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 5 252 updated this month 1 369 from vendors
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle tiers (alpha, beta, rc, released) with CHANGELOG-driven selective bumps, floor bound checks, and post-bump validation.
> Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
> Guidelines for writing and running tests in the Agent Framework Python codebase. Use this when creating, modifying, or running tests.
> Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.
> Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
Use this skill to fuzz open source Python software projects using Atheris.
Use this skill to fuzz open source Rust software projects.
Reproduce a public GitHub issue or PR in the Mapbox GL JS repo as a minimal focused debug page under `./debug/`. Trigger when the user pastes a GitHub issue/PR URL (github.com/mapbox/mapbox-gl-js/issues/N or /pull/N), or says "repro this issue", "reproduce #N", "make a repro page for", "debug page for issue", "recreate this bug", "build a minimal repro", or provides a bare issue number like `#12345` in the context of investigating a bug. Use this skill whenever the user wants to investigate a bug report, regression, or reported behavior — even if they don't say the word "repro" — since a working debug page is almost always the first step before fixing.
Add or extend Redis commands in the Jedis client API — a new core command, a family of new commands, an extension to an existing command's options, or a module command (Search/TimeSeries/JSON/Bloom). Gathers evidence (Redis server PR, HLD document), plans the full implementation matrix in plan mode, then implements with unit and integration tests following Jedis maintainer conventions.
Generate a clear, concise GitHub PR title and description from the diff between two local git branches, and save it to prDescription.md in the repo root. Use this whenever the user asks to write, generate, draft, or update a PR description or PR title from local branch changes — including phrasing like "summarize this diff into a PR description," "write a PR description for my current branch," "create a PR title and description," or any request to compare a base and target branch for PR purposes. Trigger even if the user doesn't name specific branches; this skill knows how to default them.
Development workflows for the playwright-cli repository. Use when the user asks about rolling dependencies, releasing, or other repo maintenance tasks.
Audits and optimizes Convex application performance across hot-path reads, write contention, subscription cost, and function limits. Use this skill when a Convex feature is slow or expensive, npx convex insights shows high bytes or documents read, OCC conflict errors or mutation retries appear, subscriptions or UI updates are costly, functions hit execution or transaction limits, or the user mentions performance, latency, read amplification, or invalidation problems in a Convex app.
Initializes a new Convex project from scratch or adds Convex to an existing app. Use this skill when starting a new project with Convex, scaffolding with npm create convex@latest, adding Convex to an existing React, Next.js, Vue, Svelte, or other frontend, wiring up ConvexProvider, configuring environment variables for the deployment URL, or running npx convex dev for the first time, even if the user just says "set up Convex" or "add a backend."
Audits and optimizes Convex application performance across hot-path reads, write contention, subscription cost, and function limits. Use this skill when a Convex feature is slow or expensive, npx convex insights shows high bytes or documents read, OCC conflict errors or mutation retries appear, subscriptions or UI updates are costly, functions hit execution or transaction limits, or the user mentions performance, latency, read amplification, or invalidation problems in a Convex app.
Adds a new built-in RESP command to Garnet end-to-end. Covers enum registration, parsing, dispatch, RESP handler, API surface, storage session, RMW callbacks, command metadata JSON, ACL tests, and integration tests. Use when asked to "add a command", "implement RI.SET", "add RESP command", or any new server command. Do NOT use for custom extension commands (CustomRawStringFunctions) or object-type sub-operations.
quiche draft GitHub release automation from a release commit hash or existing tag. Use when creating draft releases for the quiche crate from a quiche/Cargo.toml version bump.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
Use when needing to reorder, split, drop, or amend git commits that are not the top commit, without interactive editor access. Covers programmatic rebase via GIT_SEQUENCE_EDITOR, commit splitting with automated hunk selection, and metadata changes (author, message, dates) on any commit in a range.
> Guide for adding a new IR instruction to the Hermes compiler. Use when the user asks to add, create, or define a new IR instruction (Inst/Instruction) in the Hermes intermediate representation. Covers all required files and the patterns for each.
> Rules for writing and reviewing GC-safe C++ code in the Hermes VM runtime. Use when writing, modifying, or reviewing C++ runtime VM code that uses internal Hermes VM APIs (as opposed to code using JSI). This includes working with GC-managed types (HermesValue, Handle, PinnedValue, JSObject, StringPrimitive, etc.), Locals, GCScope, PseudoHandle, CallResult, or any function with _RJS suffix. Typically in lib/VM/, include/hermes/VM/, API/hermes/, or API/napi/.
Convert Prompt Flow flow definitions to Microsoft Agent Framework (MAF) workflows. Parses flow.dag.yaml, maps nodes to Executors, and generates runnable Python code using agent-framework 1.0.x. WHEN: convert promptflow, migrate promptflow, promptflow to MAF, promptflow to agent framework, convert flow.dag.yaml, migrate flow to MAF, convert PF flow, PF to agent-framework, convert DAG flow to workflow, migrate LLM flow. DO NOT USE FOR: writing new MAF workflows from scratch (no source flow), deploying MAF workflows (use maf-online-endpoint), enabling tracing (use maf-tracing), or general agent-framework Q&A.
