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/internscience/molclaw/molclaw-esmfoldnpx skills add InternScience/MolClaw --skill molclaw-esmfoldgit clone --depth 1 https://github.com/InternScience/MolClawWrote 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/internscience/molclaw/molclaw-esmfold)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-esmfold"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-esmfold.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 | $0.00024 | $0.00261 |
| Opus 5 | $0.00012 | $0.00130 |
| Sonnet 5 | $0.00005 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
molclaw-esmfold 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 5d 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.
What it actually says
Protein Structure Prediction
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transferbefore execution. - For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixerbefore execution. - Please refer to skill
molclaw-scp-serverto complete tool invocation.
The description of tool pred_protein_structure_esmfold.
Use the ESMFold model for protein 3D structure prediction.
Args:
sequence (str): Protein sequence
Return:
status: success/error
msg: message
pdb_path (str): The predicted pdb file path
How to use tool pred_protein_structure_esmfold :
response = await client.session.call_tool(
"pred_protein_structure_esmfold",
arguments={
"sequence": sequence
}
)
result = client.parse_result(response)
pred_protein_structure = result["pdb_path"]
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.
- 5d ago First seen · 40 lines · 24 tokens per session scan A 06283180274c
molclaw-esmfold is a skill published in the GitHub repository InternScience/MolClaw (33 stars, last pushed 29d ago), licensed MIT. It adds 24 tokens to every session and 261 once invoked, about $0.0001 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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