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Skills para agentes

Skills para Claude Code, Codex, Cursor e outros agentes do diretório skills.sh: instalações, status da auditoria de segurança e o comando para adicionar cada skill.

Uma skill não fica presa a um agente: npx skills add a instala em qualquer agente compatível. Sem -a, a CLI encontra os agentes do seu computador e pergunta onde adicioná-la.

Atualizado · Fontes: skills.sh

Exibidas: 100 · encontradas: 102 · de 10.000 no diretório

  • python-appservice-deploy microsoft/azure-skills

    Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.

    203.423 Aprovada
  • python-testing-patterns wshobson/agents

    Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

    34.423 Aprovada
  • python-performance-optimization wshobson/agents

    Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

    34.191 Aprovada
  • developing-genkit-python firebase/agent-skills

    33.597 Aprovada
  • fastapi-templates wshobson/agents

    Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

    25.013 Aprovada
  • python-design-patterns wshobson/agents

    Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when deciding whether to add a new abstraction or live with duplication, when evaluating a pull request for structural issues like tight coupling or leaking internal types, when choosing between inheritance and composition for a new class hierarchy, or when a codebase is becoming hard to test because of entangled I/O and business logic.

    21.739 Aprovada
  • async-python-patterns wshobson/agents

    Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.

    17.103 Aprovada
  • python-code-style wshobson/agents

    Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards.

    15.605 Aprovada
  • python-project-structure wshobson/agents

    Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with __all__, or planning directory layouts.

    14.025 Aprovada
  • fastapi-python mindrally/skills

    Expert in FastAPI Python development with best practices for APIs and async operations

    13.502 Aprovada
  • python-error-handling wshobson/agents

    Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.

    13.330 Aprovada
  • data-visualization anthropics/knowledge-work-plugins

    Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

    12.757 Aprovada
  • python-anti-patterns wshobson/agents

    Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.

    12.556 Aprovada
  • python-packaging wshobson/agents

    Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

    12.280 Aprovada
  • python-patterns affaan-m/ecc

    Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications. Use when writing or reviewing Python code and idiomatic structure, typing, or PEP 8 is in question.

    12.041 Aprovada
  • python-type-safety wshobson/agents

    Python type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining structural interfaces, or configuring mypy/pyright.

    11.973 Aprovada
  • fastapi fastapi/fastapi

    FastAPI best practices and conventions. Use when working with FastAPI APIs, Pydantic models, dependencies, streaming responses including Server-Sent Events (SSE), and serving frontend apps. Keeps FastAPI code clean and up to date with the latest features and patterns.

    11.351 Aprovada
  • python-resilience wshobson/agents

    Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.

    11.094 Aprovada
  • django-security affaan-m/ecc

    Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations. Use when reviewing Django authentication, authorization, input handling, or deployment settings.

    11.091 Aprovada
  • python-testing affaan-m/ecc

    Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements. Use when writing pytest tests — fixtures, mocks, parametrization, or coverage.

    11.055 Aprovada
  • python-configuration wshobson/agents

    Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.

    10.924 Aprovada
  • python-background-jobs wshobson/agents

    Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

    10.568 Aprovada
  • database-migrations affaan-m/ecc

    Safe, reversible database migration patterns: forward-only production changes, expand-contract zero-downtime renames, concurrent indexes, batched backfills, and per-tool workflows for PostgreSQL, Prisma, Drizzle, Kysely, Django, and golang-migrate. Use when writing a schema or data migration, adding a column or index to a large table, planning a rollback, or preparing a zero-downtime deploy.

    10.521 Aprovada
  • python-resource-management wshobson/agents

    Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.

    10.409 Aprovada
  • flash runpod/runpod-plugins-official

    runpod-flash — code-first serverless: write Python locally, run it on remote Runpod GPUs/CPUs with `flash dev` (hot-reload + live worker logs), then `flash deploy`. Use for @Endpoint/@remote functions, resource config, and debugging flash deployments. For CLI-only infra management use runpodctl or runpod-mcp.

    10.399 Aprovada
  • django-verification affaan-m/ecc

    Run the full Django verification loop — environment check, mypy/ruff/black linting, migration safety, pytest with coverage targets, pip-audit and bandit security scans, settings and logging review, and diff review — producing a phased pass/fail report before release or PR. Use when preparing a Django pull request, validating migrations or coverage, or running pre-deploy readiness checks.

