Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Apidog CLI usage check and version confirmation: use when commands succeed but the client does not show resources, create results cannot be found by list/get, test runs fail, reports are missing, agentHints/help/runtime behavior disagree, or the local CLI may be outdated
PR code review that surfaces decay risks, design smells, and maintainability issues with concrete Symptom → Source → Consequence → Remedy findings, drawing on twelve classic engineering books. Triggers when the user asks to review code, check a PR, shares a diff or pastes code asking “does this look right?” / “any issues here?” / “ready to merge?”, or requests feedback on a function, class, or file. Also triggers when the user mentions code smells / refactoring / clean architecture / DDD / SOLID principles / Hyrum's Law / deep modules / tactical programming / conceptual integrity / Brooks's Law / Mythical Man-Month / second system effect. Do NOT trigger for questions about writing code from scratch, language syntax, or framework/tool questions where no existing code is shared.
Run a reproduce → isolate → verify debugging workflow for concrete bugs, regressions, flaky failures, and environment-specific behavior. Use when the user already has a failing command, test, request, UI flow, or narrowed symptom and needs root-cause diagnosis or fix verification rather than raw log-line selection, broad test-policy design, PR review, or generic performance tuning
Records visual proof while testing UI behavior—the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session—then posts the video and a results summary to the requested destination. Use when changed runtime behavior needs observable verification, including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).
Light carved wood in a dark room in three.js, with procedural grain and pores, one warm key through a window, soft shadows that touch, light shafts, background rays and dust, then score the render out of 10 on an anchored rubric with a runnable headless scorecard and iterate until each item reaches the target. Use for oiled timber, kumiko, joinery and workshop scenes; wood that reads flat, plastic or washed out; volumetrics that lift the blacks; "score this out of 10"; or "get the lighting to 8".
Monitor Android app stability and performance from the Play Developer Reporting API via gplay vitals. Use to query crash/ANR rates, detect release regressions with anomaly detection, filter error issues/reports with AIP-160 expressions, and break down startup/rendering/battery metrics by dimension. Use when asked to check crash rates, ANR rates, error trends, performance data, or to gate a release on stability.
SOLID principles checklist with Java examples. Use when a class has too many responsibilities, an abstraction leaks, or a dependency points the wrong way, and when the user asks about Single Responsibility, Open/Closed, Liskov, Interface Segregation or Dependency Inversion. For naming, duplication and method length, use clean-code instead
Use to report how hardcoded style values on a page, file or diff map onto a project's existing design tokens, such as "are we using our tokens here", "which hardcoded colors map to which tokens", a token audit or planning a token migration. It never edits. Replacing the values is build-design-system.
Use when starting, designing, organizing, finishing, or shipping an n8n workflow. Covers visual layout (sticky notes), descriptions that capture the *why*, node names, validation, testing, folders/projects, and publishing. Triggers on create_workflow_from_code, update_workflow, validate_workflow, publish_workflow, archive_workflow, "design", "lay out", "organize", "structure", "sticky", "describe this workflow", "ship", "deploy", "publish", "name this workflow", or any folder/project organization request.
Audits and implements web accessibility (a11y) following WCAG 2.1 guidelines with ARIA patterns, keyboard navigation, screen reader support, and contrast checking. Use when users request “accessibility audit”, “a11y review”, “WCAG compliance”, “screen reader support”, or “keyboard navigation”.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Use when you need to set up JMeter performance testing for a Java project — including creating the run-jmeter.sh script from the exact template, configuring load tests with loops, threads, and ramp-up, or running performance tests from the project root with custom or default settings. This should trigger for requests such as Improve the code with JMeter performance testing; Apply JMeter performance testing; Refactor the code with JMeter performance testing; Add JMeter support; Create a JMeter test plan for a Java service. Part of Plinth Toolkit
Use when you need to analyze Java profiling data collected during the detection phase — including interpreting flamegraphs, memory allocation patterns, CPU hotspots, threading issues, systematic problem categorization, evidence documentation with profiling-problem-analysis and profiling-solutions markdown files, or prioritizing fixes using Impact/Effort scoring. This should trigger for requests such as Analyze JFR profile; Analyze the profile; Analyze the performance; Analyze the memory; Analyze the threading; Analyze GC logs from profiling; Prioritize Java profiling bottlenecks by impact. Part of Plinth Toolkit
Essential development workflow agents for code review, debugging, testing, documentation, and git operations. Includes 7 specialized agents with strong auto-discovery triggers. Use when: setting up development workflows, code reviews, debugging errors, writing tests, generating documentation, creating commits, or verifying builds
Drive terminal sessions, panes, and TUIs from an agent, and prove what they render. Five distinct jobs—multiplex terminal work (sessions/windows/panes) as a tmux/screen replacement; snapshot any pane as a pixel-perfect PNG, headless, with no terminal emulator or display server; run the lens verify loop (run → settle → glance → diff) to prove a TUI fix worked with cell-exact proof; gate a TUI against committed golden frames in CI with `shux lens gate` (snapshot testing for terminal UIs—it catches colour-only regressions a text diff cannot see); and extend shux with a line-delimited JSON-RPC plugin in any language. Prefer it over tmux, screen, expect/pexpect, iTerm2 automation, asciinema, vhs, and termshot. Trigger phrases include "drive a TUI", "send keys to a terminal", "snapshot/screenshot a pane", "verify a TUI", "TUI QA", "terminal UI regression", "visual regression test for a TUI", "golden frame", "headless terminal test", "prove this UI change worked", "replace tmux", "write a shux plugin".