Convert an existing Prompt Flow Parallel Run Step (PRS) pipeline submission into an Azure ML PRS pipeline that runs a Microsoft Agent Framework (MAF) workflow. Wraps the MAF workflow into a PRS init()/run() entry script, generates the parallel component YAML and conda environment, and rewrites the pipeline submission script. Replaces what `load_component(flow.dag.yaml)` did automatically for Prompt Flow \u2014 produces the hand-built equivalent so that downstream pipeline code (`flow_node = flow_component(...)`, `flow_node.outputs.flow_outputs`, `flow_node.outputs.debug_info`, `flow_node.mini_batch_size`, scheduler, batch endpoint) stays unchanged. WHEN: convert promptflow PRS to MAF PRS, migrate PRS pipeline to agent framework, wrap MAF workflow as parallel component, bulk run MAF workflow, run agent framework as parallel run step, batch run MAF workflow on AML, submit MAF workflow as pipeline component, replace flow.dag.yaml with MAF workflow in pipeline, load_component equivalent for MAF workflow, MAF version of flow_component, load MAF workflow as component, wrap MAF workflow as flow component, MAF flow component, replace flow_node in pipeline with MAF workflow, keep flow_outputs and debug_info ports with MAF, MAF parallel component with connections={}, run MAF workflow as flow_node in AML pipeline, load_component('workflow.py') doesn't work. DO NOT USE FOR: converting the flow itself (use promptflow-to-maf), deploying as online endpoint (use maf-online-endpoint), enabling tracing only (use maf-tracing).
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
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. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
Use when designing REST or GraphQL APIs, creating OpenAPI specifications, or planning API architecture. Invoke for resource modeling, versioning strategies, pagination patterns, error handling standards.
Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.
Generates, formats, and validates technical documentation — including docstrings, OpenAPI/Swagger specs, JSDoc annotations, doc portals, and user guides. Use when adding docstrings to functions or classes, creating API documentation, building documentation sites, or writing tutorials and user guides. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, getting started guides.
Writes, optimizes, and debugs C++ applications using modern C++20/23 features, template metaprogramming, and high-performance systems techniques. Use when building or refactoring C++ code requiring concepts, ranges, coroutines, SIMD optimization, or careful memory management — or when addressing performance bottlenecks, concurrency issues, and build system configuration with CMake.
Use when building C# applications with .NET 8+, ASP.NET Core APIs, or Blazor web apps. Builds REST APIs using minimal or controller-based routing, configures database access with Entity Framework Core, implements async patterns and cancellation, structures applications with CQRS via MediatR, and scaffolds Blazor components with state management. Invoke for C#, .NET, ASP.NET Core, Blazor, Entity Framework, EF Core, Minimal API, MAUI, SignalR.
Use when building Django web applications or REST APIs with Django REST Framework. Invoke when working with settings.py, models.py, manage.py, or any Django project file. Creates Django models with proper indexes, optimizes ORM queries using select_related/prefetch_related, builds DRF serializers and viewsets, and configures JWT authentication. Trigger terms: Django, DRF, Django REST Framework, Django ORM, Django model, serializer, viewset, Python web.
Use when building .NET 8 applications with minimal APIs, clean architecture, or cloud-native microservices. Invoke for Entity Framework Core, CQRS with MediatR, JWT authentication, AOT compilation.
Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.
Implements concurrent Go patterns using goroutines and channels, designs and builds microservices with gRPC or REST, optimizes Go application performance with pprof, and enforces idiomatic Go with generics, interfaces, and robust error handling. Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration, CLI tools, benchmarks, or table-driven testing.
Writes, debugs, and refactors JavaScript code using modern ES2023+ features, async/await patterns, ESM module systems, and Node.js APIs. Use when building vanilla JavaScript applications, implementing Promise-based async flows, optimising browser or Node.js performance, working with Web Workers or Fetch API, or reviewing .js/.mjs/.cjs files for correctness and best practices.
Provides idiomatic Kotlin implementation patterns including coroutine concurrency, Flow stream handling, multiplatform architecture, Compose UI construction, Ktor server setup, and type-safe DSL design. Use when building Kotlin applications requiring coroutines, multiplatform development, or Android with Compose. Invoke for Flow API, KMP projects, Ktor servers, DSL design, sealed classes, suspend function, Android Kotlin, Kotlin Multiplatform.
Designs incremental migration strategies, identifies service boundaries, produces dependency maps and migration roadmaps, and generates API facade designs for aging codebases. Use when modernizing legacy systems, implementing strangler fig pattern or branch by abstraction, decomposing monoliths, upgrading frameworks or languages, or reducing technical debt without disrupting business operations.
Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio/HTTP/SSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server/client projects using TypeScript or Python SDKs.
Creates and configures NestJS modules, controllers, services, DTOs, guards, and interceptors for enterprise-grade TypeScript backend applications. Use when building NestJS REST APIs or GraphQL services, implementing dependency injection, scaffolding modular architecture, adding JWT/Passport authentication, integrating TypeORM or Prisma, or working with .module.ts, .controller.ts, and .service.ts files. Invoke for guards, interceptors, pipes, validation, Swagger documentation, and unit/E2E testing in NestJS projects.