Development Claude Skills
8 676 development skills from 759 authors. They write and change code. Half of them fit into 1 830 tokens or less — that is what one costs your context window when the agent loads it. 1 213 ship runnable scripts rather than instructions alone. 42 of them cannot work without an MCP server, most often rube. We also found 1 172 copies of these same skills sitting in other people's repositories — counted once here, not 1 172 times.
8 676 unique 759 authors 5 250 updated this month 1 369 from vendors
Stamp a bounded project, component, or CI scaffold and verify the generated result once. Triggers: "scaffold", "create project component or boilerplate".
Run authorized repository security scans for vulnerabilities, dependency risk, secrets, and binary policy. Triggers: "security", "run repository security scans for", "security skill".
Create a metadata-complete AgentOps skill source package, regenerate its derived projections, and check or repair structural hygiene in skill packages. Triggers: "create a skill", "scaffold skill", "absorb external skill", "new skill", "heal skill", "repair skill hygiene", "audit skill structure", "check skill package".
Scaffold an explicit one-shot workflow adapter without lifecycle authority. Triggers: "build a workflow adapter", "scaffold a one-shot workflow".
Builds Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use when building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface - creating an Airflow plugin, adding a custom UI page or nav entry, building FastAPI-backed endpoints inside Airflow, serving static assets from a plugin, embedding a React app, adding middleware to the API server, creating custom operator extra links, or calling the Airflow REST API from inside a plugin; also when AirflowPlugin, fastapi_apps, external_views, react_apps, or plugin registration come up.
Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across Airflow 2.x and 3.x.
Builds human-in-the-loop (HITL) Airflow workflows - approval gates, form input, and human-driven branching. Use when a DAG needs a human in the loop - an approval or reject step, sign-off before a task runs, a decision or approval UI, branching on a human choice, or collecting form input mid-run; also on mentions of ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, or HITLTrigger. Requires Airflow 3.1+. Not for AI/LLM task calls (see migrating-ai-sdk-to-common-ai).
Writes Airflow task logic in Go using the Airflow Go SDK. Use when the user wants to implement Airflow tasks in Go, asks about `BundleProvider`/`RegisterDags`, the `bundlev1` Registry/Dag interfaces, registering Go tasks (`AddTask`/`AddTaskWithName`), dependency injection by parameter type (`context.Context`, `sdk.TIRunContext`, `*slog.Logger`, `sdk.Client`), or reading connections/variables/XComs from Go. This skill covers the Go-specific native API; the shared Python-stub pattern and conceptual model live in authoring-language-sdk-tasks. For building/packing/shipping the bundle see deploying-go-sdk-bundles; for coordinator config see configuring-airflow-language-sdks.
Writes Airflow task logic in Java, Kotlin, or any JVM language using the Airflow Java SDK. Use when the user wants to implement Airflow tasks in Java/JVM, asks about `@Builder.Dag`/`@Builder.Task`/`@Builder.XCom`, the `Task`/`BundleBuilder` interfaces, reading connections/variables/XComs from Java, the JSON-to-Java type mapping, or logging from Java tasks. This skill covers the Java-specific native API; the shared Python-stub pattern and conceptual model live in authoring-language-sdk-tasks. For building/shipping the bundle see deploying-java-sdk-bundles; for coordinator config see configuring-airflow-language-sdks.
The language-neutral foundation for Airflow language SDKs — implement task logic in a non-Python language while the DAG stays in Python. Use when the user wants to run an Airflow task in another language (Java, Kotlin, Go, or other JVM/native languages), asks how the Python `@task.stub` pairs with native task code, how task/DAG IDs must match across the two sides, how data passes via XCom as JSON, or which language SDKs exist. This skill owns the shared Python-stub pattern and conceptual model; for a specific language's native API, build, and runtime, use that language's skill (e.g. authoring-java-sdk-tasks, authoring-go-sdk-tasks).
Configures Airflow to run language SDK tasks (Java, Go, and future native SDKs) — register a coordinator, map a queue to it, ensure the runtime/artifact on workers, and tune coordinator options. Use when the user wants Airflow to route a queue to a native-language coordinator, asks about the `[sdk]` `coordinators`/`queue_to_coordinator` settings, `AIRFLOW__SDK__COORDINATORS`, `jars_root`, `executables_root` or other coordinator `kwargs`, `task_startup_timeout`, or why their native tasks aren't being picked up. Covers the shared routing mechanism plus per-coordinator options (e.g. JavaCoordinator, ExecutableCoordinator).
Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.
Builds, packs, and deploys compiled Airflow Go SDK bundles so the ExecutableCoordinator can run them. Use when the user wants to compile a Go task bundle, asks about `go build`, `go tool airflow-go-pack`, the AFBNDL01 self-contained executable bundle, packing or inspecting a bundle, placing it under `executables_root`, cross-compiling a bundle for workers, `go-sdk` module versioning/tags/pseudo-versions, or getting the bundle onto an Airflow worker (Docker, Kubernetes, or Astro). For the task code see authoring-go-sdk-tasks; for the shared coordinator settings see configuring-airflow-language-sdks.
