Agent Skill 目录与安装教程 — ClawHub、SkillsMP 与开源 Skill

io.github.sickn33/agentic-awesome-skills/huggingface-spaces

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants.

io.github.slopus/happy/release

Release pipeline for CLI, mobile, web, and server. Guides through version bumping, building, testing, publishing, and deploying. Replaces the old interactive release-it flow with a Claude Code-native experience. Use when user types /release or asks to release, publish, deploy, or ship any component.

io.github.code-yeongyu/oh-my-openagent/publish

Publish oh-my-opencode to npm by triggering the GitHub Actions publish workflow and verifying its artifacts. Ship-only: never runs pre-publish-review or re-reviews merged code unless the user explicitly asks. Argument: <patch|minor|major|explicit-semver>. Triggers: publish, release, deploy, npm publish.

io.github.addyosmani/agent-skills/shipping-and-launch

Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

io.github.bentoml/BentoML/bentoml-containerize

Build a local BentoML project into a Bento, containerize it into an OCI/Docker image, smoke-test it locally, and push it to a container registry (Docker Hub, GHCR, ECR, private registry, kind/minikube local load, or ttl.sh). Use when the user asks to "containerize a Bento", "build a Docker image for my BentoML service", "package my BentoML service for deployment", "push my Bento image to a registry", or as the first step of deploying BentoML to Kubernetes or EC2. Does NOT deploy anything itself — hand off to bentoml-k8s-deploy or bentoml-ec2-deploy for that.

io.github.netdata/netdata/docs-learn-site-structure

Authoritative reference for how docs in this repo (and 5 other Netdata-org repos) become published pages on `learn.netdata.cloud`. Covers the `<repo>/docs/.map/map.yaml` source-of-truth (the actual lever -- filesystem path is irrelevant for routing), the live `ingest/ingest.py` orchestrator in the learn repo (NOT the legacy `ingest.js`), frontmatter injection, slug rules, sidebar autogeneration, MDX escape rules, versioning, the 4-mechanism redirect stack, the 6 source repositories, the every-3-hours CI ingest, Netlify deploy, and the `part_of_learn=True` opt-in for files hand-authored in the learn repo. Use when adding/moving/renaming/deleting a docs page; when a page on Learn looks wrong; when wondering whether to edit a doc here or in the learn repo; when reading `ingest.py`, `sidebars.js`, `docusaurus.config.js`, `static.toml`, `LegacyLearnCorrelateLinksWithGHURLs.json`, `netlify.toml`, the `<!--startmeta` blocks in `.mdx` files, or the workflows `ingest.yml` and `daily-learn-link-check.yml`.

io.github.serverless/serverless/serverless-mcp

Host Model Context Protocol (MCP) servers on AWS with the Serverless Framework's built-in MCP support — an official-SDK server module deployed to AWS Lambda behind a streaming API Gateway REST endpoint, with gateway access control, OAuth protected-resource discovery, packaging and elicitation state handled for you. Use whenever the user wants to deploy, host, secure or debug an MCP server on AWS or Lambda, writes or edits an `mcp:` block in serverless.yml, exposes tools, resources or prompts to Claude or another AI client over HTTP, or hits an `MCP_*` configuration error (MCP_UNSUPPORTED_NODE_RUNTIME, MCP_FUNCTION_NAME_COLLISION, MCP_INVALID_STATE_ARN, and the rest). Trigger even when neither "Serverless Framework" nor "MCP" is named but the user describes putting their own tools in front of an AI client over an HTTPS endpoint.

io.github.netdata/netdata/learn-site-structure

Authoritative reference for how docs in this repo (and 5 other Netdata-org repos) become published pages on `learn.netdata.cloud`. Covers the `<repo>/docs/.map/map.yaml` source-of-truth (the actual lever -- filesystem path is irrelevant for routing), the live `ingest/ingest.py` orchestrator in the learn repo (NOT the legacy `ingest.js`), frontmatter injection, slug rules, sidebar autogeneration, MDX escape rules, versioning, the 4-mechanism redirect stack, the 6 source repositories, the every-3-hours CI ingest, Netlify deploy, and the `part_of_learn=True` opt-in for files hand-authored in the learn repo. Use when adding/moving/renaming/deleting a docs page; when a page on Learn looks wrong; when wondering whether to edit a doc here or in the learn repo; when reading `ingest.py`, `sidebars.js`, `docusaurus.config.js`, `static.toml`, `LegacyLearnCorrelateLinksWithGHURLs.json`, `netlify.toml`, the `<!--startmeta` blocks in `.mdx` files, or the workflows `ingest.yml` and `daily-learn-link-check.yml`.

io.github.twentyhq/twenty/manage-app

Use when the user wants to manage or troubleshoot tooling, remotes, sync, build, deploy, logs, CI/CD, or operational workflows for an existing Twenty app.

io.github.paperclipai/paperclip/paperclip-page

Publish static HTML pages and asset folders to the Paperclip S3/CloudFront page host. Use when asked to deploy, host, or share a persistent page, viewer, prototype, report, or static site without here.now.

io.github.ArchiveBox/ArchiveBox/archivebox

Use this when working on the ArchiveBox app, CLI, server, Docker image, Admin UI, REST API, data dirs, crawls, snapshots, and release/deploy scripts.

io.github.NousResearch/hermes-agent/fastmcp

Build, test, and deploy Python MCP servers.

io.github.affaan-m/ECC/laravel-verification

Verification loop for Laravel projects: env checks, linting, static analysis, tests with coverage, security scans, and deployment readiness. Use when verifying a Laravel project before merge or deploy — lint, static analysis, tests, coverage, security.

