Skills de Claude Code · página 139

Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.

Generate targeted test inputs to reach specific code paths and hard-to-reach behaviors in Python code. Use when: (1) Targeting uncovered branches or specific execution paths, (2) Need coverage-guided test generation, (3) Want to leverage LLM understanding of code semantics for meaningful test inputs, (4) Testing boundary conditions and edge cases systematically, (5) Combining symbolic reasoning with fuzzing. Provides path analysis, constraint solving, coverage-guided strategies, and LLM-driven semantic generation for comprehensive test input creation.

Automatically identify potential boundary and exception cases from requirements, specifications, or existing code, and generate comprehensive test cases targeting boundary conditions, edge cases, and uncommon scenarios. Use this skill when analyzing programs, code repositories, functions, or APIs to discover and test corner cases, null handling, overflow conditions, empty inputs, concurrent access patterns, and other exceptional scenarios that are often missed in standard testing.

Generate setup scripts and instructions for development environments across platforms. Use when: (1) Setting up new development machines (Python, Node.js, Docker, databases), (2) Creating automated setup scripts for team onboarding, (3) Need cross-platform setup instructions (macOS, Linux, Windows), (4) Installing development tools and dependencies, (5) Configuring version managers and package managers. Provides executable setup scripts, platform-specific guides, and tool installation instructions.

Explains test failures and provides actionable debugging guidance. Use when tests fail (unit, integration, E2E), builds fail, or code throws errors. Analyzes error messages, stack traces, and test output to identify root causes and suggest concrete fixes. Handles pytest, jest, junit, mocha, vitest, selenium, cypress, playwright, and other testing frameworks across Python, JavaScript/TypeScript, Java, Go, and other languages.

Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.

Selectively instruments code to capture runtime data for debugging failures and bugs. Use when investigating crashes, exceptions, unexpected behavior, test failures, or performance issues. Analyzes stack traces and error messages to identify suspicious code regions, then adds targeted logging, tracing, and assertions to capture variable values, execution paths, timing, and conditional branches. Supports Python, JavaScript/TypeScript, Java, and C/C++.

Identifies non-deterministic or unreliable tests through static code analysis and test result analysis. Use when Claude needs to find flaky tests, analyze test reliability, or investigate intermittent test failures. Supports Python (pytest, unittest) and Java (JUnit, TestNG) test frameworks. Trigger when users mention "flaky tests", "intermittent failures", "non-deterministic tests", "unreliable tests", or ask to "find flaky tests", "analyze test stability", or "why tests fail randomly".

Generate formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.

Automatically migrate Python web applications between frameworks (Flask → FastAPI, Django → FastAPI). Use when you need to migrate an existing web application to a modern framework while preserving functionality. The skill analyzes the codebase, updates routes, handlers, configuration, dependency injection patterns, and tests. Creates git commits for each migration phase and generates a comprehensive summary of all changes. Supports automatic dependency updates, code transformations, and test adaptations.

Designer-turned-developer who crafts stunning UI/UX even without design mockups. Use for any frontend implementation requiring visual design decisions, aesthetic direction, or pixel-perfect UI work.

Generate complete, production-ready functions and classes from formal specifications, design descriptions, type signatures, or natural language requirements. Use this skill when implementing APIs from specifications, creating data structures from schemas, building classes from UML diagrams, generating code from contracts, or translating design documents into code. Supports multiple programming languages and follows language-specific best practices.

Generate randomized and edge-case inputs to detect unexpected failures, bugs, and security vulnerabilities through fuzz testing. Use when creating test cases for robustness testing, generating adversarial inputs, testing error handling, finding edge cases, or security testing. Produces Python test code with fuzzing inputs for strings, numbers, and structured data focusing on edge cases, invalid inputs, and random valid inputs. Triggers when users ask to generate fuzz tests, create randomized test inputs, test edge cases, find bugs through fuzzing, or generate adversarial test cases.

