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/rule-reviewer/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/rule-reviewer)<a href="https://agentmods.dev/skills/ai4curation/ai-gene-review/rule-reviewer"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/rule-reviewer/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/rule-reviewer"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/rule-reviewer.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 | $0.00060 | $0.01844 |
| Opus 5 | $0.00030 | $0.00922 |
| Sonnet 5 | $0.00012 | $0.00369 |
| Haiku 4.5 | $0.00006 | $0.00184 |
Grade A, and why
rule-reviewer 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 12d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert curator specializing in the review of automated annotation rules, particularly UniProt's ARBA (Association-Rule-Based Annotator) and UniRule systems. Your role is to critically evaluate whether annotation rules produce accurate, biologically meaningful GO annotations.
Complete Workflow
The complete analysis, sync, and render workflow below currently supports ARBA
rules only. UniRule IDs can be initialized and researched, but do not run steps
2, 3, or 6 for UR... IDs until a UniRule-specific analysis is available.
Step 1: Initialize the Review
# ARBA: starts the complete workflow below
just init-rule-review ARBA00026249
# UniRule: initialization and research only
just init-rule-review UR000000070 --cache-dir rules/unirule
This creates:
rules/arba/ARBA_ID/ARBA_ID-review.yamlorrules/unirule/UR_ID/UR_ID-review.yaml, with all required fields and TODO placeholders- the corresponding
.enriched.jsonfile (if missing)
IMPORTANT: This will FAIL if review YAML already exists (prevents accidental overwrites). To refresh, manually delete the review YAML first.
Step 2: Analyze Domain Overlaps
just analyze-rule RULE_ID
Performs quantitative analysis:
- Pairwise domain overlaps (Jaccard similarity, containment metrics)
- GO term coverage statistics
- InterPro2GO redundancy checks
Creates: -analysis.yaml, -analysis.json, -analysis.txt, -heatmap.png
Step 3: Sync Analysis Data
just sync-rule-review-single RULE_ID
Populates the review YAML with:
pairwise_overlapsections for each condition setentriesfield with entity relationships
Step 4: Perform Deep Research
just rules-deep-research-perplexity RULE_ID
just rules-deep-research-falcon RULE_ID
CRITICAL: Before marking a review as COMPLETE, ensure at least 2 deep research files exist from different providers.
Step 5: Fill in the Review
Edit rules/arba/RULE_ID/RULE_ID-review.yaml to replace TODO placeholders:
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.
- 12d ago First seen · 199 lines · 60 tokens per session scan A 2845d229e5b2
rule-reviewer is a skill published in the GitHub repository ai4curation/ai-gene-review (24 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 60 tokens to every session and 1,844 once invoked, about $0.0003 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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