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Found 112 Skills
Re-reads code you just wrote with fresh perspective to catch bugs, errors, and issues. Use after completing a feature, fixing a bug, or any code changes. Triggers on "review my code", "fresh eyes", "check for bugs", "did I miss anything", or "sanity check".
Technical solution evaluation and code review in the style of Linus Torvalds. Only use this when the user explicitly requests a Linus-style review or explicitly asks for a rigorous evaluation of code changes/technical solutions (e.g., "review changes/code", "evaluate if the solution is appropriate", "check submission standards", "linus-tech-review").
Analyzes git commits and changes within a timeframe or commit range, providing structured summaries for code review, retrospectives, work logs, or session documentation.
Runs a Trailmark structural review gate over a branch, pull request, fix commit, release diff, or git ref range to detect new entrypoints, new tainted paths, removed validation or authorization calls, privilege-boundary drift, blast-radius growth, complexity growth, and newly reachable sensitive sinks. Use when reviewing a PR, branch, remediation commit, or release diff where graph-level security regressions should be checked before merge.
Remove AI-generated code slop from a branch. Use when cleaning up AI-generated code, removing unnecessary comments, defensive checks, or type casts. Checks diff against main and fixes style inconsistencies.
Review only git diff for impact, regression, correctness, compatibility, and side effects. Scope-only atomic skill; output is a findings list for aggregation.
Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.
Guide for making code reviews. Use this when asked to make code reviews, or ask to use it before committing changes.
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.
Validate changesets in openai-agents-js using LLM judgment against git diffs (including uncommitted local changes). Use when packages/ or .changeset/ are modified, or when verifying PR changeset compliance and bump level.
Generate conventional commit messages automatically. Use when user runs git commit, stages changes, or asks for commit message help. Analyzes git diff to create clear, descriptive conventional commit messages. Triggers on git commit, staged changes, commit message requests.
Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.