আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.
Implements navigation patterns and routing for both frontend (React/TS) and backend (Python) including menus, tabs, breadcrumbs, client-side routing, and server-side route configuration. Use when building navigation systems or setting up routing.
Implements search and filter interfaces for both frontend (React/TypeScript) and backend (Python) with debouncing, query management, and database integration. Use when adding search functionality, building filter UIs, implementing faceted search, or optimizing search performance.
Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.
Writing optimized, secure, multi-stage Dockerfiles with language-specific patterns (Python, Node.js, Go, Rust), BuildKit features, and distroless images. Use when containerizing applications, optimizing existing Dockerfiles, or reducing image sizes.
Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.
Add a Python-only figure reference skill to a BioClaw installation. Use when the user wants publication-quality plotting guidance inside agent containers without adding source-code features. Creates `container/skills/figure/` with a Python-only `SKILL.md` and a root-level `seaborn_reference.md`.
Rust 核心驱动的高性能 Python Tars (JCE) 协议库。定义 Struct schema 进行二进制编解码，或使用 TarsDict 做无 schema 的 Raw 模式。使用场景：(1) 定义 JCE 请求/响应 Struct，(2) 使用 TarsDict 构造动态协议数据，(3) encode/decode 二进制数据，(4) wrap_simplelist 嵌套编码，(5) CLI 调试 JCE 二进制，(6) Meta 约束校验，(7) Schema 演进与版本兼容。触发词：tarsio, Tars, JCE, Struct, TarsDict, encode, decode, field, wrap_simplelist, jce.dumps, jce.loads。
Apply Python tooling standards including uv package management, pytest testing, ruff/based-pyright code quality, one-line docstrings, and self-documenting code practices. Use this skill when working with Python backend code, managing dependencies, running tests, or ensuring code quality. Apply when installing packages, writing tests, formatting code, type checking, adding docstrings, organizing imports, or deciding whether to create new files vs. extending existing ones. Use for any Python development task requiring adherence to tooling standards and best practices.
Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.
Build and deploy applications on inference.sh. Use when getting started, understanding the platform, creating apps, configuring resources, or needing an overview of inference.sh app development. Supports both Python and Node.js. Triggers: inference.sh app, infsh app, inf.yml, inference.py, inference.js, deploy app, app development, build app, create app, GPU app, VRAM, app resources, app secrets, app integrations, multi-function app
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
Full Sentry SDK setup for Python. Use when asked to "add Sentry to Python", "install sentry-sdk", "setup Sentry in Python", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for Python applications. Supports Django, Flask, FastAPI, Celery, Starlette, AIOHTTP, Tornado, and more.
Panel data analysis with Python using linearmodels and pandas.
This skill should be used when the user asks to "find a class", "search for symbol", "find usages", "find implementations", "search codebase", "find file", "class hierarchy", "find callers", "module dependencies", "unused dependencies", "project map", "project conventions", "project structure", "what frameworks", "what architecture", "find Perl subs", "Perl exports", "find Python class", "Go struct", "Go interface", "find React component", "find TypeScript interface", "find Rust struct", "find Ruby class", "find C# controller", "find Dart class", "find Flutter widget", "find mixin", "find Scala trait", "find case class", "find object", "find PHP class", "find Laravel model", "find PHP trait", or needs fast code search in Android/Kotlin/Java, iOS/Swift/ObjC, Dart/Flutter, TypeScript/JavaScript, Rust, Ruby, C#, Scala, PHP, Perl, Python, Go, C++, or Protocol Buffers projects. Also triggered by mentions of "ast-index" CLI tool.
Data processing skill with Python and shell scripts for file analysis and transformation
Use when asked to prepare new serious_python release by bumping versions and author release notes.
Specialized guidance for modeling mathematical optimization problems using the pyoptinterface_native library. Use when building optimization models (LP, QP, MIP, NLP), interfacing with solvers (Gurobi, HiGHS, Ipopt, etc.), and performing matrix-based or nonlinear modeling in Python.
