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metratonmetraton/gaia/skills/verification-rubric/SKILL.md

verification-rubric

Skill4 starsChanged 5 months agoMITView on GitHub

Use when judging a task_gates entry whose verification_type is semantic or self_review -- reading the gate's evidence_shape as an explicit rubric, assessing the produced work against each stated criterion, and emitting a justified pass/fail verdict. Loaded by the verifier role for judgment-based gates.

The file

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4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

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  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Explicit criteria beat holistic impression: Read the rubric as a list of discrete, independently-checkable claims, not one paragraph to …

    line 31
  2. Distinctive:

    Do not let a strong criterion compensate for a failing one (verdict inflation), do not let whichever criterion you read first anchor the …

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  3. Very specific:

    Load the gate: Read the task_gates row: verification_type (semantic|self_review), evidence_shape (the rubric prose or self-review …

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  4. Very specific:

    Gather the evidence: Read the produced work at artifact_path (or wherever the task's outcome lives).

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≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Verification Rubric

Verification-rubric is the judging discipline for a task_gates entry whose verification_type is semantic or self_review: it reads the gate's evidence_shape as an explicit rubric, assesses the produced work against each stated criterion independently, and emits a structured, justified pass/fail verdict -- never a hollow "looks fine." It is the judgment-based counterpart to the deterministic-oracle mode, which re-runs a command/code gate and diffs the result; there is no command to re-run here, only evidence to weigh against criteria.

Core principle

Two of the four verification_type values (gaia.state.VALID_VERIFICATION_TYPES, gaia/state/__init__.py) route to this skill: semantic (evidence_shape carries an externally authored rubric, judged here by the verifier; the contract-envelope's requires_human marker names the same discipline, and a human is the judge only in the two cases gaia-planner step 5 names) and self_review (evidence_shape is the producer's own statement of what it checked -- the envelope's reviewed field is the same idea, and it is the honesty-rule floor from agent-protocol, not a shortcut around it). Both are LLM-as-judge tasks, not code execution.

Three forces shape the judgment:

  • Explicit criteria beat holistic impression. Read the rubric as a list of discrete, independently-checkable claims, not one paragraph to eyeball. A verdict built on an overall feeling collapses the moment someone asks "which part, exactly?"
  • Judge each criterion on its own -- resist position and verbosity bias. Do not let a strong criterion compensate for a failing one (verdict inflation), do not let whichever criterion you read first anchor the whole read (position bias), and do not treat a longer or more polished artifact as automatically meeting more criteria (verbosity bias) -- effort and length are not what the rubric asks for.
  • Confirmed beats assumed. A criterion is "met" only when the produced work was actually observed to satisfy it. An unobserved criterion is not met -- it is unknown, and unknown is not pass (mirrors investigation's confirmed-vs-assumed line).

The judging cycle

  1. Load the gate. Read the task_gates row: verification_type (semantic|self_review), evidence_shape (the rubric prose or self-review statement), artifact_path (the produced work, if any). Run gaia.state.gate_validation.validate_gate first -- a structurally invalid gate (missing/empty evidence_shape) has nothing to judge; return that rejection, not a verdict. A gate with stale_at set keeps an old verdict that predates a change to the gate, its task or a covered AC: judge it afresh, as if pending.
  2. Parse the rubric into criteria. Split evidence_shape into discrete, checkable statements -- one criterion, one claim. A rubric with a single paragraph is read as one criterion; a well-authored rubric names several, and each is graded independently.
  3. Gather the evidence. Read the produced work at artifact_path (or wherever the task's outcome lives). Judge from what actually exists, never from the task's own description of itself.
  4. Assess each criterion independently. For each: is it met, and why -- the observation that grounds the call. An assessment with no stated reason is an assertion, not a judgment.
  5. Aggregate to the overall verdict. Pass requires every criterion met, unless the rubric itself marks one optional/advisory (name that explicitly if so). One unmet, unwaived criterion is fail, regardless of how many others passed.
  6. Emit the structured, justified verdict -- {gate_id, verdict, criteria, overall_reasoning} (see Reference implementation below). Justification is not optional: the rubric exists so the verdict can be defended criterion-by-criterion, not merely announced. A fail is recorded with the cause that says what has to move -- product (the work misses a criterion), broken_test (the rubric is uncheckable, contradicts itself, or leaves the user a judgment gaia-planner step 5 does not give them -- gaia-verifier Domain Errors), requirement_changed (the rubric no longer matches the brief); environment rarely applies to a judgment. The observations go in as evidence tied to the gate, --negative when they refute it.

self_review vs semantic

  • self_review -- evidence_shape is the producer's own statement of what it checked, not a rubric someone else authored. Judge it by the same discipline: does the statement name concrete checks and observations, or is it a hollow "looks good"? A statement that names nothing checked fails the honesty-rule floor regardless of how confident it reads.
  • semantic -- evidence_shape is an externally authored, typically multi-criterion rubric -- the case this skill's cycle is built around.

Reference implementation

skills/verification-rubric/scripts/rubric_verdict.py provides the deterministic assembly half of the cycle: skills/verification-rubric/scripts/rubric_verdict.py::parse_rubric_criteria splits rubric prose into discrete criteria (step 2), and skills/verification-rubric/scripts/rubric_verdict.py::assemble_verdict aggregates a list of CriterionAssessment (criterion, met, reasoning) into a RubricVerdict (verdict, criteria, overall_reasoning), rejecting an empty assessment list and any assessment with blank reasoning -- the honesty rule enforced structurally, mirroring gaia.state.gate_validation.validate_gate's pure/deterministic style. The per-criterion met call -- reading the evidence against the criterion's wording -- is the judge's job (step 4); this module only assembles what has already been judged, it does not automate the judgment.

Anti-patterns

  • Holistic pass -- grading the whole artifact by one overall impression instead of per-criterion. A strong majority does not offset one unmet criterion.
  • Hollow verdict -- pass/fail with no stated reasoning. The rubric exists so the verdict can be defended, not merely declared.
  • Verbosity bias -- crediting a longer or more detailed artifact as automatically meeting more criteria. Judge against the criterion's wording, not against effort.
  • Position bias -- anchoring the whole judgment on whichever criterion was read first instead of giving each an independent look.
  • Treating self_review's floor as license to skip observation -- self_review still requires naming what was checked; "I reviewed it, it's fine" is a shrug, not a review.

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