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/bioreason-predictions/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/bioreason-predictions)<a href="https://agentmods.dev/skills/ai4curation/ai-gene-review/bioreason-predictions"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/bioreason-predictions/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/bioreason-predictions"><img src="https://agentmods.dev/badge/skills/ai4curation/ai-gene-review/bioreason-predictions.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 | $0.00083 | $0.01782 |
| Opus 5 | $0.00042 | $0.00891 |
| Sonnet 5 | $0.00017 | $0.00356 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
bioreason-predictions 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BioReason-Pro-RL Predictions
About
BioReason-Pro-RL is a pipeline for producing functional summaries of genes, along with reasoning traces, and other raw material (including high recall function predictions)
- Web app: app.bioreason.net (model toggle top-left). Default: RL.
- Paper: doi:10.64898/2026.03.19.712954
We store raw RL exports in:
{GENE}-bioreason-rl-predictions.md
We are primarily interested in the higher precision RL results rather than just Supervised Fine Tuning (SFT)
Structure of a bioreason report
A bioreason report is structured as a markdown doc in these parts:
- Metadata (organism and sequence)
- Thinking/Reasoning trace -- the models exposed internal trace of thoughts as to why it arrived at its conclusions
- Functional summary -- a concise summary of what the function of the gene is
- Other details
- UniProt-style summary (model-generated; not imported from UniProt)
- Interpro domains (from running it as part of pipeline)
- GO Terms (upstream ESM autoregressive transformer predictions -- high recall, low precision)
We are generally NOT interested in the GO terms in this report. We know in advance these over-predict; they are there to show provenance of the reasoner.
File naming
| File | Content |
|---|---|
{GENE}-bioreason-rl-predictions.md |
Raw RL web export (provenance) |
{GENE}-deep-research-bioreason-sft.md |
Raw SFT web export when available (provenance) |
{GENE}-gogpt-leaf-predictions.yaml |
GO-GPT leaf terms as PredictionReview YAML |
{GENE}-bioreason-rl-review.md |
Evaluation of RL reasoning trace vs curated review |
Reviewing a reasoning trace
Create {GENE}-bioreason-rl-review.md with:
# BioReason-Pro RL Review: {GENE} ({organism})
Source: {GENE}-bioreason-rl-predictions.md
- **Correctness**: X/5
- **Completeness**: X/5
## Functional Summary Review
{freeform analysis}
Comparison with interpro2go:
{comparison}
## Notes on thinking trace
{freeform analysis}
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.
- 12d ago First seen · 159 lines · 83 tokens per session scan A 1ac96a9fc5e7
bioreason-predictions is a skill published in the GitHub repository ai4curation/ai-gene-review (24 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 83 tokens to every session and 1,782 once invoked, about $0.0004 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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