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.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00764 |
| Opus 5 | $0.00020 | $0.00382 |
| Sonnet 5 | $0.00008 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00076 |
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 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 — 57 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. AlphaFold3 is intentionally not part of this stack (non-commercial license, large VRAM footprint, no clear quality gap for the workflows in this repo). Use:
- Boltz-2 (MIT license; CUDA; NVIDIA cuEquivariance kernels) as the default predictor — joint structure-and-affinity, ~1000× faster than FEP for binding-affinity estimation, comparable accuracy to AF3 on benchmarked complexes.
- ColabFold v1.5.5+ with an MMseqs2-GPU MSA backend when a wider MSA than Boltz-2 builds is required (≈31.8× faster MSA generation versus the standard AF2 pipeline; Nature Protocols 2025, DOI: 10.1038/s41596-024-01060-5).
- ESMFold for fast monomer pre-screening only (15–20 GB VRAM; lower accuracy than Boltz-2).
- Search predicted or experimental structures with Foldseek v9+. Use
--gpu 1on CUDA Turing or newer for the ProstT5-backed search (4–27× speedup). Consider Foldseek-Multimer when complex-vs-complex search is needed. - 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. Public MSA services receive biological sequences;--use-msa-serveris 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 ... |
Input Requirements
Prerequisites:
- Tools declared in the project's pinned Pixi environment. See
docs/README.mdfor expected tools. - Reference DB root: set
BIO_DB_ROOTto the project or site-local database directory. - Protein FASTA inputs are available. Inputs:
- proteins.faa (FASTA protein sequences)
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.
- yesterday Changed · +7 tokens per session 147033ec4c90
- 7d 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 (7 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 764 once invoked, about $0.0002 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 2026-08-31.
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