FastGPT Docker 自托管部署
io.github.labring/FastGPT/deploy
FastGPT 的 Docker Compose 自托管部署流程。当用户要求 AI 智能体参照 /deploy/SKILL.md 用 Docker 部署 FastGPT 时使用,包括创建空的 fastgpt 目录、以非交互模式运行安装脚本、启动 Docker Compose、监控服务健康、排查 compose 问题,并返回访问地址、root 账号凭据与后续步骤。
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io.github.labring/FastGPT/deploy
FastGPT 的 Docker Compose 自托管部署流程。当用户要求 AI 智能体参照 /deploy/SKILL.md 用 Docker 部署 FastGPT 时使用,包括创建空的 fastgpt 目录、以非交互模式运行安装脚本、启动 Docker Compose、监控服务健康、排查 compose 问题,并返回访问地址、root 账号凭据与后续步骤。
io.github.streamlit/streamlit/developing-with-streamlit
**[必备]** 适用于所有 Streamlit 任务:创建、编辑、调试、美化、样式、主题或优化 Streamlit 应用。构建自定义组件(内联或打包)、使用 st.components.v2 或任何 HTML/JS/CSS 组件工作时同样必需。触发词:streamlit、st.、dashboard、app.py、beautify、style、CSS、color、background、theme、button、widget styling、custom component、st.components、packaged component、pyproject.toml、asset_dir、CCv2、HTML/JS component。
io.github.heygen-com/hyperframes/pr-to-video
把 GitHub pull request(PR 链接、owner/repo#N,或在已检出仓库中指“这个 PR”)做成代码变更讲解视频——基于 diff、commits 与文件构建的更新日志、功能揭示、修复或重构讲解:输入是代码变更而不是网站。不是产品宣传片(/product-launch-video),也不是无 PR 的主题讲解(/faceless-explainer)。不确定时先用 /hyperframes。
io.github.langgenius/dify/how-to-write-component
在实现或重构 React/TypeScript 组件、且任务需要就组件归属、功能边界、状态、数据流、副作用或交互归属做决策时使用。不适用于仅审查、仅测试、仅改文案或仅改样式的请求。
io.github.langgenius/dify/frontend-code-review
仅在用户明确要求审查 web/ 或 packages/dify-ui/ 下的前端代码时使用。支持待提交变更、按文件和粘贴 diff 三种审查模式。不适用于仅实现代码的请求、无审查意图的诊断、或纯后端代码。
io.github.RSSNext/Folo/release-promo
Announcement kit for a Folo release - the X post with the highlights, its reply with the release links, a one-sentence quote line for the second account, and a 1080p promo video rendered from real app captures. The desktop and mobile release skills end with it; also use it when the user asks for promo material for a release.
io.github.cathrynlavery/diagram-design/diagram-design
Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.
io.github.lobehub/lobehub/run-eval-harbor
Run and diagnose existing Harbor or FrontierHarness evaluations against local production LobeHub or LobeHub Cloud, on a local Docker host, SSH worker, or Runta. Use for eval infrastructure, target preflight, lh CLI injection, Harbor smoke or job runs, resume, and failure triage. Excludes authoring Harbor tasks and product acceptance.
io.github.obra/superpowers/requesting-code-review
在完成任务、实现重要功能或合并前使用,以验证工作符合要求。
io.github.angular/angular/angular-developer
Generates Angular code and provides architectural guidance. Trigger when creating projects, components, services, or HTTP communication, or for best practices on reactivity (signals, linkedSignal, resource, httpResource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, naming conventions, or CLI tooling.
io.github.affaan-m/ECC/token-card
Render a token-usage stat card from local Claude Code, Codex, Gemini CLI, and OpenCode session logs and commit it into the repository as a file. Use when someone wants a current usage figure in a README, or asks for a shareable card of their coding-agent activity.
io.github.affaan-m/ECC/prompt-caching-strategy
Optimize LLM prompt caching hit rate to reduce API costs and improve latency. Analyzes prompt structure, identifies cacheable vs non-cacheable content, and recommends restructuring to maximize cache reuse. TRIGGER when: user says "prompt cache", "cache hit rate", "reduce LLM cost", "optimize token usage", "cheaper LLM calls", or asks about prompt caching strategies for OpenAI/Anthropic/other providers. DO NOT TRIGGER when: user just wants to optimize a single prompt's quality (use prompt-optimizer instead), or asks about general caching infrastructure.
