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Claude Code Skills · page 124

Individual Claude Code skills mined from every repository in the directory: each SKILL.md, installable with one command, with its full definition and the repository's trust signals.

13,969 skills1-command install
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  73. 小红书 / 抖音 / 快手 / 视频号 自媒体「(compose 素材打底 →) 找对标 → 拆爆款 → 套自己」四命令闭环,另支持带货账号诊断、带货视频链接拆解和证据驱动的脚本生成(commerce)。给账号 / 平台视频链接 / 本地视频 / 商品详情、SKU 与资质材料,输出账号与对标差距、内容拆解、商品事实卡、口播和分镜。当用户说"找对标"、"拆这条爆款"、"对着这条仿写"、"下一条该写什么"、"我这个号缺爆款选题"、"帮我写一条对标 XX 的笔记"、"我有素材帮我写一条能爆的"、"这份文档/这组图/这段视频能出一条爆款吗"时调用。诊断模式("为什么不爆")走 references/diagnostic-mode.md。带货触发词:带货账号诊断、带货视频链接、带货素材、商品详情截图、SKU 表、商品资质、带货脚本、口播、分镜、商品卡一致性 — 走 commerce 路由。商品链接不等于视频链接;本 Skill 不解析在线商品页,不提供选品、趋势监控或 GMV/佣金推断。

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  77. Accessibility patterns for WCAG 2.2 compliance, keyboard focus management, React Aria component patterns, cognitive inclusion, native HTML-first philosophy, and user preference honoring. Use when implementing screen reader support, keyboard navigation, ARIA patterns, focus traps, accessible component libraries, reduced motion, or cognitive accessibility.

  78. Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.

  79. AI-assisted UI generation patterns for json-render, v0.app, Google Stitch, Bolt Cloud, and Cursor workflows. Covers prompt engineering for component and full-stack app generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.

  80. Queries local analytics across OrchestKit projects for agent usage, skill frequency, hook timing, team activity, session replay, cost estimation, and model delegation trends. Privacy-safe with hashed project IDs. Supports time-range filtering and comparative analysis. Use when reviewing performance, estimating costs, or understanding usage patterns.

  81. Animation and motion design patterns using Motion library (formerly Framer Motion) and View Transitions API. Use when implementing component animations, page transitions, micro-interactions, gesture-driven UIs, or ensuring motion accessibility with prefers-reduced-motion.

  82. API contract design for REST and GraphQL, covering resource shape, URL and header versioning with deprecation windows, RFC 9457 Problem Details error handling, and OpenAPI specs. Use when specifying the wire contract an endpoint exposes, choosing a versioning scheme, or standardizing error response bodies across services. Framework-agnostic protocol layer, not runtime implementation.

  83. ADR templates in the Nygard format with context, decision, consequences, and alternatives. Use when writing ADRs, recording an architectural decision, or evaluating options.

  84. Architecture validation and patterns for clean architecture, backend structure enforcement, project structure validation, test standards, and context-aware sizing. Use when designing system boundaries, enforcing layered architecture, validating project structure, defining test standards, or choosing the right architecture tier for project scope.

  85. ASCII diagram patterns for architecture, workflows, file trees, and data visualizations. Use when creating terminal-rendered diagrams, box-drawing layouts, progress bars, swimlanes, or blast radius visualizations.

  86. Assesses and rates quality 0-10 across multiple dimensions (correctness, maintainability, security, performance, testability, simplicity) with pros/cons analysis. Compares against project conventions and prior decisions from memory. Produces structured evaluation reports with actionable improvement suggestions. Use when evaluating code, designs, architectures, or comparing alternative approaches.

  87. Async job processing patterns for background tasks, Celery workflows, task scheduling, retry strategies, and distributed task execution. Use when implementing background job processing, task queues, or scheduled task systems.

  88. Audits OrchestKit sub-agent activation from real spawn telemetry — computes the generic-vs-specialist spawn split, flags dormant agents (never fired), and classifies each as fires/mis-triggered/niche. The agent-side analogue of audit-skills. Use when specialized agents feel under-used, before pruning the catalog, or after wiring new agent spawn paths.

  89. Single-pass codebase analysis leveraging a 1M-token context window for comprehensive security scanning, architecture review, and dependency auditing. Loads entire codebases for cross-file pattern detection and generates structured audit reports with severity-ranked findings. Use when you need whole-project analysis before releases or security reviews.

  90. Audits all OrchestKit skills for quality, completeness, and compliance with authoring standards. Use when checking skill health, before releases, or after bulk skill edits to surface SKILL.md files that are too long, have missing frontmatter, lack rules/references, or are unregistered in manifests.

  91. auto298

    Intent-classified router, the front door to OrchestKit and the DEFAULT entry point for any goal-shaped request. Classifies a plain-English goal and routes it to the right specialist skill. Routing is never overhead, so use it even when the target skill seems obvious; skip only when already executing inside another skill (no recursion). Triggers on: auto, do this, figure out, just make, I want, help me, fix, build, improve, any goal description.

  92. Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.

  93. Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring (0-10 across 7 dimensions), synthesis of top approaches, and trade-off comparison. Supports open exploration, constrained design, comparison, quick ideation, and iterative optimization modes. Use when brainstorming ideas, exploring solutions, or comparing alternatives.

  94. Security wrapper over the upstream agent-browser skill, adding URL blocklisting, rate limiting, robots.txt enforcement, and scraping guardrails. Use when automating browser workflows that need safety limits.

  95. Business case analysis with ROI, NPV, IRR, payback period, and TCO calculations for investment decisions. Use when building financial justification, cost-benefit analysis, build-vs-buy comparisons, or sensitivity analysis.

  96. Chain patterns for multi-phase pipelines: MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building or debugging a pipeline skill.

  97. Rate-limit-resilient pipeline with checkpoint/resume for long multi-phase sessions. Saves progress to .claude/pipeline-state.json after each phase. Use when starting a complex multi-phase task that risks hitting rate limits, when resuming an interrupted session, or when orchestrating work spanning commits, GitHub issues, and large file changes.

  98. Diagnose a failing CI run against an 11-pattern playbook. Classifies the failure, cites the relevant memory entry, proposes the exact fix command — but NEVER applies without explicit user approval. Use when a specific PR check or GitHub Actions run failed and you want a diagnosis instead of speculation. Don't use for org-wide CI sweeps (that's /status) or for app-level test failures (the playbook is CI-infra-specific).

  99. Daily autonomous classifier for failing PRs across your repos. Runs /ci-debug headless against every open PR with red required checks, posts the verdict as a collapsed PR comment, and appends to a per-repo .sentinel/ledger.jsonl. v1 is propose-don't-apply — NEVER auto-pushes a fix. Use when you're tired of /status sweeps catching the same 10 CI failure patterns over and over.

  100. Structured review processes, conventional comments, language-specific checklists, and feedback templates. Use when reviewing PRs, conducting code review, or standardizing review practice.