ai-gene-review: Skill for Claude Code

.claude/skills/review/SKILL.md

review is a skill for Claude Code from ai4curation/ai-gene-review. It costs 28 tokens per session (690 once invoked), scanned A, original, BSD-3-Clause.

A workflow for researching and reviewing a gene, either from scratch or by improving an existing review.

In plain words
What is it for?
Use it to fetch gene data, run research and publication caching, and produce or extend a structured gene review for a specified organism and gene.
Why use it?
It coordinates gene data retrieval, literature caching, deep research, and annotation review so the work follows a repeatable sequence.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions subagents.

This is ai4curation/ai-gene-review's own configuration. It tells Claude Code how to work on ai-gene-review itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-gene-review configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/ai4curation/ai-gene-review/main/.claude/skills/review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ai4curation/ai-gene-review

Made for: Claude Code.

Wrote 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.

agentmods badge for review

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/review/github.svg)](https://agentmods.dev/skills/ai4curation/ai-gene-review/review)
Your own site
<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.

agentmods 80×15 button for review

Your own site · 80×15
<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>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 690 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 6d ago against content hash a1f21ec36d65, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

.claude/skills/review/SKILL.md · 62 lines

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-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.

Read the full file on GitHub · 62 lines

Changes

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.

  1. 6d ago Changed · +15 lines a1f21ec36d65
  2. 11d ago First seen · 47 lines · 28 tokens per session scan A 347d5ae52e50

Subscribe to this mod's changes

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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