Claude Code Skills · page 48

A friendly, hand-drawn sketch interface inspired by pencil illustrations on warm cream paper. Soft teal brand accents, hand-written display headings, rounded pill controls.

Real-world mimicry with textured surfaces, 3D effects, and familiar physical metaphors for intuitive digital interfaces.

Modern minimalist aesthetic with clean lines, intentional color palette, subtle interactions, and consistent spacing.

Generous whitespace, consistent padding, and grid-based layouts for clean, readable, and breathing interfaces.

Narrative-driven design using visuals, copy, and interaction to guide users through engaging, emotionally resonant journeys.

A sun-baked, clay-toned editorial interface built on warm cream surfaces, ink-brown headlines set in a display serif, and a single terracotta accent.

Classic block-game inspired design with playful colors, bold display fonts, and compact, high-energy layouts.

Lively, colorful design with bold playful typography, warm accents, and dynamic visual energy.

1950s-1990s nostalgia with skeuomorphic touches, grainy textures, retro color palettes, and pixel-style typography.

Burp Suite scanning via MCP tools — passive traffic analysis, active payload testing, OOB verification, and vulnerability reporting using Burp's proxy, HTTP sender, Collaborator, and scanner APIs. Use when the user has Burp Suite running with the AI Agent MCP server and wants to scan, test, or analyze web traffic through an AI coding assistant (Claude Code, Gemini CLI, Codex, etc.).

Use the Helmor CLI to remote-control Helmor from the terminal. Use when the user asks to inspect Helmor data/settings, manage repositories/workspaces/sessions/files, send prompts to agents, list models, use GitHub integration, inspect scripts, migrate from Conductor, run Helmor as an MCP server, generate shell completions, quit a running app, check/install/update the Helmor CLI beta, install/update Helmor skills through the beta app flow, or needs the Helmor command reference. Also plan and build a large change as a stack of dependent PRs (`/helmor-cli stack`), split a change you've already written into a stack (`/helmor-cli break`), and re-sync a stack after lower layers change or merge (`/helmor-cli restack`).

Prepare Helmor releases by inspecting the current branch, drafting a concise user-facing Changesets entry first (bump + body — keep it as short as possible), creating any needed pending in-app release announcement under `.announcements/`, and then showing the user the result with a short menu of adjustments they can pick from. Use when the user wants to cut a release, write a changeset, decide patch/minor/major, draft GitHub release notes, create a release announcement, or summarize branch changes into release-ready language.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends AIPex's capabilities with specialized knowledge, workflows, or tool integrations.

Minimalist UX/Interaction Audit Expert that deconstructs complex interactions through cognitive load and operational efficiency lenses. Use this skill when you need to perform a UX walkthrough audit on a Figma prototype or web interface, evaluating usability based on principles like fewer clicks, less UI elements, no hidden logic, and self-explanatory design.

WCAG 2.2 Accessibility Audit skill that systematically evaluates web pages against 8 core Success Criteria (1.1.1, 1.4.3, 1.4.11, 2.1.1, 2.1.2, 2.4.3, 2.4.7, 4.1.2) using accessibility tree inspection and visual analysis. Use this skill when you need to perform accessibility testing/auditing on a live webpage.

AI-powered browser automation using the AIPex Chrome Extension via MCP bridge. Use this skill when the agent needs to control a Chrome browser — navigating pages, clicking elements, filling forms, capturing screenshots, managing tabs, or downloading content — by connecting to the AIPex MCP bridge.

Manages work transitions between team members or agents by creating structured handoff documents, summarizing project status, documenting key decisions, blockers, and open questions, and generating onboarding briefs. Use when someone needs to hand off, hand over, or transition a project; pass work to another person or agent; brief a colleague taking over; prepare a shift change summary; or onboard someone mid-task. Produces ready-to-use handoff documents covering current status, next steps, known issues, technical context, and communication templates for both planned and unplanned transfers.

Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".

Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.

Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.

