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 PKU-YuanGroup/OpenAI4S --skill alphafold2git clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/alphafold2)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/alphafold2"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/alphafold2/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/pku-yuangroup/openai4s/alphafold2"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/alphafold2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 73 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00117 | $0.01405 |
| Opus 5 | $0.00059 | $0.00702 |
| Sonnet 5 | $0.00023 | $0.00281 |
| Haiku 4.5 | $0.00012 | $0.00140 |
Grade A, and why
alphafold2 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaFold2 (ColabFold runner)
This skill wraps AlphaFold2 and AlphaFold2-Multimer through colabfold_batch,
which replaces DeepMind's local-database MSA pipeline with a call to the public
MMseqs2 server — so a prediction is one command and one FASTA, not a 2 TB
database mount. AF2 remains the reference monomer predictor and the multimer
model is still a strong protein–protein validator, but it does not handle
ligands or nucleic acids; for those, route to boltz, chai1, or openfold3.
The ColabFold code is MIT (github.com/sokrypton/ColabFold) and the AlphaFold2
code is Apache-2.0 (github.com/google-deepmind/alphafold); the AF2 model
parameters are CC-BY-4.0 with DeepMind's terms of use.
Running it
colabfold_batch input.fasta out \
--num-recycle 3 \
--model-type alphafold2_multimer_v3
The input is a plain FASTA. For a complex, put every chain on one sequence
line separated by : — colabfold_batch builds a paired MSA per segment and
runs the multimer model when it sees the colon (so the explicit --model-type alphafold2_multimer_v3 above is belt-and-braces). For monomers omit
--model-type and the colon. --templates and --amber add PDB templates
and OpenMM relaxation respectively; both are off by default and both add
minutes per model.
ColabFold runs all five AF2 model weights by default and ranks them by pLDDT
(pTM/ipTM for multimer), so output per query lands in out/ as five ranked
PDBs <name>_unrelaxed_rank_00{1..5}_*.pdb (b-factor column carries pLDDT)
and a matching <name>_scores_rank_00{N}_*.json with plddt, ptm, and — for
multimer — iptm and the pae matrix. Rank-1 is the model to read first;
ipTM > 0.5 is the usual soft pass for an interface.
Unified-memory defaults loop forever in the GPU sandbox — patch them out
colabfold/batch.py hard-sets TF_FORCE_UNIFIED_MEMORY=1 and
XLA_PYTHON_CLIENT_MEM_FRACTION=4.0 on import. In the confined GPU sandbox
unified memory is unsupported, so JAX's device_put loops indefinitely
allocating host RAM during AF2 parameter load — the job appears hung, never
errors. Override both before the import (TF_FORCE_UNIFIED_MEMORY=0, fraction
0.95), or sed-patch the two assignments out of batch.py in the image
build, or the first fold never starts.
What ships with it
2 files 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.
- 9d ago Changed · +4 lines 137f4859c1ae
- 12d ago First seen · 101 lines · 117 tokens per session scan A db6423588504
alphafold2 is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 117 tokens to every session and 1,405 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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