molclaw-p2rank

molclaw-p2rank is a skill for Claude Code, Codex from InternScience/MolClaw. It costs 31 tokens per session (380 once invoked), scanned A, original, MIT.

A tool guide for finding likely ligand-binding pockets in a protein structure. A binding pocket is a place on a protein where another molecule may attach; P2Rank predicts these locations from a PDB structure file.

In plain words
What is it for?
Uploading and preparing protein structure files, predicting pocket locations, and retrieving each pocket's confidence and three-dimensional center coordinates.
Why use it?
It identifies candidate sites for later molecular docking, which tests how molecules might fit onto a protein.

Skill for Claude CodeCodex

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

Good fit Uploading and preparing protein structure files, predicting pocket locations, and retrieving each pocket's confidence and three-dimensional center coordinates.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/internscience/molclaw/molclaw-p2rank
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.

Any agent
npx skills add InternScience/MolClaw --skill molclaw-p2rank
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-p2rank

README.md
[![agentmods](https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-p2rank.svg)](https://agentmods.dev/skills/internscience/molclaw/molclaw-p2rank)
Your own site
<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-p2rank"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-p2rank.svg" alt="Measured on agentmods" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 380 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00031 $0.00380
Opus 5 $0.00015 $0.00190
Sonnet 5 $0.00006 $0.00076
Haiku 4.5 $0.00003 $0.00038

Measured 8d ago against content hash 86a721f0cfcf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

molclaw-p2rank 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 8d 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-p2rank/SKILL.md · 45 lines

What it actually says

Pocket Location

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.

The description of tool pred_pocket_prank.

Use P2Rank to predict ligand binding pockets in the input protein.
Args:
    pdb_file_path (str): Path to the protein structure file (PDB format)
Return:
    status (str): success/error
    msg (str): message
    pred_pockets (List[dict]): List of dict, each containing pocket confidence and center position information. The first pocket (pred_pockets[0]) has the highest score and is usually used for molecular docking.
        --site_id (str): Pocket id
        --probability (float): Predicted confidence score (0~1) of the pocket
        --center_x (float): Center X of the pocket
        --center_y (float): Center Y of the pocket 
        --center_z (float): Center Z of the pocket

How to use tool pred_pocket_prank :

response = await client.session.call_tool(
    "pred_pocket_prank",
    arguments={
        "pdb_file_path": pdb_file_path
    }
)
result = client.parse_result(response)
pred_pockets = result["pred_pockets"]
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. 8d ago First seen · 45 lines · 31 tokens per session scan A 86a721f0cfcf

Subscribe to this mod's changes

molclaw-p2rank is a skill published in the GitHub repository InternScience/MolClaw (33 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 380 once invoked, about $0.0002 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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