execution-grounded-review

Execution-Grounded Review

A normal review reads the diff and asks "does this look right?" — and an implementation can look complete while a criterion silently fails. This skill refuses to grade an implementation on appearance: it runs the suite first, then traces each acceptance criterion to execution evidence — a test that actually exercised it, or observed behaviour — and marks anything it cannot back with execution as UNVERIFIED rather than passing it on faith.

It is the execution-grounded verifier in the agent-patterns review family: where adversarial-review attacks a design for faults and cold-read-gate measures whether text survives a reader, this skill verifies that running code meets its stated acceptance criteria. It is the reusable independent verifier a judgement-based loop gate delegates to (.claude/rules/loop-integrity.md, Pillar 1).

When to Use This Skill

The Stance

Model is opus — like adversarial-review, the inverse of cold-read-gate: building an accurate requirement→evidence ledger is a reasoning task.

Parameters

Parse $ARGUMENTS:

• Target (first positional) — what to verify: a path, a PR ref (#123 or URL), explicit files, or absent. If absent, default to the current change (git diff HEAD + staged) and say so.
• --criteria <file> (optional) — a file of acceptance criteria. If absent, gather criteria from the task/plan in context (the acceptance criteria stated for this change) and echo them back before verifying, so the user can correct the list the skill is grading against.

Execution

Execute this execution-grounded verification:

Step 1: Run the suite first

Before reading the diff for "correctness", establish ground truth by execution. Detect and run the project's full suite + typecheck + lint, capturing output to a scratch file (this is the execution evidence the verifier grades against):

Record the exit codes and failing-test names. A red suite is itself an independent signal — a failing test does not care how hard the author worked (.claude/rules/loop-integrity.md).

Step 2: Name the criteria

State, in one numbered list, the acceptance criteria under verification (from --criteria or context). If the list is empty, stop and ask for it — there is nothing to ground a verdict in. Each criterion is one ledger row in Step 3.

Step 3: Dispatch the intent-starved verifier

One Agent, model: opus, reading only the criteria, the diff, and the captured execution-evidence file — not the author's reasoning. Bind its output to the LEDGER schema below and paste that schema verbatim into the brief: a schema forces a determinate answer on every row where prose lets a row go quietly unanswered.

The LEDGER schema

{
  "type": "object",
  "required": ["rows", "coverage", "verdict"],
  "properties": {
    "rows": {
      "type": "array",
      "items": {
        "type": "object",
        "required": ["criterion", "evidence", "verdict", "sequenceMatchesProduction"],
        "properties": {
          "criterion": { "type": "string" },
          "evidence": { "type": "string" },
          "verdict": { "enum": ["PASS", "FAIL", "PARTIAL", "UNVERIFIED"] },
          "sequenceMatchesProduction": { "enum": ["yes", "no", "not-applicable"] }
        }
      }
    },
    "coverage": { "type": "string" },
    "verdict": { "enum": ["pass", "fail"] }
  }
}

evidence is the test name / file:line / observed output drawn from the execution-evidence file, or the literal none. coverage is <#rows with PASS/FAIL evidence> / <total rows>.

sequenceMatchesProduction is required on every row, and that requirement is the entire gain of the schema. Step 3a's check is the one a verifier skips silently when it is only prose, because a green test looks like evidence — a required enum makes "I did not check" unrepresentable.

The overall verdict is pass only when every row is PASS and no row is sequenceMatchesProduction: "no". Any FAIL, any UNVERIFIED, or any sequence divergence makes it fail.

Template:

subagent_type: general-purpose
model: opus
prompt: |
  You verify whether an implementation meets its acceptance criteria, grounded
  in EXECUTION EVIDENCE. Read ONLY these inputs (no other files, no repo
  exploration beyond resolving evidence cited below):
    - Acceptance criteria: <numbered list from Step 2>
    - The change under review: <diff / file paths>
    - Execution evidence (suite/typecheck/lint output): <scratch file path>

  Do NOT read the author's plan, commit narrative, or rationale — grade the
  behaviour, not the intent.

  Emit ONE object conforming to this schema, with one row per criterion:
    <paste the LEDGER schema verbatim>

  For every row, decide `sequenceMatchesProduction` explicitly: identify what
  real call sequence exercises the claim in production and confirm the test
  reproduces that sequence, not just a convenient shorter one.
  Cite evidence for every row. Your final message is the deliverable.

No workflow harness here — deliberately. The schema is the whole delta; agent count stays at one. This skill is the Pillar-1 oracle other loops delegate their stop condition to, so a fan-out design would multiply through every iteration of every loop in the repo — the one place where per-invocation cost compounds rather than adds (.claude/rules/workflow-vs-skill.md).

For several independent targets, dispatch one verifier per target in a single-message parallel Agent batch — except on a [1m] model, where the concurrent-subagent rate-limit caveat applies (skill-fork-context.md); run those sequentially. Do not set context: fork — the caller needs the ledger in the main context to act on it.

Step 3a: Match the test's operation sequence to production

"A passing test exists for this claim" is not sufficient evidence for a round-trip, determinism, reproducibility, or idempotence claim. The test can pass while the design is broken, because a narrower hand-built repro silently avoids the exact ordering that would expose a divergence. The tell is when the test's sequence of operations differs from the real call path production uses.

So for any such claim, add a step to the verifier's brief:

Identify what real call sequence exercises this claim in production, and confirm the test under review reproduces that sequence — not just a convenient shorter one.

Stateful / RNG-dependent code is the high-risk class — lazy initialization, global mutable RNG state, and caching all defer or share observable state, so when an operation runs relative to its neighbours changes the result. A test of the shape construct → forward immediately and a production path of construct → generate batches (consuming lazy RNG) → first forward draw their deferred state at the same relative point in each stream, so the round-trip "works" with no error — while a real trained model's reload diverges. The passing test is real; it just exercises the one ordering that can't see the bug.

Grade such a claim UNVERIFIED until the test reproduces the production sequence, even though a green test exists — that is the row whose sequenceMatchesProduction is no.

Step 4: Triage against over-correction

The verifier grades strictly, so guard both failure modes before acting — neither talk yourself into passing broken code, nor into failing correct code:

Step 5: Report and bound the loop

Emit the ledger: target, per-criterion rows with evidence, COVERAGE, and the overall verdict. Apply or hand off the genuine fixes (closing UNVERIFIED rows by adding the missing test counts as a fix). Re-run from Step 1 only if the verdict was fail; do not loop more than twice — a third round means a structural problem the gate can't resolve, which is the signal to surface to a human, not to keep grinding.

Anti-patterns

Related

• adversarial-review — attacks a design for faults; this skill verifies running behaviour against criteria
• verify-before-plan — verifies premises before work; this verifies outcomes after
• cold-read-gate — the isolation + triage + bounded-loop pattern this skill reuses (legibility lens; uses haiku)
• code-quality-plugin:code-review — the first-pass review this layers on top of
• workflow-orchestration-plugin:workflow-checkpoint-refactor — a loop whose phase gate delegates its independent verdict here
• .claude/rules/loop-integrity.md — Pillar 1: a loop's stop condition is judged by an independent verifier like this one, not the worker. Keep this literal path in the body: scripts/check-loop-integrity.sh requires the token loop-integrity.md in this file, so a later "tighten the Related section" edit that drops it fails the build.