Automatically performs git bisect to identify the first bad commit that introduced a bug or failure. Use when debugging regressions, tracking down when a test started failing, or identifying which commit broke functionality. Handles flaky tests with retry logic and provides comprehensive reports with bisect logs and confidence levels.

Git expert combining atomic commits, rebase/squash, and history search (blame, bisect, log -S). Use for any git operations requiring structured commit strategies, history rewriting, or code archaeology. Triggers: 'commit', 'rebase', 'squash', 'who wrote', 'when was X added', 'find the commit that'.

Unified GitHub triage for issues AND PRs. Classifies open items, answers questions from codebase, analyzes bugs, reviews PRs, and produces a structured triage report. Triggers: 'triage', 'triage issues', 'triage PRs', 'github triage'.

Extract abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.

Incrementally implement new features in Java repositories from natural language descriptions. Use when adding functionality to existing Java codebases (Maven or Gradle projects). Takes a feature description as input and outputs modified repository with implementation code, corresponding JUnit tests, and verification that all tests pass. Supports method additions, new class creation, and method modifications with proper Java conventions.

Takes a Python repository and natural language feature description as input, implements the feature with proper code placement, generates comprehensive tests, and ensures all tests pass. Use when Claude needs to: (1) Add new features to existing Python projects, (2) Implement functions, classes, or modules based on requirements, (3) Modify existing code to add functionality, (4) Generate unit and integration tests for new code, (5) Fix failing tests after implementation, (6) Ensure code follows existing patterns and conventions.

Generates hierarchical context files (CLAUDE.md) throughout a project directory tree, providing AI agents with directory-specific knowledge for better code understanding. Use when setting up a new project for AI-assisted development.

Generate integration tests for multiple interacting components in Python. Use when testing interactions between: (1) Multiple services or APIs (REST/GraphQL endpoints, microservices), (2) Database operations with repositories/ORMs (SQLAlchemy, Django ORM), (3) External services (payment gateways, email services, third-party APIs), (4) Message queues and event-driven systems, (5) Full stack workflows (API + database + business logic). Provides test structure templates, fixtures, test data builders, and patterns for pytest-based integration testing.

Verify that interface and class contracts (preconditions, postconditions, invariants) are preserved across program versions. Use when validating refactorings, checking API compatibility, verifying design-by-contract implementations, or ensuring behavioral contracts remain intact after code changes. Automatically detects contract violations, identifies affected methods and classes, and provides actionable guidance for resolving violations while maintaining program correctness.

[TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]

Analyze differences in program intervals between two versions of a program (old and new) to identify added, removed, or modified intervals. Use when comparing program versions, analyzing variable ranges, detecting behavioral changes in numeric computations, validating refactorings, or assessing migration impacts. Supports optional test suite integration to validate interval changes. Generates comprehensive reports highlighting intervals requiring further testing or verification.

Automatically updates regression tests based on interval analysis to maintain coverage of key program intervals. Use when code changes affect value ranges, conditionals, or control flow, and existing tests need updating to maintain interval coverage. Analyzes interval information from updated code, identifies coverage gaps, adjusts test inputs and assertions, removes redundant tests, and generates new tests for uncovered intervals. Supports Python, Java, JavaScript, and C/C++ with various test frameworks (pytest, JUnit, Jest, Google Test).

Inline orchestration workflow for automated bug detection and fixing with Beads integration. Provides step-by-step phases for bug-hunter detection, history enrichment for priority bugs, priority-based fixing with bug-fixer, and verification cycles.

Process error logs from admin panel - fetch new errors, analyze, create tasks, fix, and mark resolved

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

Calculate priority score for bugs, issues, or tasks based on severity, impact, and likelihood. Use for bug prioritization, task ordering, or risk assessment.

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

Inline orchestration workflow for dead code detection and removal with Beads integration. Provides step-by-step phases for dead-code-hunter detection, priority-based cleanup with dead-code-remover, and verification cycles.

