Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.
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 agentmods add skills/unicomai/wanwu/boltznpx skills add UnicomAI/wanwu --skill boltzgit clone --depth 1 https://github.com/UnicomAI/wanwuWrote 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/unicomai/wanwu/boltz)<a href="https://agentmods.dev/skills/unicomai/wanwu/boltz"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/boltz.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 | $0.00088 | $0.01475 |
| Opus 5 | $0.00044 | $0.00737 |
| Sonnet 5 | $0.00018 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00147 |
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 6d 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
100% identical to boltz — 18 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
Affinity head
Add a properties: block naming one ligand chain as the binder and Boltz-2
predicts protein–small-molecule binding affinity alongside the structure:
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.
- 6d ago First seen · 123 lines · 88 tokens per session scan A 4205a0836395
boltz is a skill published in the GitHub repository UnicomAI/wanwu (2,456 stars, last pushed yesterday), licensed Apache-2.0. It adds 88 tokens to every session and 1,475 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to boltz, differing in 18 lines, and is treated as a copy.
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