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/rig-review

Review the current diff, a commit, or a path with independent read-only reviewers and a validated findings artifact.

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ai-rig
2948 skills16 agents2 MCP
Install
$ npx -y skills add Borda/AI-Rig --skill rig-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/rig-review

Context preview

The summary Claude sees to decide when to auto-load this skill.

Review the current diff, a commit, or a path with independent read-only reviewers and a validated findings artifact.

SKILL.md

rig-review.SKILL.md
name: Rig Review
description: Review the current diff, a commit, or a path with independent read-only reviewers and a validated findings artifact.

Rig Review

> Read [Evidence rules](../../references/evidence.md). This workflow is read-only; it never writes to GitHub.

Input

  • `workspace` (default): staged and unstaged changes.
  • `path <path>`: one file or directory.
  • `commit <rev>`: a committed range.
  • `pr <number|url>`: optional PR mode when `gh` is authenticated.

Workflow

1. **Classify.** `TRIVIAL`, `LOCAL`, `BROAD`, or `HIGH_RISK`. Risk categories set depth; they do not grant execution authority. 2. **Start the run.** `rig_evidence` with `action: "start"`, `skill: "rig-review"`, and the exact target paths. 3. **Collect exact source.** Record the diff and the complete bytes of every reviewed file. For `workspace`, prefer a detached review worktree so later edits cannot silently change the evidence. A digest change invalidates the review. 4. **Select axes.** `correctness` is mandatory and must be run (cannot be omitted). Add `security` for auth, credentials, deserialization, or network input; `tests` when behavior changes; `api` for public interfaces; `performance`, `docs`, and `data` when the diff warrants. Cap at four axes below `BROAD`. Also ensure coverage records what was actually inspected for correctness. 5. **Dispatch independent reviewers.** Use `rig-reviewer` (or `rig_dispatch`). Give each only the task, role card, exact source digests, and assigned axis. Reviewers are read-only and return JSON findings; the parent never counts its own reading as reviewer evidence. 6. **Validate findings.** Reject any finding without a concrete file and observed evidence. De-duplicate by signature (file + invariant), not by wording. Use `rig_findings` with `action: "write"`, the axes, the coverage, and `reviewer` naming the independent reviewer(s). That write records the review evidence bound to the scope digest. 7. **Report.** Order by severity. State the axes that ran, the axes that did not, and why. An honest partial review beats a fabricated complete one. 8. **Finalize.** `rig_evidence` with `action: "finalize"`. Open findings are a valid review outcome; they are not a completion failure. Source drift is: start a new review instead of certifying changed bytes.

Rules

  • Never write to the repository or GitHub during review.
  • Do not fix findings here; hand them to `rig-remediate`.
  • A finding must cite source that was actually read; pattern-matched suspicion is labeled as suspicion.
  • If an independent reviewer is unavailable, say so and cap confidence; do not substitute the parent's own analysis.

Output

Report run ID, scope digest, axes run and skipped, findings by severity, coverage limits, and confidence.

Read more
Ships withai-rig

Practical agent workflows for Python, ML, and open-source maintenance. AI-Rig turns recurring work—scoping a change, reproducing a bug, reviewing a pull request, running an experiment, or checking release readiness—into explicit workflows with specialist

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