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 InternScience/MolClaw --skill molclaw-diffdock-autogit 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-diffdock-auto)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-diffdock-auto"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-diffdock-auto/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/internscience/molclaw/molclaw-diffdock-auto"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-diffdock-auto.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 | $0.00054 | $0.00280 |
| Opus 5.5 | $0.00022 | $0.00112 |
| Sonnet 5.5 | $0.00011 | $0.00056 |
| Haiku 4.5 | $0.00005 | $0.00028 |
Grade A, and why
molclaw-diffdock-auto 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.
What it actually says
DiffDock Automated Protein-Ligand Docking
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.
⚠ TOOL NOT AVAILABLE: The
diffdock_autotool is not currently deployed on the MCP server. The DiffDock API endpoint is disabled. Do NOT attempt to calldiffdock_auto. Use one of these alternatives instead:
molecule_docking_quickvina_fullprocess— GPU-accelerated AutoDock Vina variant (see skillmolclaw-quickvina-docking)karmadock_tool— ML-based docking (see skillmolclaw-karmadock-tool)
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 · 19 lines · 54 tokens per session scan A fa2cc2527fe3
molclaw-diffdock-auto is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 280 once invoked, about $0.0002 per session on Opus 5.5. 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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