Claude Code Skills · page 132

Identifies and analyzes conflicts in software requirements including logical contradictions, technical incompatibilities, resource constraints, timeline issues, data conflicts, and stakeholder priority mismatches. Use when reviewing requirement sets, specifications, user stories, or project plans to detect conflicts that could block implementation or cause rework. Provides detailed conflict analysis with resolution strategies and impact assessment.

Generate Dockerfiles, Docker Compose configurations, and Kubernetes manifests for containerizing applications. Use when: (1) Creating Dockerfiles for Node.js, Python, Java, Go, or other applications, (2) Setting up multi-service environments with Docker Compose, (3) Generating Kubernetes deployments, services, and ingress configurations, (4) Optimizing container images for production, (5) Implementing containerization best practices. Provides both ready-to-use templates and custom-generated configurations based on project requirements.

Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).

Debug proof failures using counterexamples from Nitpick (Isabelle) or QuickChick (Coq) to identify specification errors, missing preconditions, and proof strategy issues. Use when: (1) A proof attempt fails and you need to understand why, (2) Counterexamples are generated by Nitpick or QuickChick, (3) Specifications may be incorrect or incomplete, (4) Theorems need validation before proving, (5) Missing preconditions or lemmas need identification, or (6) Proof failures need explanation and correction suggestions. Supports both Isabelle/HOL and Coq equally.

Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.

Generate concrete counterexamples when formal verification, assertions, or specifications fail. Use this skill when debugging failed proofs, understanding why verification fails, creating minimal reproducing examples, analyzing assertion violations, investigating invariant breaks, or diagnosing specification mismatches. Produces concrete input values, execution traces, and state information that demonstrate the failure.

Automatically generates executable test cases from model checking counterexample traces. Translates abstract counterexample states and transitions into concrete test inputs, execution steps, and assertions that reproduce property violations. Use when working with model checker outputs (SPIN, CBMC, NuSMV, TLA+, Java PathFinder, etc.) and needing to create regression tests, validate bug fixes, or reproduce verification failures in executable test suites.

Analyze existing test suites and source code to suggest additional unit tests that improve test coverage. Use this skill when working with test files and source code to identify untested code paths, missing edge cases, uncovered branches, untested error conditions, and gaps in test coverage. Supports major testing frameworks (pytest, Jest, JUnit, Go testing, etc.) and generates targeted test suggestions based on coverage analysis.

Translate C/C++ programs to equivalent Dafny code while preserving semantics and ensuring verification. Use when users ask to convert, translate, or port C/C++ code to Dafny, or when they need to formally verify C/C++ algorithms using Dafny's verification capabilities. Handles functions, structs, pointers, arrays, memory management, and ensures the generated Dafny code is well-typed, executable, verifiable, and can successfully run.

Analyzes code to identify security-critical time intervals and timing vulnerabilities in authentication, authorization, and time-sensitive security operations. Use this skill when reviewing code for proper timeout enforcement, token expiration, session management, rate limiting, password reset validity, or any time-sensitive security mechanism. Detects missing expiration checks, excessive timeout values, lack of rate limiting, client-side only validation, hardcoded timeouts, and timing attack vulnerabilities. Triggers when users ask to check security timeouts, verify token expiration handling, audit session timeout implementation, review rate limiting, or analyze time-based security controls.

Analyze CVE reachability in software repositories by examining how vulnerable dependencies are imported and used. Determines whether vulnerable components, classes, or functions are reachable from project code through call chain analysis, reflection detection, dynamic loading patterns, and configuration-gated behavior. Classifies each CVE as likely reachable, possibly reachable, or likely unreachable with supporting evidence. Use when analyzing security vulnerabilities in dependencies, performing post-disclosure CVE triage, assessing vulnerability impact, or when users ask to analyze CVE reachability, check if vulnerabilities are exploitable, or evaluate dependency security risks.

