Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Clean Code for TypeScript — code smell catalogs (Martin's per-line heuristics, Fowler's change-shape smells, TypeScript-native type smells), a linter-first detection policy, and step-by-step review, refactoring, TDD, and estimation workflows. Use when the user: - Writes, reviews, or refactors TypeScript/JavaScript code, or asks whether code is clean, maintainable, or well-designed - Names a smell — Shotgun Surgery, Feature Envy, Data Clumps, Long Method, Divergent Change, Duplicated Code, Large Class, Long Parameter List, Primitive Obsession, Message Chains, Dead Code, Speculative Generality, god class, copy-paste, stringly typed, premature abstraction - Asks about TypeScript type hygiene — `any` leakage, `as` assertions, non-null `!`, discriminated unions, exhaustiveness, strict flags, branded types - Asks what to enforce with lint or tsconfig versus what needs judgment, or names knip, jscpd, dependency-cruiser, typescript-eslint, type-coverage - Runs TDD, fixes a bug with test coverage, or plans test strategy - Estimates tasks, negotiates a deadline, or works through a commitment or team problem
Unified codebase review — audits skill quality, code health, infrastructure alignment, and cross-consistency across a portfolio of repos. Runs deterministic metrics (ruff, coverage, complexity, TODOs, dependency staleness) and LLM-driven architectural judgment. Also handles delivery status, commit squashing, infrastructure harmonization, and boilerplate backporting. Use when user says "review skills", "audit skills", "audit codebase", "code quality", "repo status", "what needs pushing", "align repos", "backport", "upgrade deps", "assess codebase", "health check", or wants a thorough quality pass on skills and/or code.
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Embedded network debugging tool for discovering interfaces, capturing packets, analyzing pcap/pcapng, running connectivity tests, port scanning and traffic statistics. Automatically triggers when the user mentions Wireshark, tshark, Npcap, packet capture, network integration debugging, port scanning, connectivity troubleshooting, pcap analysis, network interfaces, ping tests, traceroute, traffic statistics, Modbus TCP, EtherNet/IP and other network protocol debugging; it also supports explicit invocation via /net. Even if the user only says "capture a packet and take a look", "scan the ports", "is the network up" or "analyze this pcap", this skill should trigger as long as the context contains a specific tool name (tshark, Wireshark, Npcap), a protocol name (Modbus TCP, EtherNet/IP, ICMP, etc.), a debugging action (packet capture, port scanning, connectivity test, ping, traceroute, traffic statistics, pcap analysis) or a network interface operation.
Run automatic SPICE simulations on subcircuits detected from KiCad schematic analysis — validates filter frequencies, divider ratios, opamp gains, LC resonance, and crystal load capacitance. Supports ngspice, LTspice, and Xyce (auto-detected). Generates testbenches, runs batch mode, produces structured pass/warn/fail report. Use when the user asks to simulate, verify, or validate any analog subcircuit — RC filters, LC filters, voltage dividers, opamp circuits, crystal oscillators. Also for "simulate my circuit", "run spice", "verify with simulation", "check my filter cutoff", "does this divider give the right voltage", "what's the bandwidth of this opamp stage". Consider suggesting simulation during design reviews when the schematic analyzer reports simulatable subcircuits and a SPICE simulator is available.
Use when auditing a codebase for semantic duplication — functions that do the same thing but have different names or implementations. Especially useful for LLM-generated codebases, where new functions are often created rather than reusing existing ones.
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks. Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Fuzzing skill for automated input-driven bug finding in C/C++. Use when setting up libFuzzer or AFL++ fuzz targets, defining fuzz entry points around parsing or I/O boundaries, integrating fuzzing into CI, managing corpora, or combining fuzzing with sanitizers. Activates on queries about libFuzzer, AFL, afl-fuzz, fuzz targets, corpus management, coverage-guided fuzzing, or OSS-Fuzz integration.
Run a quality, sanity and efficiency audit of Ane's MEL/SRHR system (Ann/Vi/Li/Researcher team, wiki, harness, three-repo architecture): surfaces drift, inconsistencies, hygiene issues and architectural ceilings, categorised by severity, as a Tier 1 working brief with sequencing. Does NOT auto-execute fixes. Use when Ane types /system-audit, /audit, /system-check, /system-health, or asks whether anything is broken or drifting; run before any /li curate and after agent-improvement work. Different from /test (harness only), /li lint (wiki only), and /grade-system (overall quality posture rather than specific issues).