Builds and deploys compiled Airflow Java SDK bundles so workers can run them. Use when the user wants to package a JVM task bundle into a JAR, asks about the `org.apache.airflow.sdk` Gradle plugin, `./gradlew bundle`, the Maven shade/BOM setup, fat vs thin JARs, the logging integration artifacts (JPL, SLF4J, Log4j 2, JUL), preview/snapshot builds, or getting the JAR onto an Airflow worker (Docker, Kubernetes, or Astro). For the task code see authoring-java-sdk-tasks; for the Airflow coordinator settings see configuring-airflow-language-sdks.
Migrates Airflow projects from airflow-ai-sdk to apache-airflow-providers-common-ai 0.4.0+. Use when replacing airflow-ai-sdk with the official Airflow AI provider - migrating LLM decorators (@task.llm, @task.agent, @task.llm_branch, @task.embed), switching from model strings/objects to connection-based LLM configuration, updating imports from airflow_ai_sdk to the new provider, or upgrading an existing common-ai 0.1.x setup to 0.4.x (multimodal prompts, toolsets, embedding operators); also when common-ai provider, AIP-99, a pydanticai connection or migrating away from airflow-ai-sdk come up.
Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.
Re-detect this project's tech stack from package.json / requirements.txt / pyproject.toml / go.mod / Cargo.toml and diff it against the Tech Stack section of every CLAUDE.md. Read-only — returns added / removed / renamed dependencies, never edits.
Audit every CLAUDE.md in this project for drift against the last week of git history. Flags sections that reference deleted files, renamed paths, or removed dependencies. Read-only — returns a punch list, never edits.
Run Python (ruff) and JavaScript (Biome) linting.
Detect stale TODOs, unused imports, and dead code.
TypeScript type checking via tsc --noEmit with actionable error output.
Python quality checks: ruff, pytest, mypy, bandit in deterministic order.
Review and fix temporal references in code comments.
Read public Bluesky feeds via AT Protocol API.
Design a CLI interface: args, flags, help, output, errors, exit codes, config.
Post tweets, build threads, upload media via the X API.
Three.js app builder: imperative, React Three Fiber, and WebGPU in 4 phases.
Deterministic API endpoint validation with pass/fail reporting.
Scaffold vexjoy-agent operator .md files: frontmatter, routing block, operator context, reference loading table, phase/gate workflow.
Run benchmark-selected GPT-5.6 work through the Codex CLI.
DAG-based multi-skill orchestration with dependency resolution.
Triage GitHub notifications and issue/PR queues.
Statistical rule discovery from Go codebase patterns.
Systematic codebase exploration and architecture mapping.
Read-only exploration, inspection, and reporting without modifications. Explore and report on code/config/state without writing or modifying anything.
Mandatory rules for agents in git worktree isolation.
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
Local security review of git changes: deterministic scan + Security reviewer over the diff. No API key, no SDK.
Structured multi-phase workflows: review, debug, refactor (tidy, clean up, untangle messy code without behaviour change), deploy, create, research.
Deeply Understand (codemap) — 持久化代码图谱 + 变更影响分析 + 理解地图。接手遗留项目 Day-1 / PR 影响分析 / 解释陌生代码 / PRD·ADR 前现状理解时用。编排 tools/codemap/ 的 codemap CLI
Use when wiring up or switching between China-domestic LLM providers (DeepSeek, Doubao/Volc Ark, Qwen/DashScope, MiniMax). Provides OpenAI-compatible adapter pattern, env-var contracts, fallback strategy, cost guardrails, and minimum verifications before declaring integration done.
Use whenever a user, product-lead, or qa-engineer wants to create a GitHub issue in the project repo — including phrases like "提一个 issue / 写一个 issue / 报 bug / 把这个开成 issue / gh issue / 上 GitHub / track 一下 / 立个 ticket". Provides the required-field skeleton, locked label set (type / area / epic / priority / severity / phase), gh CLI heredoc template, and the QA-auto-issue exception path. Replaces ad-hoc `gh issue create` calls with inconsistent titles / missing labels / freestyle bodies.
| Analyzes and safely cleans up local Git branches. Categorizes branches by merge status, staleness, and remote tracking. Provides interactive selection with safety guards. Use when the user wants to clean up branches, delete old branches, organize Git branches, or asks about which branches can be safely deleted.
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
Update per-package CHANGELOG.md files for a Ratel release. Drafts entries with git-cliff (scoped per package), lets you curate, then writes the CHANGELOGs. Handles both RC entries and GA-graduation collapse (merging X.Y.Z-rc.* sections into a single X.Y.Z section). Invoke before tagging a release.
Workflow for updating the popular LLM applications pool (section/x_llm_apps.md) using fetch_llm_apps.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked from applications.md. DO NOT USE FOR: Hand-curating application entries inside applications.md or adding GitHub star badges to the generated file.
Guides users through setting up an ElevenLabs API key for ElevenLabs MCP tools. Use when the user needs to configure an ElevenLabs API key, when ElevenLabs tools fail due to missing API key, or when the user mentions needing access to ElevenLabs. First checks whether ELEVENLABS_API_KEY is already configured and valid, and only runs full setup when needed.
Use when reading or writing Python files (.py, pyproject.toml, requirements.txt).