    9.567 Aprovada
  • aws-cdk aws/agent-toolkit-for-aws

    Authors, deploys, and troubleshoots AWS infrastructure using CDK with TypeScript or Python. Covers best practices, stack architecture, and construct patterns. Applies when writing CDK constructs, bootstrapping environments, running cdk deploy/synth/diff, fixing CDK or CloudFormation errors, planning stack structure, importing existing resources, resolving drift, or refactoring stacks without resource replacement.

    8.361 Aprovada
  • modern-python trailofbits/skills

    Configures Python projects with modern tooling (uv, ruff, ty). Use when creating projects, writing standalone scripts, or migrating from pip/Poetry/mypy/black.

    8.066 Aprovada
  • chdb-datastore clickhouse/agent-skills

    Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.

    7.595 Aprovada
  • dxf earthtojake/text-to-cad

    Generate, regenerate, and validate 2D DXF drawings from Python build123d sources. Use for DXF files, `.py` drawing scripts, @dxf models, 2D profiles, outlines, templates, gaskets, panels, flat patterns, laser/plasma/waterjet cut layouts, and 2D drawing exports of CAD geometry. Open and visually review existing DXF files in CAD Viewer.

    7.552 Aprovada
  • aws-sdk-python-usage aws/agent-toolkit-for-aws

    AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.

    7.030 Aprovada
  • fastapi-patterns affaan-m/ecc

    FastAPI best practices covering project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest. Use when building or reviewing FastAPI apps — Pydantic schemas, dependencies, async handlers, auth, or tests.

    6.235 Aprovada
  • error-handling affaan-m/ecc

    Patterns for robust error handling across TypeScript, Python, and Go. Covers typed errors, error boundaries, retries, circuit breakers, and user-facing error messages. Use when designing error types, retries, circuit breakers, or user-facing failure messages in TypeScript, Python, or Go.

    5.987 Aprovada
  • django-celery affaan-m/ecc

    Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.

    5.827 Aprovada
  • netmiko-ssh-automation affaan-m/ecc

    Safe Python Netmiko patterns for read-only collection, bounded batch SSH, TextFSM parsing, guarded config changes, timeouts, and network automation error handling. Use when automating network device access with Python Netmiko, whether collecting state or pushing guarded config changes.

    5.750 Aprovada
  • build-mcpb anthropics/claude-plugins-official

    This skill should be used when the user wants to "package an MCP server", "bundle an MCP", "make an MCPB", "ship a local MCP server", "distribute a local MCP", discusses ".mcpb files", mentions bundling a Node or Python runtime with their MCP server, or needs an MCP server that interacts with the local filesystem, desktop apps, or OS and must be installable without the user having Node/Python set up.

    5.579 Aprovada
  • marimo-notebook marimo-team/skills

    Write a marimo notebook in a Python file in the right format.

    5.579 Aprovada
  • docx-manipulation claude-office-skills/skills

    Create, edit, and manipulate Word documents programmatically using python-docx

    5.537 Aprovada
  • bigquery-bigframes google/skills

    Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.

    5.423 Aprovada
  • create-viz anthropics/knowledge-work-plugins

    Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

    5.039 Aprovada
  • langgraph-docs langchain-ai/deepagents

    Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.

    4.540 Aprovada
  • kb-retriever conardli/garden-skills

    面向本地知识库目录的检索和问答助手。核心流程:(1)分层索引导航 (2)遇到PDF/Excel时必须先读取references学习处理方法 (3)处理文件后再检索。按文件类型组合使用 grep、Read、pdfplumber、pandas 进行渐进式检索,避免整文件加载。用户问题涉及"从知识库目录回答问题/检索信息/查资料"时使用。

    4.514 Aprovada
  • dignified-python dagster-io/skills

    4.420 Aprovada
  • 4.264 Aprovada
  • uv astral-sh/claude-code-plugins

    Guide for using uv, the Python package and project manager. Use this when working with Python projects, scripts, packages, or tools.

    4.202 Aprovada
  • temporal-developer temporalio/skill-temporal-developer

    Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java, .NET, Ruby, and Rust. Use when the user is building workflows, activities, workers, or background job queues with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or Temporal Cloud, or working with durable execution concepts like signals, queries, heartbeats, versioning, continue-as-new, child workflows, or saga patterns. Also use when the user mentions "run a Temporal workflow from the CLI", "start a dev server", "run temporal server start-dev", "temporal workflow start", "temporal workflow execute", "temporal workflow signal", "temporal workflow query", "temporal workflow update".

