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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(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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evidence codes, qualifiers and obsoletion status come from deterministic tooling; never assert from memory that a term is obsolete, merged, or renamed — verify via OLS/QuickGO before any rationale depends on it.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.

レビュー

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同じリポジトリのスキル

概要と使いどころ

Review AI Gene Review pull requests and curation changes for GO term quality, evidence support, core function identification, and annotation action justification. Use for PR review comments, review follow-up automation, and QA of gene review YAML.

日本語の概要は準備中です。原文の説明を表示しています。

ai4curation/ai-gene-review242026年10月13日 更新

Use this agent when you need to systematically review existing GO annotations for a gene and make curation decisions based on literature evidence and functional analysis. This agent should be called after seeding the SPECIES/GENE/GENE-ai-review.yaml file, which seeds each annotation with `action: PENDING`; these should all be manually reviewed.

日本語の概要は準備中です。原文の説明を表示しています。

ai4curation/ai-gene-review242026年10月13日 更新

Use this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.

日本語の概要は準備中です。原文の説明を表示しています。

ai4curation/ai-gene-review242026年10月13日 更新

Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".

日本語の概要は準備中です。原文の説明を表示しています。

ai4curation/ai-gene-review242026年10月13日 更新

boss

無料

Use ONLY when the user explicitly asks to orchestrate parallel external agents (Codex or Claude Code) in tmux sessions via tp (tmux-pilot). Or when a user tells you that you're the boss or orchestrator. You can also use this if you are an operclaw agent. NEVER auto-invoke for a generic "do this in parallel" request. For in-process subagents, use superpowers:dispatching-parallel-agents instead.

日本語の概要は準備中です。原文の説明を表示しています。

ai4curation/ai-gene-review242026年10月13日 更新

Use this agent when you need to synthesize and distill the core functions of a gene from multiple information sources (textual summaries, reviewed annotations, bioinformatics analysis) and populate the `core_functions` section of a gene review YAML file. This agent should be called after completing the gene research, annotation review, and bioinformatics analysis phases of a gene review project.

日本語の概要は準備中です。原文の説明を表示しています。

ai4curation/ai-gene-review242026年10月13日 更新

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