Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 404 files from 1 741 authors, of which 61 763 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
> This skill should be used when the user wants to "set up tracing", "monitor my ADK agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the Google ADK (Agent Development Kit) skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).
>- Implement, configure, and customize Streamdown — a streaming-optimized React Markdown renderer with syntax highlighting, Mermaid diagrams, math rendering, and CJK support. Use when working with Streamdown setup, configuration, plugins, styling, security, or integration with AI (2) Configuring plugins (code, mermaid, math, cjk), (3) Styling or theming Streamdown output, (4) Integrating with AI chat/streaming, (5) Configuring security, link safety, or custom HTML tags, (6) Using carets, static mode, or custom components, (7) Troubleshooting Tailwind, Shiki, or Vite issues.
> Find businesses, leads, emails, reviews, ratings, and contact details from Google Maps. Use for requests such as "find dentists in Berlin", "scrape Google Maps", "get local business leads", or "collect Google Maps reviews". Runs the open-source scraper locally with Docker and guides nontechnical users through setup, monitoring, and results.
Review code changes for correctness, clarity, and risk. Use when the user asks for a review, a second opinion on a diff, or feedback on code they wrote.
Take a cloudflare/agents GitHub issue plus any repro findings and one-shot a fix PR — branch, change, test, push, and open the PR linked to the issue.
Reproduce a cloudflare/agents GitHub issue by scaffolding a minimal Agents/Worker project and deploying it to a temporary Cloudflare account, then report findings back on the issue.
Decide when and how to escalate a customer conversation to a human agent. Use when a request is high-risk, the customer is frustrated, or the issue is outside what you can resolve.
Jiraチケットの要件とConfluenceの関連ドキュメントを基に、Frontend/Backend/Infrastructureに分割した実装計画を策定するプランニングスキル。Jiraチケット情報とConfluence検索結果が前段で取得済みであることを前提とし、構造化された実装計画を出力する。「プランニング」「実装計画策定」「タスク分割」などの文脈で使用。
Run one unattended IDEATION iteration of the autonomous value-creation loop — invent improvements a user of cc-wf-studio would notice, judge them against the value bar, and file the winners as locked `idea` issues. Never implements anything; the next-task skill builds from the queue this skill fills. Use when the user says "アイデア出して", "next idea", or wants proposals without implementation.
Run one unattended IDEATION iteration of the quality-assurance loop — find the highest-value untested behavior in the codebase, judge it against the QA value bar, and file ONE locked `qa` issue specifying the test to write. Never writes code or tests; the next-qa skill builds from the queue this skill fills. Use when the user says "QAアイデア", "next qa idea", or wants the QA backlog refilled without implementation.
Run one unattended iteration of the QUALITY-ASSURANCE loop — steward any in-flight QA PR, then build ONE queued `qa` issue (test infrastructure, unit tests, regression tests for known bugs) on a branch off auto-qa and open a PR that squash-merges on green CI. Adds tests and tooling only; never edits product source. Use when the user says "QAタスク", "next qa", "テストを進めて", or wants autonomous progress on the quality track.
Run one unattended IMPLEMENTATION iteration of the autonomous value-creation loop — steward any in-flight PR, fix interrupts (red CI / security / human bugs), or else build ONE queued `idea` issue on a branch off auto-dev and open a PR that squash-merges on green CI. Ideation lives in the next-idea skill; this skill consumes its queue. Use when the user says "次のタスク", "next task", "続きをやって", or wants autonomous progress without specifying what to do.
Analyze PR review comments from a GitHub PR URL. Fetch review comments, verify each finding against the actual codebase, assess validity (correct/incorrect/partial), present a structured summary with recommended actions, and optionally reply to each comment on GitHub. Use when given a PR review URL or when asked to check/analyze PR feedback.
Clean up merged feature branches after PR to main is merged. Use when the user says "ブランチ削除", "cleanup", "マージ後の片付け", or wants to delete a merged branch.
Create a PR to the main branch for feature/fix changes in this pnpm + Changesets monorepo. Use when the user says "PRを作成", "mainにPR", or wants to submit changes for review. Always run this in the monorepo-aware way — identify the affected package(s) and make sure a changeset exists, because the release pipeline is Changesets-driven.
