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Second opinion bench

Skill runsagents/second-opinion-bench

Measure whether a second, independent reviewer model catches complementary defects in the second-opinion-bench synthetic corpus.From its SKILL.md

Install
npx -y skills add runsagents/second-opinion-bench

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.6 KB, 754 tokens by cl100k_base, as published. Nobody here has run it

Second Opinion Bench

Use this skill when asked to produce benchmark verdicts with the current model or to grade verdicts already produced elsewhere.

Integrity rules

  1. The harness is model-agnostic and local. Never add a live model call to src/; verdict generation happens in the host agent.
  2. During review, read only fixtures/diffs/*/change.diff. Do not read any ground-truth.json, prior verdict, result, or example report.
  3. Review every fixture independently. Do not infer that each diff must contain exactly one issue, even though the current corpus does.
  4. Freeze the raw finding summaries before opening ground truth. After freezing, map only an already-recorded finding to a planted defect ID when the semantics clearly match. Never create, expand, or delete a finding because of ground truth.
  5. Give every unmatched finding a stable reviewer-proposed ID namespaced to the fixture. The grader must be allowed to count it as a false positive.
  6. Never describe a simulated, hand-authored, incomplete, or ground-truth-exposed run as measured model performance.

Produce verdicts with this model

Choose a reviewer label that identifies the actual model/version and material review configuration. Create reviews/<reviewer>/.

For each fixture directory, in isolation:

  1. Read change.diff only.
  2. Look for correctness, edge-case, security, monetary, concurrency, and behavior-contract defects. Record only defects supported by the diff.
  3. Freeze a list of concise summaries. An empty list is allowed.
  4. Only now read that fixture’s ground-truth.json to map a frozen summary to an exact planted ID. Preserve unmatched summaries under reviewer-proposed IDs.
  5. Save reviews/<reviewer>/<fixtureId>.json conforming to schemas/verdict.schema.json:
{
  "reviewer": "provider-model-version-prompt-v1",
  "fixtureId": "edge-empty-average",
  "foundDefects": [
    {
      "id": "edge-empty-average/reduce-without-seed",
      "summary": "Empty input calls reduce without an initial value."
    }
  ]
}

Write one verdict for every fixture, including "foundDefects": [] when no defect was found. Do not copy the demonstration verdicts.

To test a different model, run this protocol again in a fresh agent context using that model. Do not expose the new reviewer to another reviewer’s files or results until its verdicts are complete.

Grade sealed verdicts

After all reviewers have sealed their files, run:

node src/run-bench.mjs \
  --reviews reviews \
  --output bench-result.json \
  --report bench-report.md

The command reads JSON files only; it does not contact a model or provider. Treat any coverage warning as an incomplete run, not as a zero-finding review.

Report honestly

State:

  • reviewer labels and enough model/configuration detail to reproduce them;
  • fixture coverage for every reviewer;
  • overall and per-class recall with numerators and denominators;
  • false-positive counts;
  • pair union recall;
  • both directional uplifts: what B adds after A and what A adds after B;
  • whether any verdict was simulated, hand-authored, post-edited, or exposed to ground truth before findings were frozen;
  • the limitations that fixtures are synthetic and the grader matches IDs, not semantics.

Do not say “model B is better” from this corpus alone. A defensible statement is narrower: “On this 12-defect synthetic corpus, under the recorded setup, B added N planted defects that A missed.”

What ships with it: 67 files

75.8 KB alongside SKILL.md, 7 of them executable

examples/

27 more files not listed here. See all 67 in the repository.

Gives 0 of the 12 instructions most review quality skills give in 754 tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • Ask questions one at a timein 81 of 1048, across 64 files
  • Provide a recommended answer for each questionin 73 of 1048, across 50 files
  • Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • Interview the user relentlessly about the planin 38 of 1048, across 13 files
  • Order findings by severityin 31 of 1048
  • Resolve each branch of the decision treein 27 of 1048, across 5 files
  • Run a grilling sessionin 26 of 1048, across 5 files
  • Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • Create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

Said here and by no other author read

  • review every fixture independently
  • freeze finding summaries before opening ground truth
  • map frozen findings to planted defect ids
  • give unmatched findings stable proposed ids
  • write one verdict file per fixture
  • run the bench grader script after sealing verdicts

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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