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 K-Dense-AI/drug-discovery-agent-skills --skill boltzgit clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-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/k-dense-ai/drug-discovery-agent-skills/boltz)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/boltz"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/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/k-dense-ai/drug-discovery-agent-skills/boltz"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/boltz.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Rogue Agent · line 5 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00145 | $0.02178 |
| Opus 5 | $0.00072 | $0.01089 |
| Sonnet 5 | $0.00029 | $0.00436 |
| Haiku 4.5 | $0.00015 | $0.00218 |
Grade A, and why
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 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Boltz-2
An open-weights cofolding model in the AlphaFold3 family, plus something AlphaFold3 does not have: a trained binding-affinity head. Give it a protein sequence and a ligand SMILES and it returns a complex structure, per-interface confidence, and a predicted potency — with weights licensed for commercial use.
Repo: github.com/jwohlwend/boltz Checked against: boltz 2.2.1 (PyPI), Python ≥3.10 <3.13.
Read references/yaml-schema.md before writing an input, references/confidence-and-affinity.md before believing an output, and references/running.md for install, GPU sizing, flags, and errors.
When to reach for this
| Situation | Use |
|---|---|
| No experimental structure, and you need a ligand in the pocket | Boltz — folding and docking in one step |
| Experimental structure exists, defined site, need to rank thousands | autodock-vina |
| Experimental structure exists, need a pose without a box | diffdock |
| Need a defensible free energy for a congeneric series | Alchemical FEP, not this |
| No GPU | tamarind runs Boltz as a service |
The loop
# 1. write the input
python skills/boltz/scripts/make_boltz_yaml.py \
--protein-fasta target.fasta \
--ligand-smiles "Cc1ccc(cc1Nc1nccc(n1)c1cccnc1)NC(=O)c1ccc(CN2CCN(C)CC2)cc1" \
--affinity --out complex.yaml
# 2. predict (external, needs a GPU)
boltz predict complex.yaml --out_dir predictions/ --use_msa_server \
--use_potentials --diffusion_samples 5
# 3. read the results with the units converted
python skills/boltz/scripts/collect_results.py predictions/
name sample confidence_score iptm ligand_iptm binder_probability pIC50 IC50_uM dG_kcal_mol
lig1 0 0.84 0.82 0.79 0.93 8.10 0.0079 -11.05
lig2 0 0.51 0.42 0.31 0.21 4.60 25.1189 -6.27
What ships with it
6 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.
- 12d ago First seen · 163 lines · 145 tokens per session scan A 153841d19cab
boltz is a skill published in the GitHub repository K-Dense-AI/drug-discovery-agent-skills (28 stars, last pushed 5d ago), licensed MIT. It adds 145 tokens to every session and 2,178 once invoked, about $0.0007 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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