Find the perfect capability for your agent.
Create and optimize Dockerfiles with BuildKit, multi-stage builds, advanced caching, and security. Use when: (1) Creating new Dockerfile, (2) Optimizing existing Dockerfile, (3) Reducing image size, (4) Improving security, (5) Using Python with uv, (6) Resolving cache or slow build issues, (7) Setting up CI/CD builds
Automate detection and removal of duplicate files/functions in TypeScript, JavaScript, and Python projects. Safely refactor imports, validate with tests, and deploy changes with zero risk of breaking the system.
Expert system for identifying deprecated patterns, suggesting refactoring to modern standards (Python 3.12+, ES2024+), checking test coverage, and leveraging AI-powered tools. Proactively applied when users request refactoring, updates, or analysis of legacy codebases.
Production-grade Docker containerization for FastAPI, Pytest, SQLModel, and Pydantic projects. This skill should be used when users ask to dockerize FastAPI applications, create multi-stage Docker builds, optimize Docker images for Python APIs, set up CI/CD Docker pipelines, or deploy containerized Python web APIs with maximum performance and minimal image size.
Use when setting up GitHub Actions CI/CD for pull request checks - provides workflow templates for Python, JavaScript, and polyglot projects that run quality gates on every PR
Use when deploying to Fly.io - covers single volume limitation, monorepo deployment, Dockerfile patterns for Next.js/Python, and common troubleshooting
Generate and run unit and integration tests using pytest (Python) or Jest (JavaScript) with fixtures, mocks, and async support. Use for test-driven development, code review validation, coverage verification, and regression testing. Target 80%+ code coverage. Supports pytest markers, Jest snapshots, and CI/CD integration. Triggers on "test", "TDD", "unit test", "integration test", "test coverage", "pytest", "jest".
Guides on analyzing and improving test coverage for Python (pytest-cov) and JavaScript (c8) projects. Use when a user asks to set up test coverage, generate coverage reports, understand why coverage is low, or improve their test coverage.
Linux VM bootstrap verification skill. MUST BE USED when setting up new VMs or verifying environment. Supports modes: check (warn-only), install (operator-confirmed), strict (CI-ready). Enforces Linux-only + mise as canonical; honors preference brew→npm (with apt fallback). Verifies required tools: mise, node, pnpm, python, poetry, gh, railway, bd, tmux, jq, rg. Handles optional tools as warnings: tailscale, playwright, docker, bv. Never prints/seeds secrets; never stores tokens in repo/YAML; Railway vars only for app runtime env. Safe on dirty repos (refuses and points to dirty-repo-bootstrap skill, or snapshots WIP branch). Keywords: vm, bootstrap, setup, mise, toolchain, linux, environment, provision, verify, new vm
Generate Docker and Traefik deployment configurations for any application (Node.js, Python, Go, Rust, Java). Creates Dockerfile, docker-compose.yml, docker-compose.for-traefik.yml overlay, and .env.sample with production best practices. Use when: dockerize app, containerize, add Docker, deploy with Traefik, reverse proxy setup, HTTPS/SSL, Let's Encrypt certificates, production deployment, docker-compose setup. Requires: Docker, docker-compose.
Dev Container を最短で導入/更新するためのブートストラップ。stack を自動判定（node/python/rust）し、テンプレート適用または安全更新を行う。既存 .devcontainer がある場合はバックアップ後にマージ。Chat オプション: stack (auto|node|python|rust), packageManager (npm|pnpm|yarn), mode (safe|overwrite), includeTools (true|false), addCI (true|false)。
Automate and inspect GitHub Actions runs with the asim-utilities Python library/CLI. Use when triggering workflows, listing recent runs or failures, waiting/canceling runs on job failure, or downloading run logs and artifacts. Covers CLI usage, output conventions, and repo layout for ~/workplace/asim-utilities.
Test-Driven Development skill. Generates unit tests before implementation. Adapts to project type (Chrome extension, React, Express, Python, etc.). Reads/updates pattern library.
Generic DigitalOcean droplet deployment using doctl CLI for any application type (APIs, web servers, background workers). Includes validation, deployment scripts, systemd service management, secret handling, health checks, and deployment tracking. Use when deploying Python/Node.js/any apps to droplets, managing systemd services, handling secrets securely, or when user mentions droplet deployment, doctl, systemd, or server deployment.
Comprehensive pre-production repository audit for preparing repos for public release. Use when the user wants to perform a "final sweep", "production check", "pre-release audit", or prepare a repository to be made public. Audits metadata files across all project types (Fabric mods, Node/npm, Go, Python, etc.) for correct username/organization, checks README links, and identifies leftover development artifacts like planning docs, test scripts, and temporary files. Interactive cleanup with user confirmation.
Comprehensive multi-AI code review system for Python, JavaScript, and TypeScript. Use when reviewing code for quality, security, performance, or best practices. Includes automated analysis scripts, language-specific patterns, and AI collaboration workflows for complex decisions.
Run SonarQube/SonarCloud static code analysis to check code quality, detect security vulnerabilities, code smells, and bugs. Use when the user mentions SonarQube, code quality analysis, security scanning, static analysis, or wants to check for vulnerabilities.
Generate production-ready Dockerfiles with best practices for various tech stacks. Covers multi-stage builds, optimization, security, and language-specific patterns (Node.js, Python, Go, Rust, Java). Use when creating new application containers.
This skill should be used when the user asks to "провести ревью", "проверить код", "найти проблемы в коде", "code review", "найти баги", "проверить на ошибки", "analyze code quality", "ревью PR", "review pull request", or needs guidance on concurrency issues, security vulnerabilities, or language-specific code quality for Go, Java, Python.