    4.083 Aprovada
  • code-review-skill awesome-skills/code-review-skill

    Provides comprehensive code review guidance for React 19, Vue 3, Angular 17+, Svelte 5, Rust, TypeScript, Java, Java 8, PHP, Ruby, Rails, Python, Django, FastAPI, Go, C#/.NET, Kotlin, Swift, Dart, Flutter, NestJS, C/C++, Zig, CSS/Less/Sass, Qt, and more. Covers architecture review, performance review, security audit, code quality anti-patterns, and common bugs across all ecosystems. Use when: reviewing pull requests, conducting PR reviews, code review, reviewing code changes, establishing review standards, mentoring developers, architecture reviews, security audits, performance reviews, checking code quality, finding bugs, giving feedback on code.

    3.166 Aprovada
  • pptx-html-fidelity-audit nexu-io/open-design

    Audit a python-pptx export against its source HTML deck, identify layout/content drift (footer overflow, cropped content, missing italic/em, lost styling, off-rhythm spacing), and re-export with strict footer-rail + cursor-flow layout discipline. Use this skill whenever the user has a .pptx that was generated from an HTML slide deck and asks to compare/audit/verify/fix the export — including phrases like "compare ppt with html", "fidelity audit", "fix the pptx", "ppt is cut off", "footer overlap", "italic missing in pptx", "re-export the deck", "pptx-html-fidelity-audit", or any case where a python-pptx → HTML round-trip needs verification or repair. Also trigger when the user shows you a deck.html and a deck.pptx side by side and is debugging visual differences.

    3.035 Aprovada
  • iii-error-handling iii-hq/iii

    Handle iii engine and SDK errors across Node, Python, Rust, and browser workers. Use when interpreting error codes, retryability, RBAC denial, timeouts, handler failures, or SDK-specific exception surfaces.

    3.028 Aprovada
  • code-testing-agent dotnet/skills

    ALWAYS USE whenever asked to write, add, or generate unit tests for existing code in xUnit, MSTest, NUnit, pytest, Vitest/Jest, Go, or another framework, including "tests only for" one helper, function, class, or missing regression case as well as project-wide suites. Also use for "cover this untested method", scaffolding tests where none exist, sparse workspaces, classic packages.config MSTest, and extending healthy suites. Focused requests use a proportional direct workflow; broad requests use the full pipeline. DO NOT USE for only running/diagnosing tests, coverage/audits, a test blocked on a missing production seam (testability-obstacle), or correcting supplied MSTest assertions, attributes, lifecycle, or configuration without designing new cases (writing-mstest-tests).

    2.999 Aprovada
  • iii-sdk-reference iii-hq/iii

    Use when working with iii SDK APIs across Node.js, browser, Python, or Rust: package installation, worker initialization, function/trigger registration, invocation, channels, logging, OpenTelemetry, and language-specific caveats.

    2.960 Aprovada
  • pygame-core gamedev-skills/awesome-gamedev-agent-skills

    Structure a pygame (pygame-ce) game in Python: the init/event/update/draw loop, delta-time movement, Surface/Rect blitting, keyboard/mouse input, and Sprite/Group management with collision. Use when building or debugging a pygame game — when the user mentions pygame, pygame-ce, the game loop, blit, Surface, Rect, sprite groups, or clock.tick. Targets pygame-ce.

    2.931 Aprovada
  • ruff astral-sh/claude-code-plugins

    Guide for using ruff, the extremely fast Python linter and formatter. Use this when linting, formatting, or fixing Python code.

    2.861 Aprovada
  • blender sfkislev/flue

    Control Blender from the shell via Flue - a Python bridge to bpy without an MCP server.

    2.758 Aprovada
  • senior-backend davila7/claude-code-templates

    Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.

    2.683 Aprovada
  • developing-genkit-python google/skills

    Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.

    2.615 Aprovada
  • dummy-dataset phuryn/pm-skills

    Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.

    2.562 Aprovada
  • ty astral-sh/claude-code-plugins

    Guide for using ty, the extremely fast Python type checker and language server. Use this when type checking Python code or setting up type checking in Python projects.

    2.481 Aprovada
  • dt-obs-services dynatrace/dynatrace-for-ai

    Service performance monitoring with RED metrics (Rate, Errors, Duration) and runtime-specific telemetry for Java, .NET, Node.js, Python, PHP, and Go. Use when analyzing service health, SLA compliance, or runtime issues. Trigger: "service response time", "error rate", "throughput", "SLA compliance", "service mesh overhead", "JVM GC", "Java heap", "Node.js event loop", ".NET CLR", "Python threads", "PHP OPcache", "Go goroutines", "service performance", "p95 latency", "request failures", "database response time by name". Do NOT use for explaining existing queries, product documentation questions, infrastructure metrics (use dt-obs-hosts), log analysis (use dt-obs-logs), or distributed tracing workflows (use dt-obs-tracing).

