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review

Review, either de-novo, or augment the review of an existing one. A list of deep research providers may be specified.

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SKILL.md (original)

Review the instructions the agent will receive before you install.

Review the gene specified in $ARGUMENTS.

  • ORGANISM should typically be a uniprot species code (in a few cases we use lowercase GO names)
  • GENE_SYMBOL should be the human readable gene symbol for that org

IMPORTANT: you MUST consult the annotation-reviewer.md subagent for this task.

Step 1: Ensure gene data is fetched

Run just fetch-gene ORGANISM GENE_SYMBOL if the gene directory doesn't exist yet.

Step 2: Run deep research AND publication caching in parallel

Publication caching only needs the GOA file (created by fetch-gene), so it can run concurrently with deep research. Launch both at the same time:

  • Deep research: If the user specifies a deep research provider(s), use that provider(s), otherwise default to falcon. Use --fallback perplexity-lite so that if the primary provider times out, it automatically retries with perplexity-lite. E.g. just deep-research-falcon ORGANISM GENE_SYMBOL --fallback perplexity-lite
  • Publication caching: just fetch-gene-pmids ORGANISM GENE_SYMBOL

Run these two steps in parallel (e.g. as concurrent background agents or shell jobs). Do NOT wait for deep research to finish before starting publication caching.

Step 3: Fetch additional data (bacterial organisms)

For bacterial organisms (e.g. PSEPK, ECOLI, SALTY, or any prokaryote), after deep research, also fetch FEBA/RB-TnSeq fitness data if available:

just fetch-fitness ORGANISM GENE_SYMBOL

This creates a GENE-fitness.md file with mutant fitness phenotypes and cofitness partners. The annotation-reviewer agent will use this data as additional evidence when reviewing annotations.

Step 4: Run annotation review

Invoke the annotation-reviewer subagent to systematically review all annotations.

Two standing rules from the annotation-reviewer skill that apply to every review:

  • Do not second-guess deterministic pipeline outputs. Term ids, labels, evidence codes, qualifiers and obsoletion status come from deterministic tooling; never assert from memory that a term is obsolete, merged, or renamed — verify via OLS/QuickGO before any rationale depends on it.
  • Review from the YAML; the GOA tsv is just its seed. The reviewer works from the review YAML's existing_annotations (seeded deterministically from the tsv) and judges each row on "does this gene product execute a function in this process". The tsv never needs to be opened; if it is, ignore the gene-product-to-term relationship type (QUALIFIER column). The only annotation flags that matter are NOT (negated: true) and qualifier: contributes_to, which must be respected. Ortholog source reviews (e.g. genes/human/<GENE>/) are in scope when the defect is on the donor side.

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