ai-driven

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

Agent-based development workflow for implementation tasks. Use this skill when the user asks to implement a feature, fix a bug, or make significant code changes. Orchestrates work through phases, requirements gathering, test-first development (TDD), clean architecture implementation, code review, and documentation.

SonarQube issue remediation workflow. Use this skill when the user asks to fix SonarQube issues, run a code quality sweep, or remediate static analysis findings for a project. Retrieves issues via MCP, groups them, presents a fix plan, and implements fixes in batches with test verification.

Run linting and formatting checks on the codebase. Use this skill when code changes are made and need static analysis validation. Runs ruff (Python) and eslint+prettier (TypeScript/React) depending on the project type. Invoke after implementation and before code review.

Deep performance audit for Python and React codebases. Use this skill ANY TIME the user shares code and mentions performance, slow queries, latency, re-renders, optimization, profiling, bottlenecks, or memory issues — even if they don't use the word "audit". Also trigger when the user pastes code and asks "is this efficient?", "why is this slow?", "how can I improve this?", or shares ORM/database code (Django, SQLAlchemy, Prisma, TypeORM) where N+1 query risks are high. Covers: N+1 queries, missing indexes, loop anti-patterns, React re-render storms, missing memoization, bundle bloat, async misuse, memory leaks, caching strategy opportunities (query caching, HTTP caching, client-side caching, cache invalidation triggers), and 20+ other patterns across both stacks.

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Analyze a database schema by running SchemaCrawler lint and extracting the full schema via information_schema. Use this skill whenever the user wants to audit a database schema, check normalization, detect missing indexes, redundant indexes, nullable FK columns, inconsistent data types, design smells, or get improvement recommendations.

Manage the full GitHub PR lifecycle from a Jira ticket create a branch with the format <JIRA-ID>/<simple-description>, push, open a PR for review, poll CI, and merge when green. Use this skill whenever the user mentions creating a PR, opening a pull request, pushing a branch for review, or linking a Jira ticket to a GitHub PR. Also trigger when the user asks to wait for CI, merge a PR or manage the git workflow around a Jira ticket.

Run a Popeye scan on a Kubernetes/K3s cluster and produce a prioritized analysis. Use this skill whenever the user wants to audit cluster health with Popeye, run popeye, analyze popeye output, triage cluster warnings, check missing probes or resource limits, orphaned services, or review cluster quality score. Also use when the user pastes raw popeye output and asks for analysis, prioritization, or next steps.

Trivy vulnerability remediation workflow. Use this skill when the user asks to fix Trivy vulnerabilities, run a security scan, or remediate CVE findings for a project. Runs trivy CLI scans, groups findings by severity and type, presents a fix plan, and implements fixes in batches with verification.