molclaw-prolif-docking

molclaw-prolif-docking is a skill for Claude Code, Codex from InternScience/MolClaw. It costs 25 tokens per session (1,099 once invoked), scanned A, original, MIT.

A tool for comparing multiple protein-drug docking poses, which are predicted ways a molecule may fit into a protein.

In plain words
What is it for?
It is for batch analysis of two or more docking poses, producing interaction fingerprints or summaries of interaction counts.
Why use it?
It helps screen different binding modes and compare how consistently each pose interacts with the protein.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Install

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.

agentmods
npx agentmods add skills/internscience/molclaw/molclaw-prolif-docking
Any agent
npx skills add InternScience/MolClaw --skill molclaw-prolif-docking
Clone the repo
git clone --depth 1 https://github.com/InternScience/MolClaw

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for molclaw-prolif-docking

README.md
[![agentmods](https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-prolif-docking.svg)](https://agentmods.dev/skills/internscience/molclaw/molclaw-prolif-docking)
Your own site
<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-prolif-docking"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-prolif-docking.svg" alt="Measured on agentmods" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,099 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.01099
Opus 5 $0.00013 $0.00549
Sonnet 5 $0.00005 $0.00220
Haiku 4.5 $0.00003 $0.00110

Measured 6d ago against content hash 82cf4f248a5b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

molclaw-prolif-docking 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 6d 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.

skills/L1_tools/molclaw-prolif-docking/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ProLIF Docking Pose Analysis Skill

Note:

  • Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution.
  • For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.
  • Please refer to skill molclaw-scp-server to complete tool invocation.

[!NOTE] Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution. For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.

Task Description

Batch-analyze multiple docking-generated binding poses and produce interaction fingerprints or interaction count summaries. Use this skill to screen binding modes and evaluate docking result quality.

Routing note: This tool is the primary choice for batch docking fingerprint comparison (≥ 2 poses). For single-structure deep analysis (one pose, one complex), use molclaw-interaction-visualizer instead — it provides Schrödinger-style 2D diagrams, PyMOL 3D renderings, and decision-ready JSON that this tool does not produce.

Input Source Mapping

Parameter Source Guidance
protein_path Generated by structure retrieval/prediction tools (e.g., retrieve_protein_structure_by_*, pred_protein_structure_esmfold, chai1_predict) or a PDB fixed by fix_pdb
ligand_paths Generated by docking tools (e.g., molecule_docking_quickvina_fullprocess, hdock_tool, karmadock_tool) as pose files (.sdf/.mol2/.pdbqt)
ligand_format Must match the upstream docking output format: sdf, mol2, or pdbqt
template_smiles Required when ligand_format is pdbqt to provide ligand chemistry reference

Usage

Tool: prolif_docking

Summarize docking poses with ProLIF and return a CSV of interaction fingerprints plus summary metrics.
Args:
    protein_path (str): Path to the receptor protein structure.
    ligand_paths (List[str]): List of ligand pose files.
    ligand_format (str): Ligand format identifier (e.g., 'sdf', 'mol2', 'pdbqt').
    template_smiles (str|None): Optional template SMILES; required when ligand_format is 'pdbqt'.
    interactions (List[str]|None): Optional interaction types to compute.
    count (bool): If True, compute interaction counts instead of fingerprints. Default: False.
    vicinity_cutoff (float|None): Optional distance cutoff for vicinity interactions.
    params_json (str|None): Optional JSON parameter file path for ProLIF interaction settings.
Return:
    status (str): 'success' or 'error'.
    msg (str): Human-readable summary or error message.
    command (str): The executed command label ('docking').
    output_dir (str|None): Run-specific directory under tool_result/prolif_result.
    output_file (str|None): Path to the produced CSV summary file.
    n_frames (int|None): Number of processed frames where applicable.
    n_interactions (int|None): Number of interaction columns in output.
    frequent_interactions (List[dict]|None): High-frequency interactions (>30%) with keys 'interaction' and 'frequency'.
    result_summary (dict|None): Full summary dictionary from the wrapper.

Read the full file on GitHub · 103 lines

Changes

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.

  1. 6d ago First seen · 103 lines · 25 tokens per session scan A 82cf4f248a5b

Subscribe to this mod's changes

molclaw-prolif-docking is a skill published in the GitHub repository InternScience/MolClaw (33 stars, last pushed 29d ago), licensed MIT. It adds 25 tokens to every session and 1,099 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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