Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Command-line interface for CloudAnalyzer — an agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.
Run any question, plan, PR, or code through a Diverse Multi-Agent Debate (DMAD) council of 5 AI advisors with distinct reasoning methods. Advisors collaborate, peer-review each other anonymously, and a chairman synthesizes a verdict. Empirically outperforms adversarial debate (M3MADBench 2026, DMAD ICLR 2025). Use when: 'council this', 'run the council', 'council review', 'pressure-test this', 'stress-test this', 'war room this', or when facing a genuine decision with stakes and tradeoffs
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
Use when performing security audits, vulnerability assessments, or compliance checks on Flutter or mobile applications. Covers OWASP Mobile Top 10 (2024) — hardcoded secrets (M1), insecure storage (M9), weak cryptography (M10), network issues (M5), and 6 more categories with automated scanners and remediation guidance
Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
TDD and testing skills for iOS/macOS apps. Covers characterization tests, TDD workflows, test contracts, snapshot tests, test infrastructure, and deterministic quality gates (fitness functions, coverage ratchet, mutation testing). Use for test-driven development, adding tests to existing code, or building test infrastructure
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to “open a website”, “fill out a form”, “click a button”, “take a screenshot”, “scrape data from a page”, “test this web app”, “login to a site”, “automate browser actions”, or any task requiring programmatic web interaction.
Review a pull request or a set of code changes for bugs, logic errors, and project-convention violations using a confidence-filtered, multi-agent process. Use this skill when the user asks to review a PR, audit pending changes, or inspect a diff for problems before merging.
Testing Inertia Rails responses with RSpec and Minitest: component assertions, prop matching, flash verification, deferred props, and partial reload helpers. Use when writing controller specs, request specs, or integration tests for Inertia pages. ALWAYS use matchers (render_component, have_props, have_flash), NOT direct access (inertia.component, inertia.props). CRITICAL: after POST/PATCH/DELETE with redirect, MUST call follow_redirect! before asserting flash or props — without it you're asserting against the 302, not the Inertia page. Setup: require 'inertia_rails/rspec'.
Use vision models to self-review screenshots against design intent. Catches spacing issues, alignment problems, color inconsistencies, responsive bugs, and accessibility gaps. Use when reviewing designs, comparing implementations to mockups, or doing pre-ship QA.
Vitest testing framework patterns and best practices. Use when writing unit tests, integration tests, configuring vitest.config, mocking with vi.mock/vi.fn, using snapshots, or setting up test coverage. Triggers on describe, it, expect, vi.mock, vi.fn, beforeEach, afterEach, vitest.
Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
Run an iterative code-improvement hill climb locally against real Datadog LLM-Obs data, with Claude Code as the agent. Establishes a baseline evaluation, makes one focused change, re-scores with the same harness, keeps the change if it improves the score toward the goal (labeling gains within noise as tentative), and repeats. Use when the user says "run an auto experiment", "hill-climb this code", "iteratively improve X and measure the delta", "optimize this prompt/file against my traces", "auto-optimize against LLM-Obs", or wants the local equivalent of the auto_experiments worker. Works from an ml_app, a dataset_id, an annotation_queue_id (a queue of human-labeled interactions), a list of trace_ids, or (by exception) a local dataset file. The corpus and its validation/test splits live in Datadog LLM-Obs Datasets, created once per run with timestamps in their names
Validate an implemented React Doctor rule before merge. Use after focused tests pass to review detector correctness, inspect open-source hits, run pull request parity, add regression coverage, prepare a changeset, write pull request copy, or address review findings.
Evaluate and improve code modularization using the Balanced Coupling Model. Analyzes coupling strength, connascence types, and distance to identify refactoring opportunities and architectural improvements. Use when reviewing code architecture, refactoring modules, or designing new systems.
Reference data for .NET test framework detection patterns, assertion APIs, skip annotations, setup/teardown methods, and common test smell indicators across MSTest, xUnit, NUnit, and TUnit. Loaded by test analysis skills (test-anti-patterns) as framework-specific lookup tables.
This skill should be used when the user asks to "analyze skill quality", "evaluate this skill", "review skill quality", "check my skill", or "generate quality report". Evaluates local skills across description quality, content organization, writing style, and structural integrity.
Conducts comprehensive frontend design reviews covering UI/UX design quality, design system validation, accessibility compliance, responsive design patterns, component library architecture, and visual design consistency. Evaluates design specifications, Figma/Sketch files, design tokens, interaction patterns, and user experience flows. Identifies usability issues, accessibility violations, design system deviations, and provides actionable recommendations for improvement. Produces detailed design review reports with severity-rated findings, visual examples, and implementation guidelines. Use when reviewing frontend designs, validating design systems, ensuring accessibility compliance, evaluating component libraries, assessing responsive designs, or when users mention design review, UI/UX review, Figma review, design system validation, accessibility audit, or frontend design quality.
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.