ai-gene-review: Skill for Claude Code

Review Function Prediction

Use this skill to review computational function predictions against allowed evidence and write schema-compatible PredictionReview YAML.

the predictions are in YAML alongside the main curation review YAMLs:

genes/<TAXON>/<GENE>/<GENE>-<TOOL>-predictions-review.yaml

id: <UniProt accession>
gene_symbol: <gene symbol>
locus_tag: <locus tag if available>
taxon:
  id: NCBITaxon:<taxon id>
  label: <organism>
status: COMPLETE
description: >-
  Brief summary of the prediction review outcome, based only on the allowed
  evidence for this review mode.
source_documents:
  - <allowed file path used>
predictions:
  - source_method: <do not change>
    source_version: <do not change>
    source_reference_id: <do not change if present>
    predicted_term:
      id: <do not change>
      label: <do not change>
    predicted_term_type: <EC | GO_MF | GO_BP | GO_CC>
    review:
      assessment: <COR | CNN | LSP | UNC | PLI | NPI | REP>
      confidence_score: <0 | 1 | 2>
      error_type: <omit unless applicable>
      summary: >-
        Explain the decision in 3-8 sentences. State what allowed evidence was
        used, whether the prediction is already present in curated annotations,
        whether it is more or less specific than existing annotations, and why
        the chosen category is justified. If using UNC, explicitly say what
        evidence is missing and avoid speculation.

Evidence and reasoning

Separate biological correctness, agreement with existing annotations, and the quality of the evidence supporting the judgment. A plausible conclusion can have an inadequate rationale; repairing the rationale need not change the conclusion.

• Do not use ARBA assertions to bolster correctness. An ARBA-derived GO term, UniProt keyword, or functional description is an association-based prediction, not independent biological validation. It may be recorded as annotation provenance or as a comparison result. Agreement with ProtNLM does not establish correctness. This is not necessarily circular; the problem is treating an unreliable predicted assertion as validating evidence. If the assertion leads to useful underlying evidence, inspect and cite that evidence instead.
• Assess AI-assisted sources by their evidential content. A generated gene description or accepted/core-function label adds no support by repeating a conclusion; use it to locate the underlying evidence. An OpenScientist investigation that integrates sequence, structural, evolutionary, and literature evidence is a substantive analytical synthesis and can carry substantial weight in adjudication. Cite the report directly in references and supported_by, identify its decisive findings and reasoning, and inspect the underlying sources or analysis artifacts for consequential claims where available. Distinguish reported computations from independently reproduced results and experimental validation. Evaluate limitations and disagreements claim by claim; neither accept a bare verdict as proof nor dismiss an integrated investigation because AI produced it. Do not count the report and its underlying sources as independent replications. A source_documents entry alone does not explain evidential weight.
• Anchor decisive claims with source excerpts. Include concise, verbatim supporting_text for sequence features, domain assignments, experiments, and analytical findings used in the rationale. Use explicit ellipses between noncontiguous excerpts. A report's recommendation is not a substitute for the findings used to reach or qualify the assessment.
• Inspect provenance within database records. "UniProt says" is insufficient when the relevant text or keyword was generated by ARBA or another predictor. Distinguish experimental findings, curator-assessed inferences, sequence/domain observations, and automated functional assertions. Do not count several restatements of the same inference as independent lines of evidence. This does not equate PAINT/IBA's curated phylogenetic judgments with ARBA associations; assess an IBA through its ancestral assertion and experimental grounding.
• Family-based inference is legitimate when the transfer is justified. Identify the target's supported family/subfamily placement, cite the characterized relatives and their relevant findings, and explain why the property is expected to be conserved. State that this is an inference rather than an observation on the target. Consider relevant divergence, paralog differences, sequence features, and taxonomic context without inventing exceptions or requiring a new experiment on every target. Strong, well-grounded transfer can support correctness; a generic domain label or an unsupported family stereotype cannot.
• Match evidence to the exact claim. Evidence for extracellular localization does not by itself establish extracellular-matrix residence, matrix structural activity, or matrix organization. Evaluate each prediction's specificity and mechanism separately. For the OLFML2A example, a defensible rationale would identify verified family/sequence evidence and relevant localization findings in characterized relatives, then explain transfer to the target with its limits. Quoting an AI description of a "matricellular regulator" supplies none of those missing steps. This example does not pre-adjudicate either ECM prediction.
• Distinguish activity from participation. Loss of a catalytic domain can refute an intrinsic enzyme activity without refuting participation in the corresponding biological process through a complex or regulatory role. A scaffold need not catalyze the reaction to participate in that process.
• Separate limited usefulness from biological error. A broad but true term is not incorrect merely because a more informative annotation is available. Use LSP only when a supported, more specific annotation actually exists; functions in different GO aspects are not automatically parent/child terms. Annotation overlap alone does not establish training-data contamination, and a common predicted term alone does not establish frequency bias.