Total 50,369 skills, Code Quality has 2287 skills
Showing 12 of 2287 skills
Enforce language-specific coding standards (Python/TS/JS/Go/Rust/C/C++) + PR/commit conventions.
Perform general code reviews for PRs and code changes. Evaluate code quality, security, and design based on common standards to make approve/reject decisions. Use this for requests like "Review this PR", "Do a code review", "Pre-merge check", or when executing the gh pr view command.
Use when explicitly asked to run the code-reviewer subagent or when another skill requires the code-reviewer agent card.
Run a full Dune app platform review against a React/TypeScript CDF codebase, following the cognitedata/dune-app-reviews scoring criteria. Produces three artifacts: review-files.md (per-file inventory), review-packages.md (dependency audit), and review-report.md (scored report with must/should/nice-fix items). Use when the user asks for a Dune app review, pre-submit review, approval review, app certification review, code quality audit, CDF platform review, or "run dune-review" on a codebase before submission.
Auto-extract patterns from coding sessions, track corrections, and build reusable knowledge with confidence scoring
Semgrep integration. Manage Rules, Scans. Use when the user wants to interact with Semgrep data.
Bug investigation and fix workflow. Triggers: 'debug', 'fix bug', 'investigate issue', 'something is broken', or /debug. Hotfix track for quick fixes, thorough track for root cause analysis. Do NOT use for feature development or refactoring. Do NOT escalate to /ideate unless the fix requires architectural redesign.
Use when writing, fixing, or editing TypeScript code that touches APIs, JSON, environment variables, storage, databases, browser APIs, SDKs, generated clients, or other external boundaries.
Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns.
Optional skill. Reconstruct a human-review-preparation file from an existing pull request, merge request, branch diff, or commit range in a repository the user trusts. Use when the user wants retrospective understanding of already-implemented changes, AI-side assessment and recommendations, and an optional provider-specific sharing variant written to a local file when needed.
Review distributed systems patterns, concurrency, and resilience. Analyzes retry policies, idempotency, timeouts, circuit breakers, and race conditions. Use when reviewing async code, workers, queues, or distributed transactions.
Mandatory analysis workflow for understanding codebase before changes