Retune a skill corpus for a new model, measurement-first: mine the run archive for a baseline, establish a noise floor, audit the corpus adversarially, then cut in measured passes until a pre-registered bar clears. Requires a benchmark harness that can A/B two builds of the corpus; refuses without one.
Systematically identify and fix all failing tests using smart grouping strategies. Use when explicitly asks to fix tests ("fix these tests", "make tests pass"), reports test failures ("tests are failing", "test suite is broken"), or completes implementation and wants tests passing.
Apply substantive quality and reliability grading to Ane's MEL/SRHR system (Ann/Vi/Li/Researcher team + wiki + harness + three-repo architecture): letter grades on 9 components and 10 dimensions plus OECD-DAC adapted criteria, three concrete risks, what an external evaluator would still want, and recommendations for moving the grade, with a mandatory self-grading bias note. Use when Ane types /grade-system, /grade, /quality-grade, /rate-system, or asks how good the system is. Honest grading, not flattery. Different from /system-audit (finds specific issues to fix; this grades overall quality posture).
Day 5 (Friday) sprint-closing move of a Design Sprint that produces the bundled Friday artifact covering per-customer interview observations, best quotes, scorecard grid (sprint questions by customers), observed patterns, hot takes from each team member, and the Decider summary (build, iterate, pivot, or stop, plus highest-confidence learning, most important revision, and next artifact). Use Friday after Thursday's prototype passes trial run and during/after the 5 customer interviews. The sprint's payoff artifact.
Validate Claude Ads contracts, scoring inputs, run bundles, capabilities, source freshness, safety, installation, uninstall, or release readiness. Use for ads validate, ads status, ads next, maturity checks, ownership-manifest uninstall, preserving unrelated ads-* skills, checksum verification, preflight, QA, or release audits.
EMC pre-compliance risk analysis for KiCad PCB designs — 18 check categories, 44 rule IDs covering ground planes, decoupling, I/O filtering, switching harmonics, clock routing, differential pair skew, board edge radiation, PDN impedance, return paths, crosstalk, ESD protection, shielding, and magnetic leakage from switching inductors. Produces severity-ranked risk report with pre-compliance test plan. Supports FCC Part 15, CISPR 32, CISPR 25 (automotive), MIL-STD-461G. SPICE-enhanced when available. Use when the user asks about EMC, EMI, radiated/conducted emissions, FCC compliance, CE marking, CISPR, ground plane issues, decoupling strategy, clock routing EMC, switching noise, differential pair skew, or whether their board will pass EMC testing. Also for "will this pass FCC?", "check my EMC", "is my ground plane okay?", "check my decoupling", or "generate an EMC test plan".
Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'
Bring a project's formal verification back in line with the code after the code changes: find the commits that touched verified code, update the model, harness, or properties to describe the code as it is now, rerun, and say whether each failure is drift in the verification or a bug in the code. Use for /maintain-formal-verification-skill, \"is the model still true to the code\", or after merges and refactors that touched verified code without rechecking it.
Run and debug C# MCP servers locally. Covers IDE configuration, MCP Inspector testing, GitHub Copilot Agent Mode integration, logging setup, and troubleshooting. USE FOR: running MCP servers locally with dotnet run, configuring VS Code or Visual Studio for MCP debugging, testing tools with MCP Inspector, testing with GitHub Copilot Agent Mode, diagnosing tool registration issues, setting up mcp.json configuration, debugging MCP protocol messages, configuring logging for stdio and HTTP servers. DO NOT USE FOR: creating new MCP servers (use mcp-csharp-create), writing automated tests (use mcp-csharp-test), publishing or deploying to production (use mcp-csharp-publish).
Review the README or a documentation page from several real reader points of view at once. Fans out one parallel reviewer per audience — Claude Code newcomer, power user, plugin/skill author, skeptical senior engineer, non-running decision-maker — each scoring it x/5 and naming concrete, line-level fixes. Use when asked to review, critique, grade, or "get to N/5" the README or any front-door doc, or to check how a doc reads for real users. Not for code review (use /code-review for that).
Correctness lens for a PR or diff, used by build and review — bugs, security, performance, and code quality. Use when asked to review a PR or give pull request feedback, even when the diff is pasted inline. Don't use for local uncommitted changes, creating PRs, or merging.
