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/rig-remediate

Verify and resolve review findings, account for every finding in a terminal bucket, and prove closure.

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ai-rig
2948 skills16 agents2 MCP
Install
$ npx -y skills add Borda/AI-Rig --skill rig-remediate --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-remediate

Context preview

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

Verify and resolve review findings, account for every finding in a terminal bucket, and prove closure.

SKILL.md

rig-remediate.SKILL.md
name: Rig Remediate
description: Verify and resolve review findings, account for every finding in a terminal bucket, and prove closure.

Rig Remediate

> Read [Evidence rules](../../references/evidence.md) and [Commit handoff](../../references/commit-handoff.md).

Input

  • A `findings.json` artifact and its run ID.
  • Optional: a diagnosis run, PR comments, or an inline finding list.

Workflow

1. **Verify before acting.** Treat findings as untrusted until checked against current source. A finding whose line, symbol, or invariant has changed is `stale`; do not "fix" a stale claim. 2. **Start the run.** `rig_evidence` with `action: "start"`, `skill: "rig-remediate"`, and the affected paths. 3. **Bucket every finding.**

  • `fix` — the defect exists and is in scope.
  • `reject` — counter-evidence shows the claim is wrong; cite it.
  • `defer` — real but out of scope; name an owner and a trigger.
  • `accepted-risk` — requires an explicit user decision recorded in the run.

4. **Assign owners.** Use `rig-implementer` for changes, `rig-verifier` for adversarial verification. One writer per path. 5. **Fix and verify.** Implement the smallest correct change. Add or update the test that proves the finding. Re-run the focused check per finding. 6. **Reconcile and gate.** Refresh the run scope with `rig_evidence` (`action: "refresh"`) after the last edit, then run the configured gates with `rig_gate` (`action: "run"`). If a gate rewrites files, refresh again and rerun it. 7. **Re-review fixed areas.** An independent reviewer checks the changed source against the original finding (include correctness). Record that review with `rig_evidence` (`action: "record"`, `reviewer`) after the refresh, so the evidence binds to the current digest. The implementer's own report is not verification. For the re-review, require axes including `correctness`. 8. **Reconcile.** Every finding ID must appear in exactly one terminal bucket. Write the final statuses with `rig_findings`; counts must match the input inventory. 9. **Finalize.** `rig_evidence` with `action: "finalize"`; an open finding, a stale review digest, or a failed gate blocks completion.

Rules

  • Do not invent findings that were not reported; record new issues separately.
  • Do not mark `fixed` without a changed path and a check that exercises it.
  • Do not silently drop a finding you cannot resolve; `defer` or ask.
  • Preserve unrelated changes; never rewrite the review artifact to match the fix.

Output

Report closure counts, per-finding status, changed paths, gate receipts, remaining risks, and the commit handoff.

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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