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 fmschulz/omics-skills --skill bio-structure-annotationgit clone --depth 1 https://github.com/fmschulz/omics-skillsWrote 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/fmschulz/omics-skills/bio-structure-annotation)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/bio-structure-annotation"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-structure-annotation/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/fmschulz/omics-skills/bio-structure-annotation"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-structure-annotation.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 | $0.00041 | $0.01066 |
| Opus 5.5 | $0.00016 | $0.00426 |
| Sonnet 5.5 | $0.00008 | $0.00213 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
bio-structure-annotation 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 23d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bio Structure Annotation
Structure prediction and structure-based annotation.
Instructions
Tool guides and versions: docs/README.md.
- Run a fast embedding screen with TM-Vec to triage candidate proteins by remote homology before incurring structure-prediction cost.
- Predict structures on a GPU node through
sbatch. AlphaFold3 is not part of this stack (non-commercial license, large VRAM footprint). Use:- Boltz-2 (MIT license, CUDA) as the default predictor for monomers, complexes, and protein-ligand binding affinity.
- ColabFold v1.5.5+ with an MMseqs2-GPU MSA backend when a deeper MSA than Boltz-2 builds is required. Kallenborn et al. (2025) Nature Methods https://doi.org/10.1038/s41592-025-02819-8 report that MMseqs2-GPU makes ColabFold prediction 31.8× faster than the standard AlphaFold2 pipeline.
- ESMFold for fast monomer pre-screening only; route candidates to Boltz-2 for final models.
- Search predicted or experimental structures with Foldseek v9+. On CUDA Turing or newer,
--gpu 1enables the GPU ungapped prefilter and needs a target database built withmakepaddedseqdb; the same paper reports a 4-27× Foldseek speedup. Useeasy-multimersearchfor complex-vs-complex search. - Annotate hits and route high-value unknowns back to
/bio-annotationfor sequence-side context, or to comparative analyses via/bio-protein-clustering-pangenome. - Build and validate commands with
scripts/run_structure_annotation.py. It prints the command plan as JSON and, with--execute, runs it. Public MSA services receive biological sequences;--use-msa-server(passed to Boltz as--use_msa_server) is rejected unless the user explicitly approved upload with--approve-public-msa-upload.
Quick Reference
| Task | Action |
|---|---|
| Validate and plan | uv run --script skills/bio-structure-annotation/scripts/run_structure_annotation.py --boltz-yaml complex.yaml --out-dir results/bio-structure-annotation |
| Structure search | add --foldseek-query query.pdb --foldseek-db targetDB (with --gpu, pass --foldseek-padded-db) |
| Embedding screen | add --tmvec-query proteins.faa --tmvec-db db.npz |
What ships with it
8 files 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.
- 23d ago Changed · +12 lines · +1 tokens per session 836f519e50a6
- 1mo ago Changed · +7 tokens per session 147033ec4c90
- 1mo ago First seen · 57 lines · 33 tokens per session scan A 92c425fa53ec
bio-structure-annotation is a skill published in the GitHub repository fmschulz/omics-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 41 tokens to every session and 1,066 once invoked, about $0.0002 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 2026-08-31.
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