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 GGbond-bo/MemOmics-Agent --skill boltzgit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote 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/ggbond-bo/memomics-agent/boltz)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/boltz"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/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/ggbond-bo/memomics-agent/boltz"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/boltz.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 | $0.00053 | $0.01409 |
| Opus 5 | $0.00026 | $0.00705 |
| Sonnet 5 | $0.00011 | $0.00282 |
| Haiku 4.5 | $0.00005 | $0.00141 |
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 7d 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.
This is a copy
89% identical to boltz — 240 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📦 本 skill 由 OpenAI4S (PKU-YuanGroup, MIT/Apache-2.0) 移植。 原仓库: https://github.com/PKU-YuanGroup/OpenAI4S
Boltz-2
Boltz-2 is the open-weights diffusion co-folder closest in surface to
AlphaFold3: a YAML describing protein, DNA, RNA, and ligand chains in, mmCIF
plus pTM/ipTM/pLDDT confidences out, with an optional small-molecule affinity
head. Among our four co-fold skills it is the default for binder-validation
campaigns — fully open MIT weights and the fastest sampler; pick chai1 when
you want a second independent model for consensus, openfold3 when AF3-faithful
settings matter, and esmfold2 when you can live without an MSA. Code and
weights are MIT (PyPI boltz, github.com/jwohlwend/boltz).
Running it
# complex.yaml
version: 1
sequences:
- protein:
id: A
sequence: MVTPEGNVSLVDESLLVGVTDEDRAVRS... # target
- protein:
id: B
sequence: AIQRTPKIQVYSRHPAENG... # binder
- ligand:
id: L
smiles: 'N[C@@H](Cc1ccc(O)cc1)C(=O)O' # or ccd: SAH
boltz predict complex.yaml \
--use_msa_server --out_dir out/ --recycling_steps 3 --diffusion_samples 5
Each protein chain needs an MSA; without one the run exits before the model
loads. --use_msa_server queries api.colabfold.com (expect a 30–90 s pause
per chain) and is the right default unless you already have an .a3m to name
under msa: in the YAML. Setting msa: empty forces single-sequence mode —
that is an accuracy sacrifice, not a speed or memory optimization, because the
MSA search runs on CPU before the GPU stage starts.
Per input the output lands at out/boltz_results_complex/predictions/complex/.
Read confidence_complex_model_0.json first: iptm > 0.5 is the community
pass line for an interface, complex_plddt > 0.7 for the fold itself, and
confidence_score is the weighted aggregate the structures are ranked by.
Structures themselves are complex_model_{0..N-1}.cif (or .pdb with
--output_format pdb).
What ships with it
1 file 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.
- 7d ago First seen · 119 lines · 53 tokens per session scan A 62f342cc03b2
boltz is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 8d ago), licensed MIT. It adds 53 tokens to every session and 1,409 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to boltz, differing in 240 lines, and is treated as a copy.
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