Total 43,988 skills, Code Quality has 2062 skills
Showing 12 of 2062 skills
Automated, project-wide code coverage and CRAP (Change Risk Anti-Patterns) score analysis for .NET projects with existing unit tests. Auto-detects solution structure, runs coverage collection via `dotnet test` (supports both Microsoft.Testing.Extensions.CodeCoverage and Coverlet), generates reports via ReportGenerator, calculates CRAP scores per method, and surfaces risk hotspots — complex code with low test coverage that is dangerous to modify. Use when the user wants project-wide coverage analysis with risk prioritization, coverage gap identification, CRAP score computation across an entire solution, or to diagnose why coverage is stuck or plateaued and identify what methods are blocking improvement. DO NOT USE FOR: targeted single-method CRAP analysis (use crap-score skill), writing tests, running tests without coverage collection, applying test filters, producing TRX reports, or troubleshooting test execution (use run-tests for all of these).
기존 PeachSolution 모듈을 test-data 패턴으로 리팩토링하는 팀 오케스트레이터 스킬. "팀 리팩토링", "레거시 코드 정리", "test-data 패턴으로 변환" 키워드로 트리거. layer=backend|frontend|all 지원, 독립 QA로 확증 편향 방지.
Local-first code intelligence — structural search, symbol context, impact analysis, and dead code detection via typed graph traversal. Use this skill when you need to search a codebase by intent (not just substring), understand how symbols connect, trace change impact before refactoring, find dead code, or get an architecture briefing. Triggers on: search codebase, find symbol, who calls, blast radius, impact analysis, dead code, code graph, structural search, architecture overview, codebase orientation, understand connections, what depends on, find where.
Create a detailed refactor plan with tiny commits via user interview, then file it as a GitHub issue. Use when user wants to plan a refactor, create a refactoring RFC, or break a refactor into safe incremental steps.
Clarify the user’s intent for vague, incomplete, or ambiguous clauses, statements, and requirements before modifying the code.
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification
Code review using the reviewer agent
Analyze repository and suggest improvements
Identify refactoring opportunities by surfacing architectural friction. Apply the deletion test, deep-modules vocabulary, and seams analysis. Each opportunity becomes its own evanflow-writing-plans cycle. Use when reviewing code for refactoring, when a file has grown too large, or when architecture concerns surface during feature work.
Iterative self-review loop after implementing a plan. Re-read changed code with fresh eyes, fix issues found, re-run quality checks, repeat until clean. For UI work, includes visual verification (view the rendered page). Use after evanflow-executing-plans completes; on success, report and stop — the user decides what's next.
Guidance for receiving and responding to code review feedback. Use when addressing PR review comments, incorporating reviewer suggestions, or managing review discussions.