Applies systematic tracing and isolation techniques to pinpoint exactly where a bug originates in code. Use when a bug is hard to locate, code is not working as expected, an error or crash appears with unclear cause, a regression was introduced between recent commits, or you need to narrow down which component, function, or line is faulty. Covers binary search debugging, git bisect for regressions, strategic logging with [TRACE] patterns, data and control flow tracing, component isolation, minimal reproduction cases, conditional breakpoints, and watch expressions across TypeScript, SQL, and bash.

Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.

Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.

Breaks down complex software, writing, or research tasks into small, atomic, independently completable units with dependency graphs and milestone breakdowns. Use when the user asks to plan a project, decompose a feature, create subtasks, split up work, or needs help organizing a large piece of work into a step-by-step plan. Triggered by phrases like "break down", "decompose", "where do I start", "too big", "split into tasks", "work breakdown", or "task list".

Creates explicit validation checkpoints (verification gates) between project phases to catch errors early and ensure quality before proceeding. Use when the user asks about quality gates, milestone checks, phase transitions, approval steps, go/no-go decision points, or preventing cascading errors across a multi-step workflow. Produces acceptance criteria checklists, automated CI gate configurations, manual sign-off requirements, and conditional review rules for scenarios such as security changes, API changes, or database migrations.

Reviews test code to identify and fix common testing anti-patterns including flaky tests, over-mocking, brittle assertions, test interdependency, and hidden test logic. Flags bad patterns, explains the specific defect, and provides corrected implementations. Use when reviewing test code, debugging intermittent or unreliable test failures, or when the user mentions flaky tests, test smells, brittle tests, test isolation issues, mock overuse, slow tests, or test maintenance problems.

Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.

Applies proven testing patterns — Arrange-Act-Assert (AAA), Given-When-Then, Test Data Builders, Object Mother, parameterized tests, fixtures, spies, and test doubles — to help write maintainable, reliable, and readable test suites. Use when the user asks about writing unit tests, integration tests, or end-to-end tests; structuring test cases or test suites; applying TDD or BDD practices; working with mocks, stubs, spies, or fakes; improving test coverage or reducing flakiness; or needs guidance on test organization, naming conventions, or assertions in frameworks like Jest, Vitest, pytest, or similar.

Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.

Create ad-ready product images (single or collage) by back-solving sub-image sizes from target output ratio, grounding scene design with media_comprehension, generating images via image_generator with strict request params and actor-count control, and pairing each deliverable with a short social tagline for 小红书/抖音.

Create ad-ready product video from product images, with or without character/subject images. The workflow leverages AI-powered image composition, scene understanding, and video generation. Video prompts should follow commercial shot language—visual hooks, product presence, hero shots, detail showcase, function expression, and dynamic visuals.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

A professional skill for App Evaluation (evaluating app's performance with score) and App Improvement (giving professional suggestions for improving the app's performance).

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Search and summarize the latest 7 days of AI news and X discussions using public sources plus browser-based X collection. Use for recent AI news, trends, X discussions, industry briefs, and summaries organized into hot topics, viewpoints, and opportunity areas.

An intelligent assistant specialized in handling media files (images/audio/video). **Only for media file analysis**, does not handle document types.\n\n✅ Media files that can be processed:\n- Images: .jpg, .jpeg, .png, .gif, .bmp, .webp, .svg\n- Audio: .mp3, .wav, .m4a, .flac, .aac, .ogg\n- Video: .mp4, .avi, .mov, .mkv, .webm, .flv\n\n❌ Files that cannot be processed (please do not trigger this skill):\n- Documents: .pdf, .doc, .docx, .txt, .md, .rtf\n- Spreadsheets: .xlsx, .xls, .csv, .tsv\n- Presentations: .pptx, .ppt, .key\n- Code: .py, .js, .ts, .java, .cpp, .go, .rs\n- Archives: .zip, .tar, .gz, .rar, .7z\n- Executables: .exe, .bin, .app, .dmg\n- Databases: .db, .sqlite, .sql\n- Configuration files: .json, .xml, .yaml, .yml, .toml, .ini\n- Web pages: .html, .htm, .css\n\n**Trigger conditions**: When the user explicitly requests to analyze image/audio/video content, or when the file extension belongs to the aforementioned media types.".

