Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Validates .env files against code references and manifests for missing vars, type mismatches, insecure defaults, and unused entries. Triggers on: "validate env file", "check environment variables", "missing env vars", "check .env", "dotenv validation". NOT for secret scanning, use repo-sentinel.
Safely diagnose and fix ESLint errors, warnings, and configuration compatibility issues. Use when the user asks to run lint, explain ESLint errors, perform scoped auto-fixes, or migrate ESLint configuration; prefer the project's locked package manager and versions, pre-check before modifying, and do not automatically download the latest version or bulk-rewrite unrelated source code.
Lints, tests, simulates, and formats Fastly VCL code using the falco tool. Also serves as the authoritative VCL reference via the falco Go source, which implements Fastly's full VCL dialect. Use when validating VCL syntax, running VCL linting, testing VCL locally, simulating VCL request handling, formatting VCL files, writing VCL unit tests with assertions, debugging VCL logic errors, looking up VCL function signatures or variable scopes, understanding VCL subroutine behavior, or running `falco lint`/`falco simulate`/`falco test`/`falco fmt`. Also applies when working with VCL syntax errors, type mismatches in VCL, choosing which VCL subroutine to use, or setting up a local VCL development and testing environment.
Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-driven planning to clarify data models, authentication method, pagination strategy, and caching before writing any code.
Generate 3GPP-compliant 5G NR downlink and uplink baseband waveforms. Use to create NR signals, test model (TM) waveforms, fixed reference channels (FRC), test and measurement (T&M) signals, or test vectors for conformance testing. Covers configuring data, control, and broadcast channels and signals: PDSCH, PUSCH, PDCCH, PUCCH, SRS, SSBurst, CSI-RS, DM-RS, PT-RS, CORESET, and BWP parameters including bandwidth, subcarrier spacing (SCS), modulation (QPSK, QAM), numerology, FR1, FR2, TDD, FDD, and multi-bandwidth-part setups. Use for signal generation, RF instrument playback, or IQ baseband synthesis. Requires 5G Toolbox.
Entry point for everything MegaLinter. Use when the user wants to lint their repository, set up MegaLinter, check or fix lint errors, make CI lint jobs pass, or says "run megalinter", "fix lint errors", "make the linters happy", "clean up code quality". Detects the repository state and orchestrates the megalinter-setup, megalinter-check and megalinter-fix skills in a loop until the repository is clean.
Collect MegaLinter lint errors for the current repository. Use when the user wants to know if the code passes linting, why the MegaLinter CI job fails, or before/after fixing lint errors. Two modes - watch a CI job (GitHub Actions, GitLab CI, Azure Pipelines, Bitbucket Pipelines) and parse its logs, or run MegaLinter locally with Docker (full run or fast parallel standalone linter runs).
Install or upgrade MegaLinter on a repository. Use when the user wants to add MegaLinter to a project, set up linting CI, update MegaLinter configuration or version, or says "install megalinter", "setup linting", "add code quality checks", "update megalinter skills". Always goes through npx mega-linter-runner (--install or --upgrade), then refines .mega-linter.yml. In upgrade mode it also refreshes the installed MegaLinter skills and sub-agents. Also sets up a MegaLinter custom flavor repository when the user explicitly asks for one.
Full Prism: multi-pass structural analysis with mandatory adversarial self-correction. Designs custom analytical passes, executes them with chaining, then attacks its own findings before synthesizing. Use for maximum depth on important code or artifacts.
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally
Unvarnished technical criticism combining Linus Torvalds' precision, Gordon Ramsay's standards, and James Bach's BS-detection. Use when code/tests need harsh reality checks, certification schemes smell fishy, or technical decisions lack rigor. No sugar-coating, just surgical truth about what's broken and why.
Cross-browser, cross-platform, and cross-device compatibility testing ensuring a consistent experience across environments. Use when validating browser support, testing responsive design, or ensuring platform compatibility.
Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.
Internationalization (i18n) and localization (l10n) testing for global products including translations, locale formats, RTL languages, and cultural appropriateness. Use when launching in new markets or building multi-language products.
Apply Edward de Bono's Six Thinking Hats methodology to software testing for comprehensive quality analysis. Use when designing test strategies, conducting test retrospectives, analyzing test failures, evaluating testing approaches, or facilitating testing discussions. Each hat provides a distinct testing perspective: facts (White), risks (Black), benefits (Yellow), creativity (Green), emotions (Red), and process (Blue).
End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to reproduce, implement, replicate, or re-run a paper from scratch, or pastes an arxiv URL with reproduction intent.
Internal accessibility review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks the changed user-facing surface for accessibility: semantics, keyboard, focus, contrast, and ARIA correctness — applies only to user-facing surfaces. Findings only; never edits code.