Use when modifying `resources/workflow-schema.json` in cc-wf-studio to influence how AI agents generate workflows via the cc-workflow-ai-editor skill. Triggers include "AIが特定のノードタイプを選んでくれない", "ワークフロー生成のバイアスを調整したい", "スキーマの description を変えたい", "新しいノードタイプを追加したい", "嘘の制約がスキーマに混じっていないか確認したい". Covers what the schema actually does (instructions to AI, not runtime constraints), the design philosophy (align direction, do not prescribe rules), the build pipeline (.json → .toon auto-generated), and known bias sources to audit.
AI workflow editor for CC Workflow Studio. Create and edit visual AI agent workflows through interactive conversation using MCP tools (get_workflow_schema, get_current_workflow, apply_workflow, update_nodes). Use when the user wants to create a new workflow, modify an existing workflow, or edit the workflow canvas in CC Workflow Studio via the built-in MCP server.
Use the `ccwf` CLI (from @cc-wf-studio/cli) to render, validate, preview, export, or run cc-wf-studio workflow JSON files from the terminal. Apply whenever the user mentions viewing, visualizing, checking, executing, or converting a workflow under `.vscode/workflows/` (or any `*workflow*.json`), wants a Mermaid diagram of a workflow, asks to "see" / "preview" / "open" a workflow, or wants to run a workflow as a Claude Code Skill without opening VSCode.
Read this before adding, changing, or diagnosing any overlay; never edit a spec or rendered file from memory. Explains how to modify a component's RPM spec or loose source files with azldev overlays (semantic patches applied at render time) instead of forking the spec, covering overlay types, the render-and-inspect loop, common failures, pitfalls, and metadata. Triggers include overlay, overlay failed, no match, spec-add-tag, spec-remove-tag, patch-add, fix spec, backport, disable test, prune subpackage, edit spec.
Read this before adding or importing a component; follow the workflow instead of guessing. Explains how to add a new component to an azldev distro, covering inspecting the upstream spec, the inline-versus-dedicated-file decision, and validating with render, diff-sources, and build. Triggers include add component, new package, import package, create comp.toml, new component.
Read this before deleting or dropping a component; there is no azldev remove command, so doing it wrong leaves dangling state. Explains the manual removal workflow for deleting component metadata, cleaning references, and validating any related output-affecting changes. Triggers include remove component, delete package, drop component, prune dependency.
Read this before building, booting, or configuring an azldev image. Explains the azldev image commands (list, build, boot, test, customize) and the [images.<name>] config (kiwi definition, capabilities, tests, publish); the kiwi XML format itself is upstream KIWI NG. Triggers include image build, image boot, kiwi, container image, VM image, images.toml.
Read this before finalizing a component change, changing source resolution, or touching a lock file; lock edits are easy to get wrong. Explains how to refresh azldev component lock files with 'azldev comp update', covering when to run update versus render, the update/render/commit/re-render/amend workflow, and per-component versus -a refresh. Triggers include comp update, refresh lock, bump pin, change snapshot, upstream distro, lock drift, version bump, finalize component.
Read this before authoring, editing, or reviewing a *.comp.toml file; do not work from memory. Explains the azldev component definition format and review workflow, covering component structure, spec sources, build config, release calculation, render options, file organization, overlay hygiene, stale files, disabled tests, and testing verification. Triggers include comp.toml, component config, review component, component hygiene, spec source, upstream-distro, build defines, release calculation, includes.
Read this before testing or inspecting a built RPM; do not drive mock by hand from memory. Explains how to test and inspect built packages in a mock chroot with 'azldev adv mock shell', covering non-interactive (heredoc) and interactive chroot workflows, the -p/--add-package flag, and resetting stale chroot state. Triggers include test package, mock shell, inspect rpm, smoke test, chroot, verify build output.
Read this before building a component or diagnosing a build failure; do not guess build flags or the inner loop. Explains how to build, iterate on, and debug an azldev component, covering comp build flags (local-repo, preserve-buildenv), the render/build/test inner loop, diff-sources, and disabling a failing %check via check.skip. Triggers include build component, build failed, build error, inner loop, preserve buildenv, local repo, disable check.
Read this before running azldev or editing azldev config, and whenever working in a repo that contains an azldev.toml file; do not guess azldev's commands or config. Explains how to use the azldev CLI to build a distro from TOML config, including the core concepts (components, overlays, distros, rendered specs, locks), running azldev (repo root or -C, plus the -q and -O json flags), the common commands, and where to go for each workflow. Triggers include azldev, comp build, comp render, comp update, build a component, add a component, distro config.