Analyzes and automatically optimizes existing agents by improving system prompts and tool configuration.

Creates new agents from user requirements by generating Python implementation and mcp_config.

Single-file TikTok/Douyin video download and traffic metrics via TikHub API using only httpx; optional persisted raw API JSON plus a stdlib post-processor emitting CSV and simplified JSON. Supports one URL or concurrent batch (max 10 workers). No dependency on any project codebase.

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Lightweight TikHub Xiaohongshu image-search workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants Xiaohongshu keyword image search, page-based pagination, or structured note/image metadata from TikHub without a heavy wrapper.

Lightweight TikHub YouTube search and video-detail workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants YouTube comprehensive search results, continuation-token pagination, or structured video metadata from TikHub without a heavy wrapper.

This skill is used for segmented reading and organization when facing large-scale knowledge bases or web pages. It captures original content segment by segment, summarizes key points in real-time, and continuously deposits them into the knowledge base, ensuring orderly information ingestion, clear structure, and traceability.

HTML 转图片 skill - 将 HTML 文件或内容通过 agent-browser 渲染并截图为图片。适用于生成信息图、社交媒体配图、数据可视化截图等场景。

X (Twitter) 抓取 skill - 通过 agent-browser (CDP) 抓取指定用户推文或首页推荐流，支持关键词过滤、Tab 切换、多格式输出。使用场景：按用户/关键词抓取时间线、查看首页推荐流、生成 RSS/JSON/Markdown。

小红书发布 skill - 通过 agent-browser (CDP) 自动发布小红书图文笔记，支持多图上传、标题正文填写、一键发布。使用场景：自动化发布图文笔记到小红书创作中心。

小红书搜索抓取 skill - 通过 agent-browser (CDP) 抓取小红书搜索结果，支持列表+详情、多格式输出。使用场景：按关键词抓取笔记列表与正文、生成 RSS/JSON/Markdown。

Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.

Use when a Hermes agent needs to evaluate agent behavior, run Autocontext scenarios, inspect Hermes curator state, export reusable knowledge, or prepare local MLX/CUDA training data through the autoctx CLI.

Smoke-test or verify UI behaviour on the local Jarvis Registry frontend running at http://localhost/gateway. Use for manual regression checks, bug-fix verification, and end-to-end confirmation of specific flows without running the automated test suite.

Present time-aware itineraries with clear actions and citations

Keep activity suggestions safe and respect local conditions

Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say "analytics"; if the answer requires querying a configured ktx connection, this skill applies.

Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.

Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.

Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.

Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).

Capture semantic-layer and knowledge updates from a live database schema snapshot.

Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.

Map a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derived_table, standalone with sql_always_where). Load when the turn contains `.lkml` content.

Convert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.

Map a MetricFlow semantic_model or metric into ktx semantic layer sources. Covers the MetricFlow to ktx primitive table, `extends:` inheritance flattening, metric-type handling (simple / derived / ratio / cumulative / conversion), `model: ref('x')` resolution, and four worked examples. Load when the turn contains `.yml`/`.yaml` files with top-level `semantic_models:` or `metrics:`.

Synthesize durable ktx wiki pages and semantic-layer sources from staged Notion pages, databases, data-source rows, and clustered Notion evidence. Load when a WorkUnit contains Notion raw files or Notion evidence chunks.

ktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).

How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.

ktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.

Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.

Use this skill when the user wants to install, configure, download, or synchronize online resumes for candidates who applied to currently open BOSS/Zhipin recruiter jobs. Provides a cross-platform boss-agent-cli setup, standard CDP/Chrome conventions, persistent resume root, job/candidate JSON indexes, full and per-job incremental sync, and randomized delay during bulk downloads.