    2.387 Aprovada
  • mojo-python-interop modular/skills

    Aids in writing Mojo code that interoperates with Python using current syntax and conventions. Use this skill in addition to mojo-syntax when writing Mojo code that interacts with Python, calls Python libraries from Mojo, or exposes Mojo types/functions to Python. Also use when the user wants to build Python extension modules in Mojo, wrap Mojo structs for Python consumption, or convert between Python and Mojo types.

    2.379 Aprovada
  • deepstream-dev nvidia/skills

    NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.

    2.347 Aprovada
  • odoo-development mindrally/skills

    Expert guidance for Odoo ERP development including Python ORM, XML views, and module architecture

    2.250 Aprovada
  • uv google-deepmind/science-skills

    Checks whether the uv Python package manager is installed and installs it if missing. Ensures uv is on PATH. Use when another skill requires uv as a prerequisite.

    2.223 Aprovada
  • cuopt-routing-api-python nvidia/skills

    Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

    2.215 Aprovada
  • huggingface-gradio huggingface/skills

    Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

    2.124 Aprovada
  • cuopt-server-api-python nvidia/skills

    cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.

    2.121 Aprovada
  • python-best-practices alleneubank/claude-code

    Use when reading or writing Python files (.py, pyproject.toml, requirements.txt).

    2.108 Aprovada
  • tigris-static-assets tigrisdata/skills

    Use when deploying static assets (CSS, JS, fonts, build artifacts) to Tigris — asset pipelines, cache headers, CDN delivery, covers Next.js, Remix, Rails, Django, Laravel, Express

    2.061 Aprovada
  • employee-performance-analytics-hr reason-machines/data-skills

    SQL and Python-based employee performance analytics with KPI aggregation, departmental insights, and HR dashboard generation

    2.051 Aprovada
  • openai-agents-sdk laguagu/claude-code-nextjs-skills

    OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent handoffs, function tools, guardrails, sessions, streaming, or tracing with the `openai-agents` / `agents` Python package — including Azure OpenAI via LiteLLM. Triggers on imports from `agents`, uses of `Runner.run_sync`/`Runner.run_streamed`, `@function_tool`, `AgentOutputSchema`, `SQLiteSession`, or questions about the openai-agents-python SDK. Python only — not the TypeScript `@openai/agents` SDK.

    1.976 Aprovada
  • 1.961 Aprovada
  • scrapy-web-scraping mindrally/skills

    Expert guidance for building web scrapers and crawlers using the Scrapy Python framework with best practices for spider development, data extraction, and pipeline management.

    1.942 Aprovada
  • scikit-learn k-dense-ai/scientific-agent-skills

    Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

    1.934 Aprovada
  • setup-python-tools cognitedata/builder-skills

    Pyodide Python tools for an already-approved in-app useAtlasChat UI. EOS sidebar Python tools run from agent CDF config — do not vendor Pyodide. Triggers: Pyodide, pythonRuntime, usePyodideRuntime, runPythonCode.

    1.894 Aprovada
  • seaborn k-dense-ai/scientific-agent-skills

    Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

    1.863 Aprovada
  • houdini sfkislev/flue

    Control SideFX Houdini from the shell via Flue - a Python bridge to hou without an MCP server.

    1.839 Aprovada
  • polars k-dense-ai/scientific-agent-skills

    High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

    1.818 Aprovada
  • networkx k-dense-ai/scientific-agent-skills

    Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.

    1.790 Aprovada
  • pyzotero k-dense-ai/scientific-agent-skills

    Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.

    1.785 Aprovada
  • matlab k-dense-ai/scientific-agent-skills

    Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

    1.772 Aprovada
  • optimize-for-gpu k-dense-ai/scientific-agent-skills

    GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

    1.753 Aprovada
  • geopandas k-dense-ai/scientific-agent-skills

    Guidance and local audit tools for Python workflows that directly use GeoPandas GeoSeries, GeoDataFrame, spatial operations, or vector-data I/O.