Analyzes code changes for bugs, security vulnerabilities, performance problems, and architectural improvements. Generates structured markdown reports with evidence-based findings tied to specific file:line references, and creates Beads tasks for tracking. Adapts review depth based on scope — from quick pre-commit checks to thorough branch reviews. Use proactively when reviewing code before commits, after implementing features, before PRs, when checking code quality, or when the user mentions "review", "check changes", "code review", "PR review", "review branch", or any code quality assessment.

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

Inline orchestration workflow for dependency audit and updates with Beads integration. Provides step-by-step phases for dependency-auditor detection, priority-based updates with dependency-updater, and verification cycles.

Parse and validate semantic version strings from various formats. Use for extracting versions from text, validating semver compliance, or comparing version numbers.

Generate standardized conventional commit messages with Claude Code attribution. Use when creating automated commits, release commits, or any git commit requiring consistent formatting.

Generate well-formatted markdown tables from data with proper alignment and spacing. Use for report statistics, comparison tables, or summary data presentation.

Generate properly formatted TodoWrite lists for orchestrator progress tracking. Use when creating phase checklists, updating task status, or ensuring consistent todo formatting.

Guide frontend design decisions to create distinctive, creative UIs that avoid generic AI-generated aesthetics. Use when building UI components, designing layouts, selecting colors/fonts, or implementing animations.

Generate changelog entries from git commits, plan files, or structured data. Use for version releases, creating CHANGELOG.md sections, or documenting changes between versions.

Create standardized report headers with metadata for all agent-generated reports. Use when generating bug reports, security audits, dependency reports, or any worker output requiring consistent formatting.

Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

Parse build errors, test failures, type-check output, and validation logs into structured data. Use when processing npm/pnpm output, TypeScript errors, Jest failures, or any validation command results for quality gates.

Parse git status output into structured data showing staged, modified, and untracked files. Use for pre-flight validation, checking clean working directory, or listing changed files before commits.

Extract version, dependencies, and metadata from package.json files. Use when needing project version, dependency list, or package metadata for releases, audits, or reports.

Process GitHub Issues - fetch open issues, read comments, analyze suggestions, find similar, create Beads tasks, propose fix plan

Render templates with variable substitution using {{variable}} or ${variable} syntax. Use for generating formatted output, reports, commit messages, or any text requiring variable interpolation.

Inline orchestration workflow for code duplication detection and consolidation with Beads integration. Provides step-by-step phases for reuse-hunter detection, priority-based consolidation with reuse-fixer, and verification cycles.

Automatically rollback changes from failed workflow phases using changes log files. Use when workflows fail and need to restore previous state, including file restoration, artifact cleanup, and command reversal. Use for error recovery after failed bug fixes, security patches, or refactoring operations.

Execute quality gate validation with configurable blocking behavior. Use when running type-check, build, tests, lint, or custom validation commands in orchestrators or workers to enforce quality standards.

Inline orchestration workflow for security vulnerability detection and remediation with Beads integration. Provides step-by-step phases for security-scanner detection, priority-based fixing with vulnerability-fixer, and verification cycles.

Comprehensive software architecture skill for designing scalable, maintainable systems using ReactJS, NextJS, NodeJS, Express, React Native, Swift, Kotlin, Flutter, Postgres, GraphQL, Go, Python. Includes architecture diagram generation, system design patterns, tech stack decision frameworks, and dependency analysis. Use when designing system architecture, making technical decisions, creating architecture diagrams, evaluating trade-offs, or defining integration patterns.

Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.

Install and configure Knip for dead code detection. Use before running dead-code-hunter or dependency-auditor to ensure Knip is available. Handles installation, configuration creation, and validation.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

UI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.

UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.

Validate that orchestrator plan files conform to expected JSON schema. Use before workers read plan files or after orchestrators create them to ensure proper structure and required fields.

Validate that worker-generated reports have all required sections and proper formatting. Use in quality gates, for report completeness checking, or when debugging missing report sections.

