delaunay

Skill providing the exact structured YAML review format that all reviewers must use. Enables automated parsing, scoring, action-item extraction, and convergence detection.

Skill for enforcing review blinding — ensuring reviewers cannot see each other's reports, managing worktree isolation, handling optional author anonymization, and controlling unblinding at synthesis.

Programmatic validation of figure data and generation code. Since LLMs cannot interpret rendered images, this skill ensures figure correctness by analysing the underlying data and plotting code — catching identical-value plots, missing labels, broken references, and other common defects.

Skill for determining whether the review process has converged (accept), needs another round (iterate), or has reached diminishing returns (exhausted).

Skill for managing the full peer review lifecycle: intake validation, reviewer dispatch, review collection, editorial synthesis, revision cycles, re-review, and convergence determination.

Skill for venue-calibrated scoring, score weighting formulas, decision rules, anti-bias mechanisms, and score interpretation across different venue tiers.

Skill for managing review state persistence — the .review/ directory structure, state.yaml lifecycle tracking, artifact hashing, round management, and final output generation.

Skill for validating submission folder structure, metadata completeness, abstract quality, claim falsifiability, code presence, and paper compilation.