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/openfold3npx 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.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 | $0.00060 | $0.02150 |
| Opus 5 | $0.00030 | $0.01075 |
| Sonnet 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 4d 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.
- 4d ago First seen · 185 lines · 60 tokens per session scan A eef2b03a2c7e
openfold3 is a skill published in the GitHub repository UnicomAI/wanwu (2,455 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 2,150 once invoked, about $0.0003 per session on Opus 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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