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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Search and retrieve agent skills at runtime. This skill should be used when the agent needs to find specialized capabilities, workflows, or domain knowledge to accomplish a task. Skyll aggregates skills from skills.sh and returns full SKILL.md content ready for context injection.

Generate a visual anatomy annotation in Figma showing numbered markers on a component instance with an attribute table. Use when the user mentions "anatomy", "anatomy annotation", "component anatomy", "create anatomy", or wants to annotate a component's structural elements.

Generate API overview specifications documenting component properties, values, defaults, and configuration examples. Use when the user mentions "api", "api spec", "props", "properties", "component api", or wants to document a component's configurable properties.

Generate color annotation specifications mapping UI elements to design tokens. Use when the user mentions "color", "color annotation", "color spec", "tokens", "design tokens", or wants to document which color tokens a component uses.

Generate a single self-contained markdown specification for a Figma component covering API, structure, color, and screen-reader behavior. Reads a `_base.json` produced by the uSpec Extract plugin, runs four read-only interpretation skills in parallel, reconciles their outputs, and writes one `.md` to disk. Use when the user mentions "component md", "component markdown", "spec md", "source of truth", "create-component-md", or wants a portable Markdown spec that any LLM can build from.

Generate motion specification annotations from After Effects timeline data. Use when the user mentions "motion", "motion spec", "animation spec", "timeline", or wants to document a component's animation properties.

Generate a visual property annotation in Figma showing each configurable property axis with component instance previews. Use when the user mentions "property", "properties", "property annotation", "create property", or wants to document a component's configurable properties visually.

Generate structure specifications documenting component dimensions, spacing, padding, and how values change across density, size, and shape variants. Use when the user mentions "structure", "structure spec", "dimensions", "spacing", "density", "sizing", or wants to document a component's dimensional properties.

Generate screen reader accessibility specifications for VoiceOver (iOS), TalkBack (Android), and ARIA (Web). Use when the user mentions "voice", "voiceover", "screen reader", "accessibility spec", "talkback", "aria", or wants to create accessibility documentation for a UI component.

Interpret a component's API (properties, sub-components, configuration examples) from the `_base.json` produced by the uSpec Extract Figma plugin, and write the normalized JSON to disk. Read-only interpretation — no Figma calls except an optional tiny delta script. Use as a sub-skill of create-component-md.

Interpret a component's color-token mapping (per-element fills/strokes/effects, axis classification, boolean delta, variable mode detection, strategy plan) from the `_base.json` produced by the uSpec Extract Figma plugin, and write the normalized JSON to disk. Read-only interpretation — no Figma calls except an optional tiny delta script.

Interpret a component's structure spec (variant axes, dimensions, sub-components, slot contents, cross-variant diffs) from the `_base.json` produced by the uSpec Extract Figma plugin, and write the normalized JSON to disk. Read-only interpretation — no Figma calls except an optional tiny delta script. Use as a sub-skill of create-component-md.

Interpret a component's screen-reader accessibility spec (focus order, merge analysis, per-state platform tables for VoiceOver/TalkBack/ARIA, slot insertion plans) from the `_base.json` produced by the uSpec Extract Figma plugin, and write the normalized JSON to disk. Read-only interpretation — no Figma calls except an optional tiny delta script.

Configure uSpec's Figma template library. Verifies your MCP connection, then extracts template component keys from your Figma library and writes them to uspecs.config.json. Run after `npx uspec-skills init` has set up the platform and config file. Use when the user mentions "firstrun", "first run", "setup library", "configure templates", or "link templates".

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Execute natural language tasks using AI

Make AI-powered assertions about screen state

Deploy TestDriver on your AWS infrastructure using CloudFormation

Speed up tests with screenshot-based caching

1.7x faster test execution with intelligent caching and optimization

Solve captchas using 2captcha service

Run TestDriver tests in CI/CD with parallel execution and cross-platform support

Click at specific coordinates or on elements

Initialize and configure the TestDriver SDK client

The fastest way to get started with TestDriver. Just set your API key and start testing.

Configure TestDriver sandbox options and environment settings