    1.730 Aprovada
  • bioservices k-dense-ai/scientific-agent-skills

    Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

    1.716 Aprovada
  • gget k-dense-ai/scientific-agent-skills

    Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

    1.708 Aprovada
  • pydantic pydantic/skills

    Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.

    1.707 Aprovada
  • modal k-dense-ai/scientific-agent-skills

    Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

    1.706 Aprovada
  • astropy k-dense-ai/scientific-agent-skills

    Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

    1.699 Aprovada
  • zarr-python k-dense-ai/scientific-agent-skills

    Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

    1.685 Aprovada
  • esm k-dense-ai/scientific-agent-skills

    Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.

    1.685 Aprovada
  • pysam k-dense-ai/scientific-agent-skills

    Python/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.

    1.678 Aprovada
  • pytdc k-dense-ai/scientific-agent-skills

    Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle workflows.

    1.674 Aprovada
  • gtars k-dense-ai/scientific-agent-skills

    Use Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.

    1.668 Aprovada
  • latchbio-integration k-dense-ai/scientific-agent-skills

    Build, register, debug, and operate bioinformatics workflows on Latch using the Python SDK, CLI, Latch Data and Registry, Nextflow, Snakemake, programmatic execution, and Latch MCP. Use when authoring or deploying Latch workflows, configuring resources or interfaces, moving data, integrating Registry, or launching and monitoring runs.

    1.665 Aprovada
  • opentrons-integration k-dense-ai/scientific-agent-skills

    Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow must support multiple robot vendors.

    1.662 Aprovada
  • n8n-code-nodes-official n8n-io/skills

    Use when the user reaches for a Code node, mentions writing JavaScript or Python in n8n, or any custom logic comes up in workflow design. Triggers on "Code node", "Code", "JavaScript", "Python", "custom logic", "transform data", "$input", "$json transformation", "loop in code", "write a function", or any time the obvious answer seems to be "just put it in code."

    1.662 Aprovada
  • aws-cdk-python-setup github/awesome-copilot

    Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.

    1.648 Aprovada
  • typing-exclusion-worker getsentry/skills

    Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.

    1.429 Aprovada
  • harvard-artifacts-collection-analytics-app reason-machines/data-skills

    Build ETL pipelines and analytics dashboards for the Harvard Art Museums API using Python, SQL, and Streamlit

    1.417 Aprovada
  • voicebox-voice-synthesis reason-machines/trending-skills

    Expert skill for Voicebox — the open-source local voice cloning and TTS studio built with Tauri, React, and FastAPI

    1.388 Aprovada
  • harvard-art-museum-data-engineering reason-machines/data-skills

    ETL pipeline and analytics app for Harvard Art Museums API using Python, SQL, and Streamlit

    1.339 Aprovada

As descrições são dos próprios autores, em inglês. Leia o código de uma skill no repositório dela antes de instalá-la.

O que é uma skill

Uma skill é uma pasta com um arquivo SKILL.md e, opcionalmente, scripts, que um agente de IA carrega quando a tarefa combina com ela. Funciona como um manual especializado: em vez de explicar tudo de novo a cada conversa, o agente lê as instruções da skill na hora certa. Este diretório reúne skills do skills.sh para Claude Code, Codex, Cursor e outros agentes.

O que a página mostra

  • nome, autor e repositório de cada skill;
  • número de instalações e status da auditoria de segurança;
  • o comando para adicionar a skill, com o botão “Copiar o comando”.

O diretório traz as 10 000 skills mais instaladas, sincronizadas diariamente. A aba “Em alta” ordena pelas instalações ganhas nos últimos 7 dias. Há filtros por tema e por agente, além da busca; cada visualização mostra no máximo 100 skills.

Como ler a auditoria

Os status vêm das auditorias publicadas pelo skills.sh: “Aprovada”, “Alertas”, “Reprovada” e “Não auditada”. Uma auditoria aprovada não é garantia. Skills podem incluir scripts que rodam na sua máquina, então leia o código no repositório antes de instalar, principalmente se ela pede acesso a arquivos, rede ou credenciais.

Passo a passo para instalar com cuidado

  1. Filtre pelo agente que você usa e pelo tema da tarefa.
  2. Abra o repositório e leia o SKILL.md e os scripts.
  3. Prefira skills com status “Aprovada” e autor identificável.
  4. Copie o comando e instale num projeto de teste antes de usar no trabalho.

Observações

As descrições são dos próprios autores, em inglês, e não são traduzidas. As instalações são contadas pelo skills.sh. Para escolher o agente que vai usar as skills, veja os agentes de programação.