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/chai1npx skills add UnicomAI/wanwu --skill chai1git 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/chai1)<a href="https://agentmods.dev/skills/unicomai/wanwu/chai1"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/chai1.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 | $0.00098 | $0.01280 |
| Opus 5 | $0.00049 | $0.00640 |
| Sonnet 5 | $0.00020 | $0.00256 |
| Haiku 4.5 | $0.00010 | $0.00128 |
Grade A, and why
chai1 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 chai1 — 8 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chai-1
Chai-1 is an all-atom diffusion co-folder in the same family as Boltz-2 and
AlphaFold3: a multi-entity FASTA in, mmCIF plus pTM/ipTM/pLDDT out, with
protein, RNA, DNA, and SMILES-ligand chains all first-class. It and boltz
cover the same surface; running both and keeping designs that pass either is a
common consensus filter, and Chai's Python entry point makes it the easier of
the two to embed in a loop. Code and weights are Apache-2.0 — commercial use
including drug discovery is explicitly permitted
(github.com/chaidiscovery/chai-lab).
Running it
from pathlib import Path
from chai_lab.chai1 import run_inference
Path("complex.fasta").write_text("""
>protein|name=target
MVTPEGNVSLVDESLLVGVTDEDRAVRS...
>protein|name=binder
AIQRTPKIQVYSRHPAENG...
>ligand|name=cofactor
CCCCCCCCCCCCCC(=O)O
""".strip())
candidates = run_inference(
fasta_file=Path("complex.fasta"),
output_dir=Path("out/"),
num_trunk_recycles=3,
num_diffn_timesteps=200,
seed=42,
device="cuda:0",
use_esm_embeddings=True,
)
print([rd.aggregate_score.item() for rd in candidates.ranking_data])
The FASTA header is >{entity_type}|name={id} with entity_type ∈
{protein, rna, dna, ligand}; ligand records carry a SMILES string as
the sequence body, and modified residues are written inline as
...AAK(SEP)AAG.... From the shell the same job is chai-lab fold complex.fasta out/ --use-msa-server. Without --use-msa-server (or
use_msa_server=True in Python) the model runs on ESM embeddings alone, which
is faster but typically a few ipTM points behind the MSA-backed run.
output_dir receives pred.model_idx_{0..4}.cif plus a matching
scores.model_idx_{N}.npz per sample with aggregate_score, ptm, iptm,
per_chain_ptm, and clash flags. Rank by aggregate_score; treat iptm >
0.5 as a soft pass for an interface. The function refuses a non-empty output_dir, so
clear or rotate it between calls.
Unset CHAI_DOWNLOADS_DIR fails mid-run with PermissionError on a read-only image
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 · 112 lines · 98 tokens per session scan A 14c7b111475c
chai1 is a skill published in the GitHub repository UnicomAI/wanwu (2,458 stars, last pushed yesterday), licensed Apache-2.0. It adds 98 tokens to every session and 1,280 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to chai1, differing in 8 lines, and is treated as a copy.
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