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Omh Molecular Docking

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rlaope
omh-molecular-docking

[omh] Prepared PDBQT inputs: run bounded standalone Vina docking with real poses, scores and replay provenance; never infer efficacy or safety. Use when the user says: molecular-docking, rigid-receptor docking, PDBQT docking, ligand docking poses, AutoDock Vina.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-molecular-docking
Stars
3.6K
Forks
269
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Molecular Docking AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-molecular-docking
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-molecular-docking .claude/skills/omh-molecular-docking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Molecular Docking in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Molecular Docking on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Molecular Docking is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Molecular Docking

This is an OMH molecular-docking workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

Input validation and tutorial numbers are not measured docking. Require a real engine search and digest-bound poses, finite energies and replay settings.

First Steps

  • Read toolkit/README.md and references/execution-and-qa.md. Use the explicitly invoked standalone Vina Python helper, not an invented ToolUniverse tool or hidden core execution service.
  • Recover prepared file paths, source/license and preparation receipts, angstrom binding box, seed, CPU, new output directory and host deadline. Never guess a site, silently prepare a molecule or install dependencies on skill load.
  • Use --validate-only for dependency-free prerequisites, or explicitly invoke actual docking in an approved environment with a host-enforced timeout and stdout/stderr capture. Missing dependencies must refuse, not produce guessed findings.

Do Not Use When

  • A general literature question belongs to ulw-research; one supplied paper belongs to omh-paper-learning.
  • A boat reservation, molecular mechanism explanation without computation, drug recommendation or clinical decision is outside this scope.

Examples

Good example:

  • Prompt: Run these prepared PDBQT inputs in this declared box with one CPU and fixed seed, preserving actual poses and replay settings.
  • Expected behavior: Validate inputs, run the explicit optional Vina helper under a host deadline, then read back poses/scores/hashes and state limitations.
  • Why: Actual engine output is distinguishable from preflight and source inspection.

Bad example:

  • Prompt: These scores prove the compound is safe and effective; recommend a dose.
  • Expected behavior: Reject the unsupported inference and do not produce clinical advice.
  • Why: Computational score evidence cannot establish those claims.

Completion Checklist

  • Input/format/resource checks and exact input hashes are observed; source rights and preparation assumptions remain explicit.
  • Actual poses, finite matching energies, backend version/settings, host logs/exit/deadline and replay manifest are read back, or the run is reported BLOCKED without invented results.

Recovery Notes

  • Preserve failed setup receipts. On this host, one authorized correction of the existing native build supplied CONDA_DEFAULT_ENV as well as CONDA_PREFIX/PATH, then Vina 1.2.7 built and the real 1IEP smoke passed; this is not a promise for other platforms.
  • If a native run fails or times out, retain its logs and partial files without a success claim; review the backend failure before retrying in a new output directory.

When to Use

Use when prepared receptor/ligand files and an explicit binding box need a bounded rigid-receptor computational run.

Strong routing signals: `molecular-docking`, `rigid-receptor docking`, `PDBQT docking`, `ligand docking poses`, `AutoDock Vina`

Catalog Metadata

Category: analysis Phase: docking-validation Quality tier: evidence-gated Reasoning demand: standard

Quality bar:

  • Reject symlinks, existing output directories, unsupported formats, non-finite inputs and resource-cap violations before output writes. Preserve existing user files and keep failed-run partial files distinct from success manifests.
  • Retain actual setup and runtime errors. Never fill a missing backend gate with tutorial numbers, fake poses, optimizer output or a success stub.

Required inputs:

  • prepared receptor and ligand PDBQT paths
  • binding box and preparation provenance
  • approved local resource budget

Expert clarification questions:

  • prepared receptor and ligand PDBQT paths
    • English: Which prepared, rights-cleared single-ligand and rigid-receptor PDBQT files are authorized?
    • Korean: Which prepared, rights-cleared single-ligand and rigid-receptor PDBQT files are authorized?

Expected outputs:

  • molecular_docking_preflight/v1
  • molecular_docking_run/v1 and real pose/score files

Artifact expectations:

  • Keep raw inputs, stdout/stderr and pose/score files in the approved evidence directory, outside metadata-only OMH state. Preflight/run/score JSON schemas are standalone file contracts, not new registered operations artifacts.

Safety rules:

  • Never infer efficacy, safety, affinity certainty, target validation, dosing or treatment advice. PDBQT syntax validation is not chemical validity or a preparation review.
  • No hidden execution, network, credentials, global install or paid compute. Dependencies are optional and task-isolated; discovery, routing and skill installation never execute the helper.
  • ToolUniverse registrations were inspected, not invoked. This toolkit uses standalone AutoDock Vina Python only; do not invent a ToolUniverse adapter or tool name.

Procedure: load references/procedure.md.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Record actual tool results, or not_observed / not_available, in the record; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Molecular Docking AI skill do?

[omh] Prepared PDBQT inputs: run bounded standalone Vina docking with real poses, scores and replay provenance; never infer efficacy or safety. Use when the user says: molecular-docking, rigid-receptor docking, PDBQT docking, ligand docking poses, AutoDock Vina.

Why use Omh Molecular Docking on TypingMind?

Because you install it once and use it with any model. Omh Molecular Docking is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Molecular Docking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-molecular-docking. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Omh Molecular Docking?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Molecular Docking?

As many as you like. As long as a model supports skills, you can use Omh Molecular Docking with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Molecular Docking AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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