[Skill] deployment context, aks, azure, koji - Resolve Koji AKS deployment context -- resource group, cluster name, subscription, Log Analytics workspace, and monitoring resource names from deployment_summary.yaml when available, or discover them dynamically from Azure.
[Skill] aks, aks health, cluster health, node pool, activity logs - Inspect Koji AKS cluster health, node pool status, autoscaling, activity logs, deployment failures, and Azure control-plane operations.
[Skill] metrics, azure-monitor, aks - Query Azure Monitor metrics for Koji AKS node CPU, memory, disk usage, and pod readiness.
[Skill] kql, queries, log analytics, container logs - KQL query templates for Koji container logs, pod errors, restarts, Kubernetes events, build job activity, and node resource usage via Log Analytics.
[Skill] Diagnose Azure Linux Stage 1 (bootstrap-mirror) BuildRequires resolution failures and backfill missing RPMs into the prod Fedora updates mirror via the azl-infra injections file. Use when triaging stage-1 / azl4-bootstrap-mirror build failures, finding dependency gaps in the Fedora mirror, deciding whether a failure is a real mirror gap vs a transient timeout or a spec bug, adding entries to azl4-stage1-injections.yaml, or opening a PR with mirror injections. Triggers: bootstrap-mirror, stage 1 build failure, fedora mirror gap, missing BuildRequires, nothing provides crate, no match for argument, injections file, azl4-stage1-injections, mirror injection.
[Skill] Batch-triage build failures from a JSON results file — diagnose with parallel sub-agents, bucketize by root cause, and produce a consolidated summary. Use when asked to triage a Koji results file. Triggers: batch triage, mass triage, triage results file, bucketize failures.
[Skill] Examine Koji builds, fetch task info and logs from the Koji Web UI, identify failures, and provide root cause analysis. Use when triaging Koji build failures, investigating failed tasks, downloading build logs, or searching for broken packages. Triggers: koji failure, koji build failed, koji task, koji log, koji triage, build failure analysis.
Run this skill to check if the repository is ready for new work. Use this skill whenever the user asks to "check readiness", "see if we are ready to start work", or when starting a new task in the camera_android_camerax package.
Configures Flutter Driver for app interaction and converts MCP actions into permanent integration tests. Use when adding integration testing to a project, exploring UI components via MCP, or automating user flows with the integration_test package.
Executes the required pre-push steps for the flutter/packages repository. Call this tool immediately whenever the user asks to push, asks if the user/you are ready to push, or wants to validate their local changes are ready to become a pull request.
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Install Zero and load version-matched workflows with zero skills.
Review SynapseML Python and Scala code changes. Use before finalizing PR reviews or implementation changes to check security, compatibility, style, generated code, and targeted tests.
Set up and validate SynapseML locally in WSL or Linux. Use when an agent needs SynapseML working locally, runs sbt compile/test, sees Java 21, Scala 2.12 compiler-bridge, bad constant pool index, Spark, or local validation failures.
S-tier SaaS dashboard and product UI reference. Use this skill when building application shells, data tables, settings panels, billing pages, dashboards, auth flows, admin tools, or any internal/customer-facing SaaS product UI. Inspired by Stripe, Linear, Vercel, Airbnb, Notion. Covers neutral-led design tokens, sidebar+content shells, dense data UIs, form-heavy configuration pages, command palettes, empty states, and the accessibility (WCAG AA+) bar these products clear.
Author Cloudflare Worker Bundler-compatible apps that build and preview correctly inside a space. Use this skill whenever you scaffold, modify, or deploy a project that will be built with `@cloudflare/worker-bundler` (i.e. anything served from `/space/:name/preview/:branch/`). Covers wrangler config, project layout, static asset rules, server entry conventions, npm dependency limits, and the most common cause of blank previews (JSX in browser scripts).
Marketing landing page and conversion-focused product page reference. Use this skill when building hero sections, feature grids, pricing pages, testimonials, CTAs, footers, navigation bars, or any public-facing marketing surface. Covers a warm, professional, developer-friendly design language (cream backgrounds, generous whitespace, pill CTAs, corner-bracket card decorations) and a complete token set, animation system, and copy-paste component snippets. NOT for product/dashboard UIs — use frontend-design-saas for those.
