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-docking-screeninggit 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-docking-screening)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-docking-screening"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-docking-screening/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-docking-screening"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-docking-screening.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 | $0.00044 | $0.01147 |
| Opus 5.5 | $0.00018 | $0.00459 |
| Sonnet 5.5 | $0.00009 | $0.00229 |
| Haiku 4.5 | $0.00004 | $0.00115 |
Grade A, and why
molclaw-docking-screening 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Large-Scale Docking Screening Skill
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.
Name
molclaw-docking-screening
Description
This skill performs autonomous, large-scale virtual screening for a protein target using a soft pipeline:
- Drug-likeness filtering (QED and Lipinski)
- QuickVina2
- EquiScore
- Consensus ranking via rank aggregation
It is designed for 10+ molecules and should adapt strategy to input size, target quality, and tool outcomes.
Use this skill when:
- The task is virtual screening for 10+ ligands.
- The user asks for ranking, prioritization, or top-hit selection.
- You need balanced use of physics-based docking and ML rescoring.
Workflow Steps
Stage 0. Input Validation and Setup
- Validate SMILES list is non-empty and count >= 10 for this skill. If <10, still run but skip aggressive prefiltering.
- Determine run mode from task objective:
- complete-ranking mode: user asks for all molecules ranked (common in MolBench-vs).
- top-n mode: user asks for best N only.
- Resolve target structure:
- If receptor_pdb_path exists, use it.
- Else resolve target_chembl_id/uniprot_id and retrieve PDB.
- Optional chain extraction if chain is specified.
- Repair receptor with
molclaw-pdbfixer(add hydrogens, remove waters/heterogens, normalize structure). - Record all chosen settings in an execution summary for reproducibility.
Stage 1. Property Filtering (Adaptive)
- Compute QED and Lipinski violations for all candidates.
- Default filter: QED >= 0.2 and Lipinski violations <= 2.
- Soft adaptation by library size:
- 10-50 molecules: keep default thresholds.
- 51-200 molecules: consider stricter QED (e.g., 0.25-0.30) only if enough survivors remain.
-
200 molecules: apply stronger triage and keep a broad but manageable subset for docking.
- If survivors < max(top_n, 5), relax thresholds once and continue.
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 · 94 lines · 44 tokens per session scan A cf7b53672595
molclaw-docking-screening is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 1,147 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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