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Found 1,974 Skills
AI-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.
親イシュー配下のサブイシュー（孫含む）を依存順を保ちつつ worktree で並列に自動実装・push 前 review・PR 作成・CI 監視・マージ可能状態化まで一括自動化。 「イシューツリーを並列実装」「配下のサブイシューをまとめて実装」「ツリー全体を並列で実装して」「イシュー階層を自動開発」で使用。 per-issue 計画立案（Plan: セッション継承モデル）→実装（Implement: sonnet）の分業。push 前 review（Review 通過後にのみ push・PR 作成して CI を 1 回だけ起動）。 外部チェック構成は args の externalChecks で明示（[] で「なし」を確定して不要待機なし・未指定なら自動マージ停止）。 autoMerge: true + externalChecks 明示確定 + repo の auto-merge 許可 + base ブランチの required checks 設定時は、GitHub ネイティブ auto-merge（gh pr merge --auto --squash）を予約しマージまで自動完結する。前提未達は fail-closed で PR をマージ可能状態のまま停止し人間がマージ。並列度（parallel）と依存（dependsOn）で実行順を制御。 単一イシューの実装は implement-issue、PR レビューは implement-review-pr を参照。
Convert Markdown documents to professionally styled DOCX (Word) files with python-docx. Handles CJK/Latin mixed text, fenced code blocks, tables, blockquotes, cover pages, TOC field, watermarks, and page numbers. Supports multiple color themes matching any2pdf (Warm Academic, Nord, GitHub Light, etc.) and is battle-tested for Chinese technical reports. Use this skill whenever the user wants to turn a .md file into a styled Word document, generate an editable report from markdown, or create a DOCX from markdown content — especially if CJK characters, code blocks, or tables are involved. Also trigger when the user mentions "markdown to docx", "md2docx", "any2docx", "md转word", "md转docx", "生成word", or asks for an "editable document" from markdown source.
Research an Elixir/Phoenix topic on the web. Searches ElixirForum, HexDocs, blogs, and GitHub. Uses efficient markdown conversion.
Convert Markdown documents to professionally typeset PDF files with reportlab. Handles CJK/Latin mixed text, fenced code blocks, tables, blockquotes, cover pages, clickable TOC, PDF bookmarks, watermarks, and page numbers. Supports multiple color themes (Warm Academic, Nord, GitHub Light, Solarized, etc.) and is battle-tested for Chinese technical reports. Use this skill whenever the user wants to turn a .md file into a styled PDF, generate a report PDF from markdown, or create a print-ready document from markdown content — especially if CJK characters, code blocks, or tables are involved. Also trigger when the user mentions "markdown to PDF", "md2pdf", "any2pdf", "md转pdf", "报告生成", or asks for a "typeset" or "professionally formatted" PDF from markdown source.
Design environment strategy for testing across dev, CI, preview, staging, and production — Docker Compose test infrastructure, multi-stage Dockerfiles, seed-data lifecycle, per-PR preview environments, production parity, and external-dependency stubbing at the HTTP boundary. Use when: "set up test environment," "docker-compose for tests," "per-PR preview environment," "staging parity," "spin up test infra," "environment tiers." Not for: choosing mock-vs-stub-vs-fake per dependency — use service-virtualization; factory and fixture data patterns — use test-data-management; pipeline/Actions config — use ci-cd-integration. Related: test-data-management, ci-cd-integration, contract-testing, service-virtualization.
Rigorously evaluate an Agent Skill end-to-end across ANY coding-agent CLI — verify its scripts emit the documented numbers (deterministic checks), test whether its description triggers on the right prompts, and measure whether an agent following the SKILL.md beats a no-skill baseline (with/without pass-rate delta, mean ± stddev, benchmarked). Use whenever you need to test, benchmark, validate, grade, or quantify a skill's quality, check if a skill "actually works," compare two skill versions, optimize a skill's triggering, or set up an eval suite — even if the user just says "is this skill any good," "does my skill work," or "benchmark this skill." Drives Claude Code, OpenAI Codex, Antigravity (agy), Cursor, GitHub Copilot, Amp, opencode, or Grok in headless mode.
Use when a user wants to set up, configure, install, or reconfigure the opencode Fusion agent team - a strong main/build agent that plans and reviews but cannot edit files, delegating all edits to a cheaper sidekick subagent, plus an explore search agent and optional research/design/reviewer/vision specialists. Triggers include "set up fusion", "configure fusion", "install fusion", "fusion setup", "undo fusion" / "remove fusion", changing which models the main, sidekick, or explore agents use, or naming a subscription to start from a ready-made profile - e.g. "set up fusion with my OpenCode Go subscription" (also OpenCode Zen, ChatGPT Plus/Pro, GitHub Copilot). Writes the global opencode config under ~/.config/opencode/.
Report local Claude, Codex, Cursor, GitHub Copilot, Grok, and Kimi quota windows via the quota-axi CLI - remaining effective usable runway, percentages, reset times, cycle-average pace vs the reset clock, and provider status read from local auth sources, with no routing, provider mutation, or default ordering preference. Use before deciding whether it is safe to keep spending a provider's quota, when the user asks about usage, rate limits, pace, or remaining quota, or when comparing local provider headroom.
Deploy compatible server, static-web, worker, scheduled-job, or reviewed remote-desktop workloads from GitHub or local source to Sealos Cloud, then run the default Runtime Truth Pass against the returned App URL, public route, authentication flow, logs, database state, and full resource footprint. Reject unsupported desktop, mobile, CLI, library, extension, hardware-dependent, mixed, and unidentified targets before readiness scoring or build. Use when the user asks to deploy a repository to Sealos or another cloud platform, or invokes "/sealos-deploy".
This skill should be used when the user asks to "migrate to Buildkite", "convert pipelines from Jenkins", "convert GitHub Actions workflows", "convert CircleCI config", "convert Bitbucket Pipelines", "convert GitLab CI", "migrate CI/CD to Buildkite", "switch from Jenkins to Buildkite", "move from GitHub Actions", "plan a CI migration", "convert my CI config", "bk pipeline convert", or "what's the Buildkite equivalent of". Also use when the user mentions migration planning, CI conversion, pipeline conversion, converting workflows, or asks about translating CI/CD configuration from another provider to Buildkite.
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.