investigating-repository-history
FeaturedInvestigate GitHub repository history before risky code changes using git blame/log, GitHub PRs, review comments, squash/rebase/cherry-pick/rename heuristics, and cited evidence. Use when asking why code exists, whether a change is safe, what PR introduced behavior, or before editing API, compatibility, security, concurrency, persistence, migration, or performance-sensitive code.
Install
Quality Score: 90/100
Skill Content
Details
- Author
- CodeAlive-AI
- Repository
- CodeAlive-AI/ai-driven-development
- Created
- 4 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Related Skills
code-reviewer
Analyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.
security-reviewer
Identifies security vulnerabilities, generates structured audit reports with severity ratings, and provides actionable remediation guidance. Use when conducting security audits, reviewing code for vulnerabilities, or analyzing infrastructure security. Invoke for SAST scans, penetration testing, DevSecOps practices, cloud security reviews, dependency audits, secrets scanning, or compliance checks. Produces vulnerability reports, prioritized recommendations, and compliance checklists.
parallel-investigation
Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.