io.github.PostHog/posthog/investigating-logs

Investigate logs in a PostHog project: verify a service or deployment is healthy, explain an error spike, triage an incident, or understand what a log stream is saying. Use when the user asks to "check the logs", asks whether a service, deploy, release, or change is working or broke anything, asks why errors are up or what changed, or wants the root cause of failures visible in logs. Routes the logs MCP tools (services overview, pattern mining, before/after pattern diffing, bucketed counts, facets, raw rows) so investigations start from summaries instead of raw rows or hand-written SQL over the logs table.

io.github.Hmbown/CodeWhale/release

Prepare a named version: preflight, version consistency, build/package, smoke test, checksums/notes, and release readiness. Publishing/tagging/deploy need separate authorization. Explicit-only.

io.github.streamlit/streamlit/developing-with-streamlit

Use for ALL Streamlit tasks: creating, editing, debugging, beautifying, styling, theming, optimizing, or deploying Streamlit apps. Also custom components, st.components.v2, HTML/JS/CSS work. Discovers and loads version-matched reference docs from the user's installed Streamlit (>=1.57). Triggers: streamlit, st., dashboard, app.py, beautify, style, CSS, color, background, theme, button, widget styling, custom component, st.components, CCv2, session state, performance, cache, fragment, slow rerun, deploy.

io.github.microsoft/ai-agents-for-beginners/local-ai-agents

Bumuo ng mga local-first AI agents na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry Local at Qwen function-calling models. Saklaw nito ang Small Language Models (SLMs), ang OpenAI-compatible na lokal na endpoint, sandboxed local tools, lokal na RAG gamit ang Chroma, lokal na MCP servers, hybrid cloud/local routing, at ang privacy/cost/offline trade-offs. Batay sa Lesson 17 ng AI Agents for Beginners. GAMITIN PARA SA: pagpapatakbo ng agent nang lokal, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, lokal na RAG, Chroma vector database, lokal na MCP server, privacy-preserving agent, hybrid local at cloud agent, small language model agent, engineering assistant sa aking makina. HUWAG GAMITIN PARA SA: pag-deploy ng mga agents sa cloud nang malakihan (gamitin ang deploying-scalable-agents / Lesson 16), paggawa ng iyong unang agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.

io.github.microsoft/ai-agents-for-beginners/azure-openai-to-responses

Ilipat ang mga Python app mula sa Azure OpenAI Chat Completions papuntang Responses API. Saklaw nito ang pag-migrate ng AzureOpenAI/AsyncAzureOpenAI client sa v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, at mga pagsusuri sa compatibility ng modelo. Nakatuon sa Python, para sa Azure OpenAI. GAMITIN PARA SA: pag-migrate sa responses API, paglipat mula sa chat completions, openai responses, pag-upgrade ng openai SDK, migration sa responses API, paglipat mula completions sa responses, gpt-5 migration, azure openai python migration, chat completions papuntang responses, AzureOpenAI papuntang OpenAI client, python azure openai upgrade. HUWAG GAMITIN PARA SA: paggawa ng mga bagong app mula sa simula (simulan direkta sa responses), Node/TypeScript/C#/Java/Go migrations (Python lang ang kasanayang ito), Azure infrastructure setup (gumamit ng azure-prepare), pag-deploy ng mga modelo (gumamit ng microsoft-foundry).

io.github.microsoft/ai-agents-for-beginners/azure-openai-to-responses

Shift Python apps dem from Azure OpenAI Chat Completions go Responses API. E cover AzureOpenAI/AsyncAzureOpenAI client shift go v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, plus model compatibility checks. Na Python-focused, Azure OpenAI-specific. USE FOR: shift go responses API, change from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions go responses, gpt-5 migration, azure openai python migration, chat completions go responses, AzureOpenAI go OpenAI client, python azure openai upgrade. DO NOT USE FOR: build new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (dis skill na Python-only), Azure infrastructure setup (use azure-prepare), deploy models (use microsoft-foundry).

io.github.calesthio/OpenMontage/hyperframes-cli

HyperFrames CLI dev loop. Use when running npx hyperframes init, add, catalog, capture, lint, validate, inspect, layout, snapshot, preview, play, render, publish, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, tts, or remove-background, or when troubleshooting the HyperFrames build/render environment. Entry point for AWS Lambda cloud rendering (`hyperframes lambda deploy / render / progress / destroy / policies`).

io.github.TheDecipherist/claude-code-mastery-project-starter-kit/docker-swarm

Production Docker Swarm deployment rules: what changes when a compose file goes from a single node to a multi-node Swarm. Use when writing or reviewing a stack file, a deploy block, an overlay network, or anything deployed with docker stack deploy. Covers the directives Swarm silently ignores, why fixed IPs and bind mounts break, the deploy orchestration block, and the exit-code and healthcheck discipline that Swarm self-healing depends on. Complements the docker skill, which covers writing the image and compose file itself.

io.github.abiosoft/colima/skills

Guide to using Colima — container runtimes (Docker, containerd, Kubernetes, Incus) on macOS and Linux via lightweight Lima VMs. Use this skill whenever Colima is (or should be) the container backend: installing Colima; `colima start/stop/status/delete/ssh`; picking or switching a runtime; the `Cannot connect to the Docker daemon at unix:///var/run/docker.sock` error; Docker contexts and socket location; registry mirrors / insecure registries; buildx; bind or volume mounts that show up empty in the container; disk space recovery/resize; reachable VM IP; GPU / AI model workloads; config files, profiles and `COLIMA_HOME`; or a Colima VM that won't start. ALSO use it for **writing scripts that drive Colima** — bootstrap, dev-env, deploy, or CI scripts that bring Colima up non-interactively — because the skill has the correct flags, profile-specific socket paths, idempotent `colima start` guards, and readiness/teardown patterns that hand-written scripts routinely get wrong (e.g. inventing a non-existent flag or ha