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 skills add UnicomAI/wanwu --skill openfold3git 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/openfold3)<a href="https://agentmods.dev/skills/unicomai/wanwu/openfold3"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/openfold3/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/unicomai/wanwu/openfold3"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/openfold3.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 | $0.00060 | $0.02150 |
| Opus 5.5 | $0.00024 | $0.00860 |
| Sonnet 5.5 | $0.00012 | $0.00430 |
| Haiku 4.5 | $0.00006 | $0.00215 |
Grade A, and why
openfold3 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 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.
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 openfold3 — 73 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenFold3 Structure Prediction
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.1+ | 12.4+ |
| GPU VRAM | 24GB | 80GB (H100) |
| RAM | 32GB | 64GB |
| Disk (weights) | 3GB | - |
How to run
Installation
pip install 'openfold3[cuequivariance]==0.4.1'
The default attention kernel is DeepSpeed DS4Sci_EvoformerAttention. If
DeepSpeed is unavailable, switch to the cuEquivariance triangle kernels (no
build-from-source) by overriding the eval memory settings in
model_config.py (use_deepspeed_evo_attention: False,
use_cueq_triangle_kernels: True). Some pre-built environments already ship
this override; check before re-patching.
Weights
Apache-2.0, ~2.3 GB from HF OpenFold/OpenFold3. The repo is gated (auto-approval) — accept the access form on the HF model page and authenticate (huggingface-cli login or HF_TOKEN) before downloading:
export OPENFOLD_CACHE=~/.openfold3
huggingface-cli download OpenFold/OpenFold3 checkpoints/of3-p2-155k.pt \
--local-dir "$OPENFOLD_CACHE"
run_openfold will also auto-download to $OPENFOLD_CACHE on first run if
egress is open and HF credentials are available (either HF_TOKEN or a prior
huggingface-cli login) with repo access granted. The interactive
setup_openfold helper exists but prompts on stdin; prefer the explicit
download above for non-interactive runs.
Running
export OPENFOLD_CACHE=/path/to/cache
run_openfold predict \
--query_json=queries.json \
--output-dir out/ \
--use-msa-server false \
--use-templates false
run_openfold discovers the checkpoint under $OPENFOLD_CACHE automatically.
Only pass --inference-ckpt-path <file.pt> if you have a non-standard layout
or multiple checkpoints and need to pin one explicitly.
For MSA + templates (slower, higher accuracy), drop the two false flags. The
MSA server is api.colabfold.com; template chain-ID remap hits
data.rcsb.org (GraphQL) — both must be reachable.
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.
- 1mo ago First seen · 185 lines · 60 tokens per session scan A eef2b03a2c7e
openfold3 is a skill published in the GitHub repository UnicomAI/wanwu (2,375 stars, last pushed 5d ago), licensed Apache-2.0. It adds 60 tokens to every session and 2,150 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to openfold3, differing in 73 lines, and is treated as a copy.
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