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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-boltzgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-boltz)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-boltz"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-boltz/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/alterlab-ieu/alterlab-academic-skills/alterlab-boltz"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-boltz.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 | $0.00163 | $0.01104 |
| Opus 5 | $0.00081 | $0.00552 |
| Sonnet 5 | $0.00033 | $0.00221 |
| Haiku 4.5 | $0.00016 | $0.00110 |
Grade A, and why
alterlab-boltz 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 11d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Boltz-2 (open AlphaFold3-style co-folding)
Overview
Boltz-2 (Passaro, Wohlwend et al. 2025; jwohlwend/boltz) is an open, commercially usable
biomolecular structure model in the AlphaFold3 family: it co-folds proteins together with
small-molecule ligands, nucleic acids, and multiple chains in a single prediction, and can
predict binding affinity — capabilities AlphaFold2/ColabFold does not have. Use it when the
biology is a complex with a ligand or other molecule types, not a bare protein.
When to Use This Skill
Use this skill when the user wants to:
- Co-fold a protein with a small-molecule ligand (SMILES or CCD code) into a holo complex.
- Predict a binding affinity alongside a co-folded pose.
- Fold protein–nucleic-acid or multi-entity assemblies in one pass.
- Get an open AlphaFold3-style prediction without proprietary access.
Does NOT Trigger
| Scenario | Use instead |
|---|---|
| Protein-only or protein–protein folding, no ligand | alterlab-alphafold |
| Antibody–antigen / general one-FASTA multi-entity complex | alterlab-chai |
| Dock a ligand into an existing, fixed receptor structure | alterlab-diffdock |
| Retrieve an experimentally determined structure | alterlab-pdb |
| Design a binding-pocket sequence around a ligand | alterlab-ligandmpnn |
Core Capabilities
1. Protein + ligand co-folding
Describe the complex in a YAML spec (chains + ligand by SMILES or CCD), then predict:
# complex.yaml (schema — TODO(verify) against installed boltz)
version: 1
sequences:
- protein: { id: A, sequence: "MKT...GGG" }
- ligand: { id: L, smiles: "CC(=O)Oc1ccccc1C(=O)O" }
boltz predict complex.yaml --out_dir out/ --use_msa_server
Outputs the co-folded structure (protein + placed ligand) plus per-model confidence.
--use_msa_server fetches the protein MSA from the hosted service (disclose for sensitive
sequences); a local MSA can be supplied instead.
2. Binding-affinity prediction
What ships with it
2 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.
- 11d ago First seen · 87 lines · 163 tokens per session scan A 565089b2e570
alterlab-boltz is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 163 tokens to every session and 1,104 once invoked, about $0.0008 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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