Skills
99,951 packaged capabilities and playbooks for agents. Install any of them with one command.
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Pre-commit hooks framework for multi-language code quality automation. USE WHEN setting up pre-commit OR configuring git hooks OR adding linting OR code formatting OR security scanning OR Terraform validation OR Kubernetes manifests OR Helm charts OR Python linting OR JavaScript formatting. Manages .pre-commit-config.yaml, hook installation, and CI integration.
Pre-commit hooks framework for multi-language code quality automation. USE WHEN setting up pre-commit OR configuring git hooks OR adding linting OR code formatting OR security scanning OR Terraform validation OR Kubernetes manifests OR Helm charts OR Python linting OR JavaScript formatting. Manages .pre-commit-config.yaml, hook installation, and CI integration.
When the user wants to optimize manufacturing processes, improve throughput, reduce cycle times, or simulate process performance. Also use when the user mentions "process improvement," "bottleneck analysis," "simulation," "discrete-event simulation," "throughput optimization," "cycle time reduction," "process efficiency," "queuing theory," "process mapping," or "capacity analysis." For lean methods, see lean-manufacturing. For scheduling, see production-scheduling.
Push to GitHub main and babysit the change until it is verifiably live in production, fixing CI and deploy failures along the way. Use when the user says "push", "push to github", "push to prod", "ship it", or "deploy". Differentiator: pushing requires the user''s explicit go-ahead; this is the procedure for AFTER they give it (this repo''s CI gate + Vercel promotion).
Deploy RAG systems to production with proper architecture, monitoring, and operations. Use this skill when deploying RAG to production, scaling retrieval systems, implementing RAG monitoring, or building enterprise RAG infrastructure. Activate when: production RAG, RAG deployment, enterprise RAG, RAG architecture, RAG monitoring, RAG scaling, RAG infrastructure, RAG ops.
Query and interact with Prometheus HTTP API for monitoring data. Use when Claude needs to query Prometheus metrics, execute PromQL queries, retrieve targets/alerts/rules status, access metadata about series/labels, manage TSDB operations, or troubleshoot monitoring infrastructure. Supports instant queries, range queries, metadata endpoints, admin APIs, and alerting information.
Create, configure, and deploy PubNub Functions 2.0 event handlers, triggers, and serverless endpoints. Covers Before/After Publish, On Request, On Interval; built-in modules (kvstore, xhr, vault, pubnub, crypto, jwt, ugc, jsonpath, advanced_math, codec/*); chaining (depth caps, Chaining vs Forking, kvstore state sharing); runtime quirks (independent per-module execution budgets, cold start, request.path normalization, vault availability, sendFile message); DB-trigger patterns; and bundling/TypeScript workflow (esbuild externals, bundle size guard, __require shim stripping, default-export shape). Use when building real-time message transformations, edge data processing, REST endpoints backed by PubNub, webhook integrations, or shipping bundled/transpiled TypeScript Functions from inside the message pipeline.
AI image generation and processing workflows. Generate images from prompts, with text-to-image, image-to-image, batch generation, image hosting management, long-image merging, and PPT packaging. The core feature is confirming each generation one by one to avoid wasting API quota.
Executes council queries by running the query pipeline across selected AI providers (Gemini, OpenAI, Grok, Perplexity), displaying formatted responses verbatim, and generating a synthesis of consensus, divergence, and recommendations. Invoked by the ask command during standard (non-agent) council queries.
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.
Apply Rails security and multi-tenant safety practices including scoped queries, SSRF defenses, rate limiting, and tenant-scoped realtime updates. Use when implementing auth, webhooks, tenant boundaries, or security-sensitive endpoints.
Analyzes Rails applications and generates comprehensive upgrade reports with breaking changes, deprecations, and step-by-step migration guides for Rails 2.3 through 8.1. Use when upgrading Rails applications, planning multi-hop upgrades, querying version-specific changes, or answering what stands between the app and the next Rails version. Based on FastRuby.io methodology and "The Complete Guide to Upgrade Rails" ebook.
React hooks best practices for React application code and custom hook design across DX, optimization, and logic. Use when writing components with hooks, creating or refactoring custom hooks, reviewing hook usage/APIs/internals, checking effects/dependencies/state/refs/callbacks, or improving hook correctness, ergonomics, and performance.
Recognize defensive deception during an engagement — honeypots, honeytokens and canary tokens, decoy AD accounts and shares, canary files, and deceptive cloud credentials — before interacting with them, and handle a suspected decoy without burning the engagement. Use when a target is unexpectedly easy, when credentials or a service appear in an implausible place, when a privileged account has no logon history, when a file or bucket looks like bait, or when deciding whether to use credentials of unknown provenance.
Approval-gated, repo-aware requirements/spec authoring for new recursive-mode runs. Use when the user wants help creating a plan, spec, scope, or requirements for work that does not yet have a run, especially prompts like "create a plan", "help me plan", "create a spec", "write requirements", or similar derivatives.
Load PROACTIVELY when task involves improving code structure without changing behavior. Use when user says "refactor this", "clean up this code", "remove dead code", "reduce duplication", "reorganize the files", or "extract this into a function". Covers extract function/component, rename symbol, simplify conditionals, improve type safety, dependency inversion, dead code removal, circular dependency resolution, and database query refactoring with automated safety validation (typecheck, tests) at each step.
Write or revise a Related Work section from a source-grounded literature map, annotated bibliography, paper list, or draft. Use this skill whenever the user asks to write related work inside an original research paper, position a paper against prior work, organize citations into themes, explain how their work differs from baselines or prior methods, avoid citation laundry lists, or calibrate novelty claims. This skill assumes the user has sources or a literature map; if they need to discover and verify the literature first, use literature-triangulation before this skill. If the user wants to write a standalone survey, tutorial, review, or perspective paper whose main contribution is the synthesis itself, use survey-paper-writer instead.
Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.
When the user wants to optimize retail allocation, distribute inventory to stores, or perform initial merchandise allocation. Also use when the user mentions "store allocation," "assortment planning," "size curve," "cluster allocation," "pre-pack," "store grading," or "initial allocation." For ongoing replenishment, see retail-replenishment. For markdown decisions, see markdown-optimization.
Read-only R code review protocol for `.R` scripts. Checks code quality, reproducibility, domain correctness, tidyverse idioms, and professional standards; produces a report without editing. Use when user says "review this R script", "check the R code", "audit the analysis code", "code review on the R", or when an R file is touched as part of a paper submission. NOT for running the code — pair with `/audit-reproducibility` for numeric verification.
Reads RHDH Jira and owns the mechanics every other RHDH Jira skill needs: `acli` flags and their traps, JQL for RHIDP, RHDHPLAN, RHDHBUGS and RHDHSUPP, custom field IDs, GraphQL and REST fallbacks, board and sprint IDs, the component catalog, and the workflow states with their exit criteria. Use to look up an issue such as RHIDP-1234, to write or debug a JQL query, to answer "which field is Story Points", "why is my search returning 30 rows", or "what does Release Pending require". Reading and query mechanics only — deciding what to file, judging readiness, or changing an issue belongs to the Jira skill that owns that verb.
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.