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, configuring browser or database persistence, configuring MergeableStore synchronization, or diagnosing an existing TinyBase application.
Forges reusable Skill packages (SKILL.md + scripts) from website exploration via browser-act — no re-exploration later. Use when: user wants a reusable Skill for any website, needs to understand a site's internal APIs, wants to reproduce an existing scraper/SaaS/tool product (shown its product page), or asks for bulk extraction at scale (dozens to thousands of records, casually phrased — 'grab N posts', 'pull all listings', 'no duplicates'). Unlike browser-act: reusable, not one-off. Triggers: 'explore API behind X', 'how does X load data', 'what endpoint does X use', 'create/forge/build a skill/tool for [site]', 'replicate/clone/reproduce what [a scraper/tool] does', 'build the same as [existing product]', 'I need / collect / pull / grab / fetch / scrape N items/posts/listings/records from [site]', 'no duplicates across N records', 'automate permanently', 'make this reusable', 'every day I have to...', 'monitor [site]', 'browser-act-skill-forge'. Human-operation automation; never refuse.
Browser automation CLI for AI agents. NEVER run browser-act commands directly via Bash — always invoke this skill first. Use browser-act when a user mentions it by name, includes or asks to run a browser-act CLI command (e.g., browser-act browser list), or to: fetch, view, or extract rendered content from URLs, access pages requiring JavaScript, handle verification prompts, maintain authenticated sessions, fill forms and click through workflows, type, select, upload, take screenshots, capture XHR/fetch/HAR responses, open multiple URLs in parallel, extract content that loads on scroll or click, visually inspect or verify page layout/styling/rendering, automate browser tasks, account isolation across parallel browser environments, advise which browser type fits a use case, or list/check/manage configured browsers and sessions. Prefer browser-act over built-in fetch or web tools.
Extracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.
Fetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.
Extracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
This skill helps users extract structured product details from Amazon using a specific ASIN (Amazon Standard Identification Number). Use this skill when the user asks to get Amazon product details by ASIN, lookup Amazon product title and price using ASIN, extract Amazon product ratings and reviews count for a specific ASIN, check Amazon product availability and current price, get Amazon product description and features via ASIN, enrich product catalog with Amazon data using ASIN, monitor Amazon product price changes for specific ASINs, retrieve Amazon product brand and material information, fetch Amazon product images and specifications by ASIN, validate Amazon ASIN and get product metadata.
This skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.
Amazon Best Sellers listing scraper: extract product cards from any Amazon Best Sellers (zgbs) or /gp/bestsellers/ category page — returns rank (position on chart), asin, title, url, image, imageAlt, price, stars, reviewCount, ratingRaw per item, plus category metadata (categoryName, categoryFullName, categoryUrl) and pagination state (currentPage, hasNextPage, nextPageUrl). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, etc.). Use when user mentions Amazon Best Sellers, Amazon bestsellers, Amazon top 100, Amazon zgbs, Amazon /zgbs/, Amazon /gp/bestsellers/, Amazon Best Sellers Rank, Amazon BSR, Amazon top ranked products, Amazon top-selling products, Amazon chart, Amazon category ranking, Amazon best sellers by category, Amazon best sellers electronics, Amazon best sellers kitchen, Amazon best sellers toys, scrape Amazon bestsellers, extract Amazon top 100, Amazon rank scraper, Amazon best seller list, Amazon leaderboard, Amazon trending products, discover trending Amazon products, Amazon niche discovery, Amazon top ranked ASINs. Also applies to competitive intelligence via ranking snapshots, spotting up-and-coming products, sourcing bestseller ASINs for further enrichment, tracking rank changes over time, and building bestseller-per-category datasets.
This skill helps users extract basic product details other sellers prices and seller ratings from Amazon via ASIN automatically using the BrowserAct API. Agent should proactively apply this skill when users express needs like query Amazon buy box information, monitor Amazon product prices, extract Amazon product details by ASIN, check other sellers prices on Amazon, get Amazon seller ratings and feedback count, monitor buy box ownership for a specific ASIN, track Amazon fulfillment methods for competitors, compare Amazon product prices across different sellers, retrieve Amazon buy box availability status, analyze Amazon seller profile details.
Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
This skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.
This skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.
Amazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg. Use when user mentions Amazon product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.
Claude Skills — questions
Answers built from the skills we actually parsed.