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-function-prediction/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-function-prediction)<a href="https://agentmods.dev/skills/ai4curation/ai-gene-review/review-function-prediction"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/review-function-prediction/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-function-prediction"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/review-function-prediction.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 | $0.00099 | $0.03285 |
| Opus 5.5 | $0.00040 | $0.01314 |
| Sonnet 5.5 | $0.00020 | $0.00657 |
| Haiku 4.5 | $0.00010 | $0.00329 |
Grade A, and why
review-function-prediction 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 yesterday.
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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Function Prediction
Use this skill to review computational function predictions against allowed evidence and write schema-compatible PredictionReview YAML.
the predictions are in YAML alongside the main curation review YAMLs:
genes/<TAXON>/<GENE>/<GENE>-<TOOL>-predictions-review.yaml
id: <UniProt accession>
gene_symbol: <gene symbol>
locus_tag: <locus tag if available>
taxon:
id: NCBITaxon:<taxon id>
label: <organism>
status: COMPLETE
description: >-
Brief summary of the prediction review outcome, based only on the allowed
evidence for this review mode.
source_documents:
- <allowed file path used>
predictions:
- source_method: <do not change>
source_version: <do not change>
source_reference_id: <do not change if present>
predicted_term:
id: <do not change>
label: <do not change>
predicted_term_type: <EC | GO_MF | GO_BP | GO_CC>
review:
assessment: <COR | CNN | LSP | UNC | PLI | NPI | REP>
confidence_score: <0 | 1 | 2>
error_type: <omit unless applicable>
summary: >-
Explain the decision in 3-8 sentences. State what allowed evidence was
used, whether the prediction is already present in curated annotations,
whether it is more or less specific than existing annotations, and why
the chosen category is justified. If using UNC, explicitly say what
evidence is missing and avoid speculation.
Selected records with no GO/EC predictions
When a selected benchmark target has no emitted GO/EC predictions in the frozen
source, still write its PredictionReview sidecar with an explicit
predictions: []. Use description for the summary evaluation: identify the
method and snapshot, distinguish absent GO/EC output from any emitted name,
location, or function text, and assess whether well-supported functions were
omitted. Cite the frozen output and decisive biological sources through
source_documents and references, with supporting excerpts in reference
findings where applicable. status: COMPLETE can describe a completed
assessment of the empty output; it does not imply the full gene review is complete.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +13 lines 4ddddf85a41f
- 1mo ago Changed · +24 lines 2dcaf039b70a
- 1mo ago Changed · +106 lines · +16 tokens per session c1f0e9569530
- 1mo ago First seen · 86 lines · 83 tokens per session scan A 2b502e01ff11
review-function-prediction is a skill published in the GitHub repository ai4curation/ai-gene-review (24 stars, last pushed today), licensed BSD-3-Clause. It adds 99 tokens to every session and 3,285 once invoked, about $0.0004 per session on Opus 5.5. 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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