Export top-level ai-gene-review `proposed_new_terms` entries to a deterministic TSV table.
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See how to installReview the instructions the agent will receive before you install.
Use this skill when the user asks for a TSV or tabular extract of ontology term requests recorded in gene review YAML files.
Run the helper from the repository root:
uv run python src/ai_gene_review/tools/export_proposed_terms.py -o reports/proposed_new_terms.tsv
Pass files or directories after the options to restrict the export. With no
inputs, the helper scans genes/**/*-ai-review.yaml.
The TSV defaults to reports/proposed_new_terms.tsv. It has one row per
top-level proposed_new_terms entry and skips reviews where
proposed_new_terms is empty. It includes:
source_path, organism, gene_directory,
review_id, gene_symbol, taxon_id, taxon_labelterm_index, proposed_name, proposed_definition,
justification, proposed_parent_id, proposed_parent_labelproposed_mappings,
supported_byThe exporter intentionally ignores knowledge_gaps[].proposed_terms; those are
nested gap-specific proposals, not the review's top-level ontology request list.
If the user explicitly asks for gap proposals too, write a separate ad hoc
extract with a column that distinguishes top-level terms from gap terms.
For custom column order, filtering, or aggregation, treat the generated TSV as a
staging table and post-process it with Python's csv module or pandas. Preserve
TSV safety by keeping embedded tabs and newlines out of scalar cells.
Treat the TSV as a read-only reporting view, not a round-trip editing source.
Scalar columns are whitespace-normalized for tabular readability; nested
proposed_mappings and supported_by values are compact JSON for inspection.
When updating a review, copy exact supporting_text from the source YAML or
publication cache, not from a TSV extract.
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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.
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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.
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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.
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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".
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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.
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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.
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