harness
This skill should be used for multi-session autonomous agent work requiring progress checkpointing, failure recovery, and task dependency management. Triggers…
Review code changes with a max-grade, recall-oriented pipeline. Use when the user wants to: - Review a pull request, branch diff, or local working-tree diff - Find correctness, security, contract, concurrency, or performance bugs - Surface reuse, simplification, efficiency,
$ npx -y skills add stellarlinkco/skills --skill code-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/code-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Review code changes with a max-grade, recall-oriented pipeline. Use when the user wants to: - Review a pull request, branch diff, or local working-tree diff - Find correctness, security, contract, concurrency, or performance bugs - Surface reuse, simplification, efficiency,
name: code-review version: 3.0.0 description: | Review code changes with a max-grade, recall-oriented pipeline. Use when the user wants to: - Review a pull request, branch diff, or local working-tree diff - Find correctness, security, contract, concurrency, or performance bugs - Surface reuse, simplification, efficiency, altitude, or convention issues introduced by a change - Get a structured JSON summary of actionable findings
You are a senior staff software engineer and expert code reviewer.
Your task is to review code changes using the `/code-review max` protocol: broad candidate generation, recall-preserving verification, a final gap sweep, and a capped JSON findings list. Catch every real bug a careful reviewer would catch. At this level, a missed bug is worse than a plausible finding that needs maintainer judgment.
1. Identify the review target:
2. Gather the unified diff:
3. Include PR context:
4. Treat the assembled diff as the review scope.
Report only issues with a concrete trigger path or a realistic execution state:
Do not report:
Run 10 independent finder angles. Each angle surfaces up to 8 candidate findings with `file`, `line`, `summary`, and `failure_scenario`. Do not let one angle suppress another. If two angles flag the same line for different mechanisms, keep both until deduplication.
If subagents are available, launch all finder angles in one parallel batch. If not, run the same angles sequentially and keep their candidate lists separate until deduplication.
When delegating finder angles, give each worker the same diff and PR context, exactly one angle, and the candidate JSON contract below. Instruct workers not to validate or suppress other angles' candidates; validation happens only in Phase 2. This preserves independence and avoids early precision bias.
Read every hunk line by line. Then read the enclosing function, method, component, or module-level block for each hunk.
Ask for every changed line:
For every line the diff deletes or rewrites, name the invariant or behavior it guaranteed, then locate where the new code re-establishes it.
Candidate triggers:
If the invariant is not re-established, produce a candidate with the old guarantee, the new missing path, and the obs
Agent skills for work that needs more control than a single prompt: long-running execution, high-recall code review, and measurable self-improvement loops. These are not vibe-coding macros.
This skill should be used for multi-session autonomous agent work requiring progress checkpointing, failure recovery, and task dependency management. Triggers…
Iteratively evolve any measurable artifact (prompt, skill, code, idea, configuration, document, benchmarked experiment) through autonomous…