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-hdock-toolgit 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-hdock-tool)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-hdock-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-hdock-tool/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-hdock-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-hdock-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.01104 |
| Opus 5.5 | $0.00019 | $0.00442 |
| Sonnet 5.5 | $0.00010 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade A, and why
molclaw-hdock-tool 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.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HDOCK Protein Docking
Invocation
Use the exact tool name and parameter schema exposed by the current harness. Tool names in SDK examples below are raw MCP names, not model-facing aliases. Local files must be uploaded with molclaw-file-transfer before a server tool can use them. Repair PDB inputs first when the workflow requires it.
Usage
1. HDOCK Protein Docking
The description of tool hdock_tool.
Run HDOCKlite protein-protein/protein-peptide docking and return the unique run directory, key files, and summary metrics for structure-based screening workflows. Inputs must be non-degenerate PDB coordinate files; SDF/PDBQT inputs are rejected before HDOCK is launched. The server chooses the output directory and may remap the partner chain in its internal copy to avoid receptor/partner chain collisions.
Args:
receptor (str): Receptor PDB file path.
ligand (str): Ligand or partner PDB file path.
nmax (int): Number of docking models to generate (default 100).
no_complex (bool): Disable complex structure generation (default False).
angle (int): Rotation sampling interval in degrees (default 15).
rsite (str|None): Optional receptor binding-site residue file.
lsite (str|None): Optional ligand binding-site residue file.
Return:
status (str): success, partial_success, or error execution status.
msg (str): Human-readable execution summary.
output_dir (str): Unique run directory under tool_result/hdock_tool_result.
receptor (str): Resolved receptor input path.
ligand (str): Resolved ligand input path.
nmax (int): Effective model count upper bound used.
no_complex (bool): Effective no-complex flag used.
angle (int): Effective angle parameter used.
rsite (str|None): Effective receptor site file used.
lsite (str|None): Effective ligand site file used.
output_files (dict): Key generated file paths such as Hdock.out, topN.pdb, and best models.
partner_chains (List[str]): Partner chain IDs present in generated complex models. For protein-peptide complexes, pass output_files["best_model_pdb"] and partner_chains[0] to interaction_visualizer(mode="peptide").
metrics (dict): Summary metrics including generated model count and best docking score when available.
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 Changed · -1 lines · +24 tokens per session 147ff248ff35
- 1mo ago First seen · 105 lines · 24 tokens per session scan A 64c7f0b01759
molclaw-hdock-tool is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,104 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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