic entrypoints, compatibility obligations, duplicate representations, speculative surfaces, and lifecycle ownership. Use when asked to simplify or clean up a repository, reclaim code entropy, reduce over-engineering or redundancy, find deletion candidates, collapse duplicate state/APIs, remove dead or added-then-abandoned code beyond static-tool output, or implement an evidence-backed simplification pass in any language or stack. Also trigger for 代码化简、熵回收、删代码、清理冗余、收敛抽象、去除过度设计. Do not use as a performance audit unless simplification is the stated goal.
Config-first setup wizard for a remindb MCP server — run it as `/remindb-setup` (interactive), `/remindb-setup automode` (hands-off), or `/remindb-setup only-config` (bridge hosts: author `.remindb/` only, no env wiring). Two passes. First-time (no server attached yet): detect the host, author the `.remindb/` config (ignore/pinned/temperatures/config.json) BEFORE compiling, compile the source, seed adjacent context, install the MCP plugin, then wire the MCP env. Verify (server attached): MemoryStats + `remindb://doctor`, reconfigure, and reseed onto existing nodes. Use on first-time workspace memory setup, when remindb tools are missing/misconfigured, on "no results"/wrong-workspace symptoms, to reconfigure an existing brain, or for bridge hosts that own env wiring externally (e.g. Hermes Agent).
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.