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-equiscore-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-equiscore-tool)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-equiscore-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-equiscore-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-equiscore-tool"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-equiscore-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 98 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00086 | $0.02272 |
| Opus 5.5 | $0.00034 | $0.00909 |
| Sonnet 5.5 | $0.00017 | $0.00454 |
| Haiku 4.5 | $0.00009 | $0.00227 |
Grade A, and why
molclaw-equiscore-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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EquiScore Multi-Tool Workflow
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.
[!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 usingmolclaw-fix-pdbbefore execution.
Usage
1. Pocket Extraction
The description of tool equiscore_pocket.
Extract binding pockets from docking results and prepare split single-molecule SDFs for EquiScore screening.
Args:
docking_result (str): Path to a docking-result SDF file.
receptor_pdb (str): Path to the receptor PDB file.
pocket_cutoff (float|None): Optional numeric cutoff for pocket detection.
dry_run (bool|None): If True, validate inputs and prepare outputs without executing EquiScore.
Return:
status (str): 'success' or 'error'.
msg (str): Human-readable summary or error message.
command (str): The subcommand executed ('get_pocket').
run_dir (str|None): Run-specific directory under tool_result/equiscore_result.
single_sdf_dir (str|None): Path to directory containing split single-molecule SDFs.
pocket_dir (str|None): Path to the generated pocket folder.
split_sdf_count (int|None): Number of split SDF files created.
pocket_item_count (int|None): Number of pocket entries generated.
sample_single_sdfs (List[str]|None): Sample single-SDF filenames.
sample_pockets (List[str]|None): Sample pocket directory names.
How to use tool equiscore_pocket :
response = await client.session.call_tool(
"equiscore_pocket",
arguments={
"docking_result": "relative/path/to/docking_result.sdf",
"receptor_pdb": "relative/path/to/receptor.pdb",
"pocket_cutoff": 10.0,
"dry_run": True
}
)
result = client.parse_result(response)
key_output = result["single_sdf_dir"]
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 · +56 tokens per session 3ddf3bb30e88
- 1mo ago First seen · 246 lines · 30 tokens per session scan A 357023211a57
molclaw-equiscore-tool is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 2,272 once invoked, about $0.0003 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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