Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Provides comprehensive guidance for Detox mobile testing framework including React Native testing, E2E testing, and test synchronization. Use when the user asks about Detox, needs to test React Native applications, write E2E tests for mobile apps, or configure Detox.
Design faster, clearer Django test data and test structure with factories, setUpTestData, SimpleTestCase/TestCase choices, fixture-file cleanup, query optimization, and unit-vs-integration boundaries. Use when Django tests create too much data, rely on slow fixtures, overuse TransactionTestCase, duplicate setup, or need refactoring for speed without losing coverage.
Create Behavior-Driven Development (BDD) scenarios - Layer 4 of the SDD flow, translating EARS requirements into executable Given-When-Then acceptance scenarios with req-to-SPEC trace links. Use after EARS, before ADR. Single-document authoring primitive; for end-to-end or batch generation the autopilot (`doc-bdd-autopilot`) drives this skill.
This skill should be used when setting up code quality tooling with ESLint v9 flat config, Prettier formatting, Husky git hooks, lint-staged pre-commit checks, and GitHub Actions CI lint workflow. Apply when initializing linting, adding code formatting, configuring pre-commit hooks, setting up quality gates, or establishing lint CI checks for Next.js or React projects.
Comprehensive Flutter development guidelines covering architecture, state management, performance optimization, testing, and cross-platform best practices. Use when building, refactoring, or reviewing Flutter applications for mobile, web, and desktop platforms. Covers widget patterns, Riverpod/BLoC state management, navigation with go_router, Firebase integration, platform channels, custom painting, animations, CI/CD, and production deployment.
Set up and maintain hk git hook manager in any repository. Use when adding pre-commit hooks, configuring linters, setting up code quality automation, working with hk.pkl, or maintaining existing hook configurations. Triggers on tasks involving hk, git hooks, pre-commit checks, commit-msg validation, or linting pipelines.
Validates and implements a GitHub issue against current repository evidence, repeats code review and black-box testing, runs the project's completion gate, and opens a draft pull request. Use when the user runs /implement-issue with an issue number.
Stop AI coding agents from shipping sloppy code — over-abstraction, drive-by refactors, obvious comments, extra files, unrequested deps, giant unreadable files, or fishing outside the workspace for “inspiration”. Use when writing or editing code, implementing features, fixing bugs, splitting or organizing files, or when the user mentions slop, cleanup, keep it simple, minimal diff, file too big, hard to navigate, or stay in the repo.
Work out what to change in a project's test environment so Agent swarm stops getting blocked when it tests against a live backend — seed better test data, widen the test user's permissions, enable feature flags, fix test login, and so on. Reads the setup blockers Agent swarm has recorded, inspects the codebase and test-environment configuration, and proposes concrete fixes. Use when Agent swarm cases are blocked by the environment rather than by the app, or when asked to "improve the test environment", "fix Agent swarm setup blockers" or "why is Agent swarm blocked".
Analyzes Move language packages against the official Move Book Code Quality Checklist. Use this skill when reviewing Move code, checking Move 2024 Edition compliance, or analyzing Move packages for best practices. Activates automatically when working with .move files or Move.toml manifests.
Generate evaluation datasets from QA/dev specification documents. Analyzes spec sheets, discovers agent span structure from code, recommends evaluators, auto-configures variable mappings, and produces an editable plan for review. Use when the user says: NDD generate, create test suite, generate dataset from spec, create evaluation plan, generate NDD plan, or provides a QA specification document.
OpenHarmony PR full lifecycle workflow. Five modes: - Commit: standardized commit with DCO sign-off and Issue linking - Create PR: commit + push to fork + create Issue + create PR on upstream - Fix Codecheck: fetch gate CI codecheck defects from a PR and auto-fix them - Review PR: fetch a PR's changes to local for code review - Fix Review: fetch unresolved review comments from a PR and auto-fix them Triggers on: /oh-pr-workflow, "提交代码", "创建PR", "提个PR", "commit", "修复告警", "修复门禁", "修复codecheck", "fix codecheck", "review pr", "review这个pr", "看下这个pr", "检视pr", "修复review", "修复检视意见", "fix review", or a GitCode PR URL with fix/review intent.