io.github.morluto/rea/rea-tool-design
Design or change REA investigation tools, CLI/MCP contracts, provider capabilities, and Evidence semantics.
io.github.affaan-m/ECC/conventional-commit-review
Reviews staged changes and drafts a Conventional Commits-compliant commit message with the right type, scope, and length. Use before committing, or when the user asks to write, fix, or review a commit message.
io.github.affaan-m/ECC/agent-security-hardening
Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.
io.github.OpenHands/OpenHands/local-stack-runtime
This skill should be used when the user asks to "change the dev stack", "add a runtime service", "change the launcher", "update Docker", "bump Agent Server", "change ingress routing", or changes scripts/dev-*.mjs, runtime-services metadata, config/defaults.json, Docker, automation startup, process shutdown, or local authentication.
io.github.affaan-m/ECC/agent-payment-x402
为 AI Agent 加入 x402 支付执行能力,支持按任务预算、支出管控与非托管钱包。通过 agentwallet-sdk 支持 Base,通过 OKX Payments / OKX Agent Payments Protocol 支持 X Layer。当 Agent 需要自行付费,并需要按任务预算、支出管控与非托管钱包时使用。
io.github.affaan-m/ECC/agent-payment-x402
将 x402 支付执行添加到 AI 代理中——通过 MCP 工具实现每任务预算、支出控制和非托管钱包。当代理需要为 API、服务或其他代理付费时使用。
io.github.affaan-m/ECC/agent-payment-x402
让 AI 代理执行 x402 支付:按任务限额、支出控制、非托管钱包。通过 agentwallet-sdk 支持 Base,通过 OKX Payments / OKX 代理支付协议支持 X Layer。
io.github.affaan-m/ECC/hookify-rules
Create and configure hookify rules — markdown files with YAML frontmatter that match bash, file, prompt, or stop events by regex or conditions and show warn/block messages to the agent. Use when creating a hookify rule, writing hook rule syntax, configuring hookify, or adding pattern guardrails such as blocking dangerous commands, .env edits, or debug code.
io.github.anthropics/skills/claude-api
Claude API / Anthropic SDK 参考——模型 id、定价、参数、流式输出、工具调用、MCP、agent、缓存、token 计数、模型迁移。触发条件:提示中出现任何 Claude/Anthropic 相关名称(Claude、Anthropic、Fable、Opus、Sonnet、Haiku 等);用户询问 LLM 相关问题(定价/选型/限额/缓存)时不要凭记忆回答;或任务属于 LLM 类且未指明供应商。仅当明确针对其他供应商(OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama)时跳过。
io.github.google/skills/gke-ai-troubleshooting-tpu-performance-degradation
Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (`gcloud alpha mldiagnostics monitored-events` / `hypercomputecluster.googleapis.com/v1alpha`) and 1-minute Cloud Monitoring system metrics (`kubernetes.io/node/accelerator/*`). Distinguishes hardware and network fabric throttling from workload resource bottlenecks (HBM capacity, host memory, or host CPU saturation). Use when TPU training throughput or duty cycle drops without crashing pods, when `PERFORMANCE_DEGRADATION` monitored events fire, or when triaging slow multi-slice training steps. Don't use for complete multi-slice XLA execution stalls with `HANG_DETECTED` logs (use gke-ai-troubleshooting-tpu-mxla-hang) or pod eviction/interruption restarts (use gke-ai-troubleshooting-jobset-interruption).
io.github.heygen-com/hyperframes/hyperframes-animation
HyperFrames 全部动画知识——原子运动规则、多阶段场景蓝图、场景转场、更宽泛的 motion design 技巧,以及七种运行时适配器(默认 GSAP,另有 Lottie、Three.js、Anime.js、CSS keyframes、Web Animations API、TypeGPU)。适用于任何运动或动画任务:挑选 2-4 条规则进行组合,或加载蓝图,或查询运行时专属 API(如 GSAP eases、Lottie 播放器、Three.js mixer)。还可审计现有合成的动作编排(animation map)与 24 种命名文字动画效果。HyperFrames 原生特性:单一暂停时间轴、seek-safe、确定性。
io.github.vercel/next.js/next-bundle-optimizer
Audit and reduce Next.js browser initial-load work. Use for slow route startup, oversized client bundles, duplicate browser dependencies, or features that can wait for interaction.