Borrowing it
Nothing to install: this file belongs to ai4curation/ai-gene-review. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ai4curation/ai-gene-review/main/.claude/skills/review/SKILL.mdgit clone --depth 1 https://github.com/ai4curation/ai-gene-reviewWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ai4curation/ai-gene-review/review)<a href="https://agentmods.dev/skills/ai4curation/ai-gene-review/review"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ai4curation/ai-gene-review/review"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.00690 |
| Opus 5 | $0.00014 | $0.00345 |
| Sonnet 5 | $0.00006 | $0.00138 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-liteso 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) andqualifier: 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago Changed · +15 lines a1f21ec36d65
- 11d ago First seen · 47 lines · 28 tokens per session scan A 347d5ae52e50
review is a skill published in the GitHub repository ai4curation/ai-gene-review (24 stars, last pushed today), licensed BSD-3-Clause. It adds 28 tokens to every session and 690 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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