Generate prioritized CVE watchlists and actionable security recommendations for repositories. Use when analyzing CVE scan results, creating security reports, prioritizing vulnerability remediation, or generating security gate reports for CI/CD. Takes CVE scan results (JSON/SARIF from npm audit, pip-audit, Snyk), reachability analysis, and cutoff date as input. Combines severity, reachability, exploitability, and dependency criticality to rank CVEs by practical risk. Outputs markdown reports with concrete next-step guidance (immediate upgrade, monitor, ignore with justification, apply mitigation) suitable for issue trackers, security reviews, and CI security gates.

Identify and analyze unused or redundant code including unused functions/methods, unused variables/imports, unreachable code, and redundant conditions. Use when cleaning up codebases, improving maintainability, reducing technical debt, or conducting code quality audits. Analyzes Python code using AST analysis and produces markdown reports listing dead code locations with line numbers, severity ratings, and recommendations. Triggers when users ask to find dead code, remove unused code, identify unused imports, find unreachable code, or clean up redundant logic.

Dead code removal via parallel scanning, reference verification, batch execution, and atomic commits. You are the ORCHESTRATOR — you scan, verify, batch, then delegate ALL removals.

Identify, analyze, and manage software dependencies before deployment. Use this skill when preparing applications for deployment, resolving dependency conflicts, updating dependencies, auditing security vulnerabilities, managing package versions, or troubleshooting dependency-related issues. Supports multiple package managers (npm, pip, maven, cargo, go mod, composer) and provides actionable recommendations for dependency management.

Identify and replace deprecated API usage in source code with modern alternatives. Use when: (1) Modernizing legacy codebases, (2) Upgrading framework versions (React, Django, Spring, etc.), (3) Fixing deprecation warnings in build output, (4) Preparing for major version upgrades, (5) Ensuring code uses current best practices. Supports Python, JavaScript/TypeScript, Java, and other major languages with both AST-based detection and pattern matching for accurate identification and automated replacement with validation.

Recommends appropriate software design patterns based on problem descriptions, requirements, or code scenarios. Use when designing software architecture, refactoring code, solving common design problems, or choosing between design approaches. Analyzes the problem context and suggests suitable creational, structural, behavioral, architectural, or concurrency patterns with implementation guidance and trade-off analysis.

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).

Brand onboarding setup skill. Captures a client's visual identity, content patterns, audience, and goals through evidence capture + pre-filled client doc + structured intake. Writes context/brand-style.md as the foundation for all social skills. Run once per client before using /social-creative-designer, /content-calendar, or /caption-writer.

Writes on-brand social media captions for SMBs. Takes post concepts or a content calendar and produces ready-to-post captions with hooks, body copy, CTAs, and hashtags. Reads brand-style.md for voice and tone. Supports Instagram, LinkedIn, Facebook, TikTok, and X. Batch and single-post modes. Optional trend and competitor research via Firecrawl and SerpApi.

Builds a month of social media post ideas for SMBs. Takes brand context, platforms, and goals and produces a structured content calendar with post topics, formats, angles, and visual direction for each slot. Output saves to context/content-calendar.md for use by /caption-writer and /social-creative-designer. Supports Instagram, LinkedIn, Facebook, TikTok, and X.

LinkedIn Content Specialist. Writes LinkedIn-native posts for SMB clients — first-person, professional-but-human, built for LinkedIn's format and algorithm. Not adapted captions: posts written from first principles for the platform. Reads brand-style.md and content-calendar.md. Flags posts that would benefit from a Blotato infographic. Output to outputs/linkedin/.

Social Media Publisher. Takes approved content from outputs/ and schedules it across platforms via Blotato MCP. Generates infographic-style visuals (stat cards, framework diagrams, process graphics, quote graphics) for posts flagged by platform-specialist skills. Requires Blotato MCP to be configured. Run after /linkedin-writer, /threads-writer, /x-writer, or /caption-writer.

