Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Pavel-Kravchenko/Bioinformatics --skill alphafold-structure-predictiongit clone --depth 1 https://github.com/Pavel-Kravchenko/BioinformaticsWrote 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/pavel-kravchenko/bioinformatics/alphafold-structure-prediction)<a href="https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/alphafold-structure-prediction"><img src="https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/alphafold-structure-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/pavel-kravchenko/bioinformatics/alphafold-structure-prediction"><img src="https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/alphafold-structure-prediction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00110 | $0.02770 |
| Opus 5 | $0.00055 | $0.01385 |
| Sonnet 5 | $0.00022 | $0.00554 |
| Haiku 4.5 | $0.00011 | $0.00277 |
Grade B, and why
alphafold-structure-prediction scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post( "https://api.esmatlas.com/foldSequence/v1/pdb/", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(url, timeout=30) The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 238 lines · 110 tokens per session scan B b6dd92fc54af
alphafold-structure-prediction is a skill published in the GitHub repository Pavel-Kravchenko/Bioinformatics (5 stars, last pushed 2mo ago), with no licence file. It adds 110 tokens to every session and 2,770 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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