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Run the AI Search Hub browser automation scripts for Yuanbao, LongCat, Doubao, Qwen, Gemini, Grok, and MiniMax. Use this skill when the user wants to ask one of those sites a prompt, auto-start or attach to a Chrome DevTools session on port 9222, seed an isolated debug browser profile from the user's local browser data, detect whether login is required, wait for the user to finish logging in if needed, and then continue automatically.

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Draft a new release of the project.

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Automate browser interactions, test web pages and work with Playwright tests.

Dry run for release: summarize changes since last release and suggest version bump.

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Scan for malicious code in git diff between a tag/commit and HEAD

Guide for creating effective skills. This skill 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.

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生产级 API 的 REST API 设计模式，包括资源命名、状态码、分页、过滤、错误响应、版本控制和速率限制。

编写文章、指南、博客、教程、时事通讯（Newsletter）等长内容，支持从示例或品牌指南中提取独特的语感语调。适用于需要撰写超过一个段落的精炼文本，尤其是对语气一致性、结构和可信度有较高要求时。

后端架构模式、API 设计、数据库优化以及 Node.js、Express 和 Next.js API 路由的服务端最佳实践。

TypeScript、JavaScript、React、Node.js 开发的通用编码标准、最佳实践和模式。

为 X、LinkedIn、TikTok、YouTube、时事通讯（Newsletters）以及跨平台内容重加工营销活动（Repurposed multi-platform campaigns）创建平台原生的内容系统。当用户需要社交媒体帖子、推文串（Threads）、脚本、内容日历，或将单一源素材清晰地适配到多个平台时使用。

Playwright E2E 测试模式、页面对象模型（POM）、配置、CI/CD 集成、产物管理以及不稳定测试（flaky test）策略。

适用于 Claude Code 会话的正规评测框架（Evaluation Framework），实现了评测驱动开发（Eval-Driven Development, EDD）原则

React、Next.js、状态管理（State Management）、性能优化（Performance Optimization）及 UI 最佳实践的前端开发模式。

从零开始或通过转换 PowerPoint 文件创建令人惊叹、动画丰富的 HTML 演示文稿（Presentations）。适用于用户想要构建演示文稿、将 PPT/PPTX 转换为 Web 页面或为演讲/路演创建幻灯片（Slides）的场景。通过视觉探索而非抽象选择，帮助非设计师发掘其审美偏好。

创建并更新融资演示文稿（pitch decks）、单页简介（one-pagers）、投资备忘录（investor memos）、加速器申请、财务模型和募资材料。适用于用户需要面向投资者的文档、预测、资金用途表、里程碑计划，或需要保持多个募资资产内部一致性的场景。

起草用于融资的冷启动邮件（Cold Emails）、引荐语（Warm Intro Blurbs）、后续跟进邮件、进展更新邮件以及其他投资人沟通内容。适用于用户需要向天使投资人、风险投资（VC）、战略投资人或加速器发送简洁、个性化且面向投资者的消息。

进行市场调研（Market Research）、竞品分析（Competitive Analysis）、投资者尽职调查（Investor Due Diligence）以及行业情报（Industry Intelligence）收集，提供来源归因与面向决策的总结。适用于用户需要市场规模估算（Market Sizing）、竞品对比、基金研究、技术扫描或为商业决策提供参考的场景。

当涉及添加身份验证（Authentication）、处理用户输入、操作机密（Secrets）、创建 API 终端节点或实现支付/敏感功能时，请使用此技能。提供全面的安全检查清单和模式。

建议在逻辑间隔进行手动上下文压缩（Context Compaction），以便在任务阶段中保留上下文，而不是依赖任意的自动压缩。

在编写新功能、修复 Bug 或重构代码时使用此技能。强制执行测试驱动开发（TDD），包括单元测试、集成测试和端到端（E2E）测试，覆盖率需达到 80% 以上。