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 001TMF/blatant-why --skill boltzgengit clone --depth 1 https://github.com/001TMF/blatant-whyWrote 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/001tmf/blatant-why/boltzgen)<a href="https://agentmods.dev/skills/001tmf/blatant-why/boltzgen"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/boltzgen/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/001tmf/blatant-why/boltzgen"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/boltzgen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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.
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 | $0.00003 | $0.06442 |
| Opus 5.5 | $0.00001 | $0.02577 |
| Sonnet 5.5 | $0.00001 | $0.01288 |
| Haiku 4.5 | $0.00000 | $0.00644 |
Grade A, and why
boltzgen scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- ❌ Write inline `subprocess.run(["boltzgen", ...])` calls → use `boltzgen_submit.py` How it starts
The opening of the file, as written. The whole thing — 473 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BoltzGen — Antibody / Nanobody / Binder Design
BoltzGen is an all-atom diffusion model that generates antibody, nanobody,
and de novo miniprotein backbones conditioned on a target structure and a
set of binding (hotspot) residues. Sequences are assigned by AntiFold, then
each design is independently refolded with Protenix to produce ipTM, pTM,
pLDDT, ipSAE, and CA-RMSD metrics. This skill teaches the exact
Write → Bash → Read pattern for invoking BoltzGen via its CLI on local
GPU (default), HPC (RunPod via by-deploy-compute), or Tamarind cloud.
The legacy CLI name was proteus-ab. It has been renamed to BoltzGen
throughout. If you see proteus-ab in older docs or scripts, treat it as
the same engine — the binary on PATH is now boltzgen.
When to Use This Skill
Use BoltzGen when you have:
- ✅ A cleaned target structure (CIF or PDB) with unambiguous chain IDs
- ✅ A defined epitope or hotspot set expressed in
label_seq_idnumbering - ✅ A modality decision in hand — VHH single-domain, full antibody Fab, or de novo miniprotein
- ✅ GPU compute available — local (CUDA-capable, ≥24 GB VRAM), HPC (RunPod), or Tamarind cloud
- ✅ A design budget — typical preview is 10–20 designs; production is 50–200
Do NOT use BoltzGen when:
- ❌ You need to validate a known antibody sequence against a target → use Protenix directly (no design needed)
- ❌ You need to score or filter an existing batch of designs → use by-scoring (ipSAE) and by-screening
- ❌ No epitope is known or characterised → run by-research + by-epitope-analysis first; designing with no
binding_typesis wasteful - ❌ You want a small-molecule binder → BoltzGen designs proteins/peptides only
- ❌ You need ranked, published results → use by-display /
/by:results
Quick Start
A 20-design VHH preview against TNF-α (PDB 1TNF, epitope residues 45–52 and 78–85) on a local GPU:
# Step 1: write spec via the Write tool — see scripts/boltzgen_submit.py for a builder
# Step 2: submit (dry-run prints the command; remove --dry-run to launch)
python scripts/boltzgen_submit.py \
--spec workspace/tnf_spec.yaml \
--protocol nanobody-anything \
--num-designs 20 \
--budget 48 \
--target local \
--output workspace/tnf_out
# Step 3: parse results into a CSV with sequences + metrics
python scripts/parse_designs.py \
--output-dir workspace/tnf_out \
--csv workspace/tnf_designs.csv
What ships with it
5 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.
- 1mo ago First seen · 473 lines · 3 tokens per session scan A 3e84c7c542b4
boltzgen is a skill published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 9d ago), licensed MIT. It adds 3 tokens to every session and 6,442 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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