Internal brand & copy review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks changed user-facing copy against the project's brand doc: voice, terminology, and honesty of claims — applies only to surfaces with user-facing text. Findings only; never edits code.
Internal correctness + simplification review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks correctness, error handling, duplication, dead code, and simplification opportunities against the project's own conventions. Findings only; never edits code.
Internal UI/UX design review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks the changed UI against the project's design doc: consistency, states, responsiveness, and reuse — applies only when the project has a UI and the change touches it. Findings only; never edits code.
Internal performance review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks the changed paths for algorithmic and resource regressions: N+1s, hot-path allocations, asset weight, and leaks. Findings only; never edits code.
Internal security review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks secrets, input validation, injection, authn/authz, PII exposure, and dependency risk on the changed surface. Findings only; never edits code.
Internal SEO review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks changed web pages/routes for indexability, metadata, and structured data — applies only to public web surfaces. Findings only; never edits code.
Call tRPC procedures directly from server code using t.createCallerFactory() and router.createCaller(context) for integration testing, internal server logic, and custom API endpoints. Catch TRPCError and extract HTTP status with getHTTPStatusCodeFromError(). Error handling via onError option.
Sets up test coverage reporting in a repository and configures upload to Codacy. Detects testing frameworks, CI/CD pipelines, and coverage gaps, then adds the missing pieces to generate and upload coverage reports. Use whenever the user wants to set up coverage, add coverage reporting, integrate coverage with Codacy, fix missing coverage uploads, troubleshoot coverage not showing up, or configure CI to send coverage data. Also trigger when the user mentions test coverage, code coverage, coverage reports, or wants to know why Codacy shows no coverage for their repo.
Run the standard post-change validation flow after a fix, refactor, or new feature. Use when implementation work is done and you should validate the latest changes by invoking the repo's review skills, starting with review-changes and then repo-doc-maintainer, before giving the final close-out.
Write and improve agent skills (SKILL.md files). Use when creating new skills, refactoring existing ones, debugging why an agent ignores instructions, or improving compliance. Covers prompt structure, TDD for skills, description optimization, and rationalization bulletproofing.
Audit an existing VictoriaMetrics vmanomaly configuration against runtime capabilities and real data. Use when reviewing model-data fit, scheduler cadence, persisted-state compatibility, cold start, excessive detections, missing anomalies, output cardinality, or upgrade readiness. Trigger on vmanomaly config review, false-positive investigation, detection quality, model effectiveness, compatibility, and "is my anomaly detection working?" Use vmanomaly-config instead when building a new configuration from scratch.
Complete CI/CD guide for Cloudflare Workers using GitHub Actions and GitLab CI. Use for automated testing, deployment pipelines, preview environments, secrets management, or troubleshooting deployment failures, workflow errors, and environment configuration issues
Retrieve unresolved PR review comments and general conversation comments (including actionable notes from the PR author), explain each issue and propose fixes interactively, then spawn a coordinated developer team to implement approved fixes. After committing, respond to each comment on GitHub with resolution details.
Production-ready scripts for Android app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and emulator lifecycle management. Optimized for AI agents with minimal token output. Android equivalent of ios-simulator-skill.
Use when the user wants to prepare, create, or generate an AWS FIS (Fault Injection Service) experiment configuration. Triggers on "prepare FIS experiment", "create FIS experiment for [scenario]", "generate chaos experiment config", "准备 FIS 实验", "生成 [scenario] 混沌实验配置", "create experiment template for AZ power interruption", "set up fault injection test". Covers Scenario Library pre-built scenarios (AZ Power Interruption, AZ Application Slowdown, Cross-AZ Traffic Slowdown, Cross-Region Connectivity), custom single FIS actions (aws:rds:failover-db-cluster, aws:ec2:stop-instances, etc.), and SSM Automation-based fault injection for Amazon MSK (broker reboot) and ElastiCache Redis/Valkey (primary node reboot, replication group failover).
write, review, and improve bruno api tests, assertions, pre-request scripts, post-response scripts, request chaining, schema checks, response visualizers, and edge-case coverage. use when working with bruno collections, opencollection yaml, .bru files, bruno runtime scripts, chai expect tests, request variables, environment variables, response validation, or api regression testing.
Use when the user asks to "create a metric", "write a metric", "design a metric", "build a metric for", "evaluate agent performance", "measure call quality", "track a KPI", "add a workflow metric", "improve my metric", "fix a metric", "debug metric results", "set up quality scoring", or "what metrics do I need". Also relevant when discussing LLM judge prompts, custom code metrics, evaluation triggers, VALID_SKIP patterns, section extraction, or metric best practices for Cekura voice AI agents. Covers both creating new metrics and reviewing, iterating on, or troubleshooting existing ones.
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.