Code review for the current change, before it is pushed. One command runs the security, secret, dependency and lint scanners that fit the changed files, a separate reviewer that checks every scanner finding and is given every changed line, and prints a short receipt with the path of the full report. Use before every git push, when asked for a code review, a security scan, or to review changes, a diff or a pull request, and when a push was blocked or warned by OpenQodex.
Make good open source contributions — check CONTRIBUTING.md first, follow project norms, be a good citizen. Covers bug reports, feature requests, and pull requests with a defensible default posture when the project hasn't documented expectations. Do not use this skill for unrelated requests; route to the nearest named specialist.
Run and configure oxlint — the high-performance JavaScript/TypeScript linter built on the Oxc compiler stack. Use this skill whenever working in a project that has oxlint installed (check for `oxlint` in package.json devDependencies or an `.oxlintrc.json` / `oxlint.config.ts` config file). This includes when you need to lint code after making changes, fix linting errors, configure oxlint rules/plugins, set up or modify `.oxlintrc.json`, or migrate from ESLint.
Generates PR reference XML with commit history and unified diffs between branches, with extension and path filtering. Use when creating pull request descriptions, preparing code reviews, analyzing branch changes, discovering work items from diffs, or generating structured diff summaries.
Use before saying an Oracle task is done, fixed, deployed, faster or working — before the final report, before “xong roi”, “da sua xong”, “deployed”, before ending an autopilot session. Collects the evidence from this session — compile errors, INVALID objects against the baseline, lint, journal entries, before/after plans when performance was the point — and reports only what that evidence proves.
Manual QA testing — verify features end-to-end as a user would, by all means necessary. Exhausts every local tool: browser (Playwright), Docker, ad-hoc scripts, REPL, dev servers. Mock-aware — mocked test coverage does not count. Proves real userOutcome at highest achievable fidelity. Blocked scenarios flow to /pr as pending human verification. Standalone or composable with /ship. Triggers: qa, qa test, manual test, test the feature, verify it works, exploratory testing, smoke test, end-to-end verification.
Discover, configure, and manage Qodo Review Standards using managed tools through MCP or the CLI. Use to set up a skills/plugin repository for reviews, map review skills to target repositories, change skill scope or rules/steering mode, capture a convention as a rule, edit or deactivate rules, or triage suggestions. For reading and applying rules use qodo-get-rules; installing local coding-agent skills uses qodo-setup.
Creates Rails concerns for shared behavior across models or controllers with TDD. Use when extracting shared code, creating reusable modules, DRYing up models/controllers, or when user mentions concerns, modules, mixins, or shared behavior. WHEN NOT: Logic used by only one model or controller (keep it in place), complex business logic (use service objects), or query encapsulation (use query objects).
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Reviews TypeScript code for project-specific quality patterns. Use when writing entities, services, repositories, comparators, formatters, bootstrap methods, deployment stages, diff support, or reviewing PRs with functional patterns. Do NOT use for general TypeScript tutorials or non-Configurator projects.
Use Roundfix to plan releases with the read-only Release Plan Command, clean CodeRabbit pull request feedback, diagnose runtime readiness with the Doctor Command, execute a Spec's Task Graph with the Implement Command, monitor Runs through the Supervisor Run Event Stream, reclaim Run storage with the GC Command, archive completed Specs, run its `--follow-up`, `--status` and `--unattended` Skill Modes, and, inside daemon-assigned Batch runs, follow the bounded Review Issue or Task resolution contract.
Audit claude-skills repository documentation with systematic 9-phase review: standards compliance, official docs verification via Context7/WebFetch, code examples accuracy, cross-file consistency, and version drift detection. Auto-fixes unambiguous issues with severity classification. Use when: investigating skill issues, major package updates detected (e.g., v1.x → v2.x), skill not verified >90 days, before marketplace submission, or troubleshooting outdated API patterns, contradictory examples, broken links, version drift.
Trigger when you would normally reach for pytest, gh, mypy, black, or other raw repo tooling. Redirect to `sm swab`, `sm scour`, `sm buff`, `sm sail`, `sm refit`, or `sm doctor` so remediation follows the established rails. Also trigger when filing issues about slop-mop friction — use `sm barnacle file` / `/slopmop:sm-barnacle`, never `gh issue create`.
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.