Analyzes git diffs against a stated intent to detect scope creep, unrelated files, broad pull requests, changes that grew beyond a fix, dependency additions, public API renames, config or CI edits, oversized hunks, and formatting-only files. Use when the user asks whether a change grew beyond the fix, a PR is too broad, or what unrelated stuff they touched, and wants keep, split, or justify guidance. Operates locally and offline.
Audit application source code against the OWASP Top 10:2025 categories — broken access control (incl. SSRF), security misconfiguration, software supply chain failures, cryptographic failures, injection, insecure design, authentication failures, software and data integrity, logging and alerting failures, mishandling of exceptional conditions — with recon, a coverage ledger, per-finding verification, and a findings.json report. Use when the user mentions 'OWASP,' 'OWASP Top 10,' 'security audit,' 'security review,' 'secure code review,' 'code security review,' 'vulnerability audit,' 'find vulnerabilities,' 'appsec review,' 'application security audit,' 'check for security issues,' 'review this PR for security,' 'pre-merge security check,' 'diff review,' 'broken access control,' 'IDOR,' 'SQL injection,' 'XSS,' 'SSRF,' 'webhook security,' or wants to check their codebase for common security weaknesses
Three-part workflow to improve overall codebase health with jscpd — find and fix duplicated code, find and remove or refactor dead code, then find and simplify the largest/most complex files. Starts from --dashboard/--health to prioritize and ends by re-measuring the score.
Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation.
Expert GDScript landmine guidance: static typing opcodes, signal-up/call-down, %UniqueName/@onready lifecycle, Callable bind/unbind, await sequences, typed collections, and safe Dictionary iteration. Use for code review, refactoring hot paths, or project standards. Trigger keywords: static_typing, signal_architecture, unique_nodes, @onready, class_name, signal_up_call_down, Callable.bind, typed_collections, await_sequence
Author and maintain versioned Playwright (@playwright/test) TypeScript UI specs for browser user flows. Use when asked to create, run, debug, or refactor E2E tests, form/navigation/auth flows, responsive checks, UI mocking, fixtures, Page Objects, or visual comparisons. Use api-testing for standalone REST/GraphQL contracts and playwright-cli for live browser sessions. Keywords: E2E spec, Playwright test, POM, fixtures, UI regression
Use this skill for code reviews with the Codex CLI from a Claude-hosted session. Activates on mentions of codex review, code review with codex, codex check, gpt review, codex exec review, run codex, review my code, review this PR, review changes, peer review, or second opinion.
Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI
Review or audit a design/UI across 6 weighted dimensions with Nielsen's 10 heuristics and a prioritized findings table. Use when the user wants a design critique, quality score, heuristic evaluation, or audit of an existing screen, page, or product before/after build.
Runs a Greptile CLI review for the current local branch, installing or authenticating the CLI when needed, then summarizes JSON findings for the user. Use when the user wants Greptile feedback before opening a PR, outside a hosted PR review flow, or directly from a local checkout.
Run a repo-wide cross-cutting governance audit via the pm-skill-auditor sub-agent. Aggregates the enforcing validator suite, re-derives aggregate counters, and surfaces cross-cutting issues no single validator catches, graded P0/P1/P2/P3 with a machine-readable status. Use for pre-release readiness checks or a periodic repo health audit.
Triage Antithesis test reports to understand what happened in a run: look up runs, check status, investigate failed properties (assertions), view metadata, download logs, search events across all timelines, inspect findings, and examine environmental details. Load after a run completes or when investigating a failure.
Evaluate a skill against the Legal Skill Design Framework — thirteen design parameters (including trust-surface, freshness, schema validation, and conflict detection), three legal failure modes, and a three-band verdict (Ready / Some Concern / Material Concerns). Use when deciding whether to trust a community skill before installing it, before deploying a first-party skill to your team, or whenever the user asks "should I trust this?" or "is this skill well-designed?". Runs automatically as part of /legal-builder-hub:skill-installer.
Review orchestrator and single entry point for all application reviews. Triage what needs reviewing, then run the right domains: security (OWASP, auth, headers, supply chain), privacy (PII, tracking, consent, GDPR/CCPA), compliance (HIPAA, SOC 2, PCI-DSS, GDPR), design (responsive, accessibility, typography, i18n), resilience (error handling, degradation, API contracts), performance (Core Web Vitals, bundles, runtime), SEO (meta, structured data, crawlability), and GEO (AI citation readiness, llms.txt, entity clarity). Use for pre-launch readiness, post-incident planning, or any 'review my app for X' request
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.