CUMCM Independent Review

SkillDev tools

Independently review a CUMCM computation package before its claims enter validation or paper writing. Use only inside a generated independent-review package.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the CUMCM Independent Review skill

What this skill tells your AI

The instructions your AI receives, as published by lucasuiii/cumcm-workflow in .agents/skills/cumcm-workflow/assets/independent-review/SKILL.md and read by ahel’s review.

Review this freshness-bound package without consulting the originating conversation. Treat conclusions as untrusted and reconstruct only what is needed from official inputs, contracts, selected source, official runs, and outputs.

Boundaries

  • Work read-only inside the package.
  • Do not search for missing official materials. Report them as missing.
  • Do not edit, rerun, or replace the preserved execution unless the user separately authorizes a reproduction run.
  • File existence and successful execution do not prove that the model answers the official question.
  • Verify the package/upstream bindings before substantive review; a stale package is inconclusive.
  • Give exact file, formula, code, or numerical locations for every P0/P1 finding.
  • Preserve negative and inconclusive findings verbatim.

Review order

  1. Read REVIEW_REQUEST.md and materials/problem/SOURCE_MANIFEST.json. In targeted mode, also read package-root TARGETED_FINDINGS.json; it is the self-contained prior-P0 brief, so do not request the complete prior review.
  2. Reconstruct each subproblem from the supplied official files and PROBLEM_FACTS.json.
  3. Compare the official request with the model objective, variables, constraints, assumptions, and cross-question dependencies.
  4. Inspect computation entry points, run manifests, executed outputs, and result locators.
  5. Challenge relevant failure classes:
    • task or target misunderstood;
    • upper/lower bound or optimization direction reversed;
    • a quantity counted twice;
    • unsupported extrapolation;
    • an observed variable omitted without justification;
    • cross-question contradiction;
    • code and mathematical formulation disagree;
    • numerical output violates units, bounds, conservation, or official constraints.
  6. For targeted mode, resolve every entry in TARGETED_FINDINGS.json first. Do not repeat a full review unless the target change has global impact or current evidence reveals a new, well-supported P0.
  7. Write the raw review and structured result using the supplied template.

Verdict

  • accepted: no open P0 and no material unresolved concern in scope.
  • accepted_with_concerns: no open P0; one or more P1 concerns remain.
  • revision_required: at least one open P0 requires returning to the earliest affected stage.
  • inconclusive: required material is missing or the available evidence cannot support a decision.

Classify findings as P0/P1/P2 and open/resolved/accepted_concern. State the reviewer, model if applicable, originating/reviewer task references, and independence grade. Never describe same-context review as independent; a same-model fresh task remains correlated.

Signals

GitHub stars
76
Forks
5
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
cumcm-independent-review
Source
github.com/lucasuiii/cumcm-workflow