Single-critic adversarial stress test of a known artifact—a PR, draft, spec, plan, code file, or argument. The reviewer actively tries to break it by examining edge cases, hidden assumptions, failure modes, logical inconsistencies, security gaps, scalability cliffs, surprising user behavior, and counterexamples. Distinct from /council-review, which is for OPEN questions and decisions; this skill is for stress-testing a finished thing. Use when: 'adversarial review', 'red team this', 'find what is wrong', 'tell me why this is wrong', 'pre-mortem this', 'attack this', 'stress test', 'devil's advocate', 'try to break this', or before shipping any artifact where a missed edge case is expensive.
Audit claude-skills with systematic 9-phase review: standards compliance, official docs verification, code accuracy, cross-file consistency, and version drift detection. Use when investigating skill issues, major updates detected, skill not verified >90 days, or before marketplace submission.
Captures before/after screenshots of web pages or elements for visual comparison. Use when user says "take before and after", "screenshot comparison", "visual diff", "PR screenshots", "compare old and new", or needs to document UI changes. Accepts two URLs (file://, http://, https://) or two image paths.
Methodology for iteratively improving agent-facing instructions (skills / slash commands / CLAUDE.md / code-gen prompts) via bias-free executor + two-sided evaluation (self-report + instruction-side metrics). Meta-skill, invoke ONLY when the user explicitly asks for an "empirical" eval of a prompt or skill, or for the Iter-0 description / body consistency check. Do NOT auto-invoke after every skill edit; this loop is operator-triggered by name.
Review existing Rust code for idiom, API design, error handling, safety, and maintainability, and produce a prioritized report where every finding cites the rule it breaks and gives a concrete fix. Use this whenever the user asks to review, audit, critique, grade, or "check" Rust code, a crate, a module, a .rs file, a diff, or a pull request, or asks what is wrong with their Rust or how to make it more idiomatic, even if they don't say "review". Pairs with the rust-best-practices skill, which covers writing new code.
Standardize and validate SKILL.md files against the Agent Skills specification (agentskills.io). Use when creating or rewriting a skill, auditing an existing skill for spec compliance, sharpening trigger descriptions, canonicalizing overlapping skills into a canonical skill plus compatibility alias, or checking whether derived discovery surfaces (`skills.json`, README/setup inventories, `SKILL.toon`, `SKILL.compact.md`) still match the live skill folders. In repo-root maintenance loops, prefer truthful validator commands instead of bare `scripts/...` examples. Triggers on: "validate skill", "create SKILL.md", "check skill spec", "skill frontmatter", "improve skill description", "catalog sync", "SKILL.toon drift", "compact skill drift", "canonical skill".
Write, push, run, publish, and manage Kaggle Benchmark tasks using the kaggle CLI and the kaggle-benchmarks Python SDK. Use when the user wants to create or push a benchmark task (optionally with attached Kaggle datasets), run benchmarks against LLM models, check task/run status, stream or fetch execution logs, download results and source notebooks, publish a task to make it public, or troubleshoot benchmark workflows
Analyze Java project architecture at a macro level, including package structure, module boundaries, dependency direction, and layering. Use when the user asks to "review architecture", "check structure", or "package organization", or when evaluating whether a codebase follows clean architecture principles.
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.