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Found 59 Skills
Detects common LLM coding agent artifacts by spawning 4 parallel subagents
This skill should be used when the user asks to "review PR and save results", "run PR review with documentation", "create PR review document", "review and document PR", "save PR review to docs", "document PR review", or mentions reviewing a PR with the intention of saving the review results. Executes comprehensive PR review using pr-review-toolkit with opus model and posts results as a PR comment.
Gold-standard code review for SAP CC Go repositories against the project's lead review standards. Dispatches 10 domain-specialist agents in parallel — each loads domain-specific references and scans ALL packages for violations in their assigned domain. Produces a prioritized report with REJECTED/CORRECT code examples. Optional --fix mode applies corrections on a worktree branch. This is the definitive "would this code pass lead review?" assessment.
Execute a comprehensive NestJS Project Health Audit. Analyzes tech stack, architecture, API design, data layer, testing, code quality, CI/CD, and documentation. Produces a Google Docs-ready report with section scores and weighted overall score. Use when the user asks to audit a NestJS project, run a health check, evaluate backend quality, or assess technical debt. Triggers on: 'nestjs audit', 'health audit', 'backend audit', 'nestjs health', 'node audit', 'api audit', 'project quality check'.
Comprehensive code review checklist for Go projects. Evaluates code quality, idiomatic patterns, error handling, naming, package structure, and test coverage. Use when reviewing Go code, PRs, or before merging changes. Trigger examples: "review this code", "check this PR", "code review", "review Go file". Do NOT use for security-specific audits (use go-security-audit) or performance-specific analysis (use go-performance-review).
This skill should be used when the user asks to "audit this codebase", "audit this code", "security audit", "code audit", "find vulnerabilities", "check for bugs", "review code quality", "find dead code", "check for anti-patterns", "performance audit", "check for code smells", "technical debt", or "code health check".
Review code for performance: complexity, database/query efficiency, I/O and network cost, memory and allocation behavior, concurrency contention, caching, and latency/throughput regressions. Cognitive-only atomic skill; output is a findings list.
Objective task quality evaluation framework using quantitative KPIs. KPIs are automatically calculated by a hook when task files are modified and saved to TASK-XXX--kpi.json. Use when: reading KPI data for task evaluation, understanding quality metrics, deciding whether to iterate or approve based on data.
Analyzes code architecture and structure — layer violations, circular dependencies, god objects, anemic domain models, missing boundaries, directory structure issues, and configuration problems. Generates severity-scored findings with fix prompts. Trigger phrases: "architecture review", "structure check", "layer analysis", "god class".
Get git records for specified users and days, perform code review for each commit, and generate detailed code review reports
This skill should be used when analyzing technical debt in a codebase, documenting code quality issues, creating technical debt registers, or assessing code maintainability. Use this for identifying code smells, architectural issues, dependency problems, missing documentation, security vulnerabilities, and creating comprehensive technical debt documentation.
Track, categorize, and prioritize technical debt when the user asks to manage tech debt, create a tech debt register, assess code quality, or plan refactoring work