Creative Designer skill. Takes a post concept, a client product photo, or a real lifestyle photo and produces on-brand social media visuals using the client's brand style guide. Four modes — Generate (AI image from concept), Composite (client product photo anchored in an AI-generated scene), Brand (apply text overlay treatment to a real client photo), Stop-Motion Reel (6-frame action sequence exported as MP4). Reads brand-style.md, builds prompts, generates/edits images via Nano Banana MCP, outputs images + prompt log + creative brief.

Social Media Manager role skill. Orchestrates the full SMB social media workflow across three layers — Foundation (brand setup + calendar), Content Creation (captions, platform-specialist posts, visuals), and Distribution (scheduling via Blotato + performance review). Coordinates all 9 component skills as a sequential, human-reviewed pipeline. Run this skill instead of invoking component skills individually.

Monthly social media performance review for SMBs. Analyses post-level and account-level data from Instagram, LinkedIn, Facebook, or TikTok. Identifies what worked, what didn't, and why. Produces a client-ready report and specific recommendations that feed back into the content calendar. Accepts CSV exports, screenshots, or manual data input.

Threads Content Specialist. Writes Threads-native posts — short, direct, opinion-led, made for conversation. Strictly enforces the 500 character limit with a count on every post. Supports standalone posts and threads (connected posts). Reads brand-style.md and content-calendar.md. Flags infographic opportunities for /publisher. Output to outputs/threads/.

X/Twitter Content Specialist. Writes X-native posts — hooks, threads, and standalone tweets optimised for the platform. Strictly enforces the 280 character limit with a count on every post. Supports standalone tweets and X threads (1/ format). Reads brand-style.md and content-calendar.md. Optional X trend research via Tasty Content MCP. Output to outputs/x/.

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.

Extract design primitives from a public website and generate starter token files for your project.

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".

查询A股实时行情、历史数据、技术指标、事件、资金面、热门行业/概念、板块热力图与个股行业信息。Use when 用户提到股票代码、板块、热门概念、热门行业、概念涨跌、行业涨跌、热力图、市场快讯、技术分析、财务指标、指数成分、交易日历、宏观数据或个股所属行业。

A股模拟盘交易与回测技能。Use when 用户要启动模拟仓服务、创建多账户、下限价单/市价单、撤单、查询持仓资金、验证涨跌停成交逻辑或运行A股回测。

A股“MACD底背离 + 零轴下二次金叉”实战分析与交易执行技能。用于把用户的盘感描述转成可执行的三档决策（上观察名单/可试错出手/必须放弃）、盘中检查单、入场与风控规则。适用于用户提到“二次金叉”“水下二次金叉”“底背离”“第一脚第二脚”“回踩验底”“MACD背离”“抄底修复”“想做但怕假信号”等场景。

基于“均线定方向，MACD定节奏”的A股选股与交易计划技能。Use when 用户要求把均线与MACD结合做选股、筛票、盘前候选池、趋势跟随、回踩再上、金叉确认、顶背离减仓、或希望把技术判断沉淀成可执行规则与风控模板。

基于退哥短线交易规则的A股场景化决策技能。Use when 用户要按交易场景查看短线规则、做选股、判断趋势回踩、涨停回调、连板接力、洗盘结束、卖出失效或仓位纪律。

Use when the user wants an end-to-end AI4S research pipeline — broad direction or specific topic in, full research package out (exploration + literature survey + experiment + paper). Meta-skill that chains the four downstream skills in order. Pure markdown, no Python runtime.

Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.

Use when the user wants a paper audited for integrity issues — image misuse, numerical anomalies, logical gaps — and needs a reviewable evidence report. Works on external papers (PDF / DOI / arXiv) and on outputs from a local paper-writer run. Single-stage skill.

Use when the user wants a comprehensive literature survey on a specific research topic. Outputs a complete PDF survey (6–20 pages, 60+ real citations, 100+ recommended) with LaTeX source, topic-specific publication figures, and a classified literature table. Single-stage, no Python runtime.

Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Outputs interactive HTML, HD PNG, and PDF with colorful branch themes.

Use when the user wants a complete, publication-grade research paper on a specific topic — produces 200+ real citations, 4–8 publication-grade figures, and 7 sections of substantive prose compiled to PDF in one pass. No skeleton stage.

Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.

Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.

Fetches AI news from smol.ai RSS and generates structured markdown with intelligent summarization and categorization. Optionally creates beautiful HTML webpages with Apple-style themes and shareable card images. Use when user asks about AI news, daily tech updates, or wants news organized by date or category.

WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.

Chinese Word document formatting toolkit for .docx/.doc/.wps workflows. Use when Codex needs to diagnose document formatting, fix mixed Chinese/English punctuation and spacing, apply official/academic/legal/custom presets, normalize tables and page numbers, preserve or output Word revision marks, convert plain text or Markdown into formatted DOCX, or batch/script document cleanup for Chinese official documents.

Map this codebase into project memory — a code graph of every symbol and how they connect, plus a written profile of stack, layout, conventions and workflows. Re-run any time to refresh both in place. Use when memory is newly installed on an existing project, or when the project has changed enough that the stored map is stale.

A decision board for evidence-based calls — the human adjudicates, the agent runs the errands. Three layers (sources → ideas → output) in plain markdown, synced on the human's command, projected onto a visual canvas. Use for vendor/tool selection, literature reviews, due diligence, competitive analysis, or any contested call that must stand on traceable evidence — triggers like "file these papers", "put this on the board", "assemble the design", "how do we decide this".

Role-based access control (RBAC) with permissions and policies. Use for admin dashboards, enterprise access, multi-tenant apps, fine-grained authorization, or encountering permission hierarchies, role inheritance, policy conflicts.

100+ animated React components (Aceternity UI) for Next.js with Tailwind. Use for hero sections, parallax, 3D effects, or encountering animation, shadcn CLI integration errors.

shadcn/ui AI chat components for conversational interfaces. Use for streaming chat, tool/function displays, reasoning visualization, or encountering Next.js App Router setup, Tailwind v4 integration, AI SDK v5 migration errors.

Vercel AI SDK v5 for backend AI (text generation, structured output, tools, agents). Multi-provider. Use for server-side AI or encountering AI_APICallError, AI_NoObjectGeneratedError, streaming failures.

Vercel AI SDK v5 React hooks (useChat, useCompletion, useObject) for AI chat interfaces. Use for React/Next.js AI apps or encountering parse stream errors, no response, streaming issues.

Secure API authentication with JWT, OAuth 2.0, API keys. Use for authentication systems, third-party integrations, service-to-service communication, or encountering token management, security headers, auth flow errors.

Creates comprehensive API changelogs documenting breaking changes, deprecations, and migration strategies for API consumers. Use when managing API versions, communicating breaking changes, or creating upgrade guides.

Verifies API contracts between services using consumer-driven contracts, schema validation, and tools like Pact. Use when testing microservices communication, preventing breaking changes, or validating OpenAPI specifications.

Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.

Implements standardized API error responses with proper status codes, logging, and user-friendly messages. Use when building production APIs, implementing error recovery patterns, or integrating error monitoring services.

Builds flexible API filtering and sorting systems with query parameter parsing, validation, and security. Use when implementing search endpoints, building data grids, or creating dynamic query APIs.

Configures API gateways for routing, authentication, rate limiting, and request transformation in microservice architectures. Use when setting up Kong, Nginx, AWS API Gateway, or Traefik for centralized API management.

Implements efficient API pagination using offset, cursor, and keyset strategies for large datasets. Use when building paginated endpoints, implementing infinite scroll, or optimizing database queries for collections.

Implements API rate limiting using token bucket, sliding window, and Redis-based algorithms to protect against abuse. Use when securing public APIs, implementing tiered access, or preventing denial-of-service attacks.

Creates professional API documentation using OpenAPI specifications with endpoints, authentication, and interactive examples. Use when documenting REST APIs, creating SDK references, or building developer portals.