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/agentd-drug-discovery

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openclaw-medical-skills
2.9k200 skills
Install
$ npx -y skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill agentd-drug-discovery --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/agentd-drug-discovery

Context preview

The summary Claude sees to decide when to auto-load this skill.

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SKILL.md

agentd-drug-discovery.SKILL.md

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COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

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This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

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Provenance: Authenticated by MD BABU MIA

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--- name: agentd-drug-discovery description: Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks. allowed-tools:

  • read_file
  • run_shell_command

---

At-a-Glance

  • **description (10-20 chars):** Hypothesis foundry
  • **keywords:** ligand-design, SAR, ADMET, docking, ranking
  • **measurable_outcome:** Generate ≥10 candidate molecules (or requested count) with SMILES, key properties, and rationales per run, all delivered within 15 minutes.

Inputs

  • `target_protein`, optional `reference_compound`, disease `indication`.
  • `constraints` dict (LogP, MW, TPSA, etc.) and `num_candidates`.

Outputs

1. Ranked candidate list with SMILES + property scores + novelty metrics. 2. ADMET/toxicity alerts and SAR rationale per molecule. 3. Reproducibility manifest (data source versions, model checkpoints).

Workflow

1. **Evidence retrieval:** Mine literature + databases for known ligands and liabilities. 2. **Generate candidates:** Run AgentD generative step (scaffold hopping/fragment growth) aligned to constraints. 3. **Score & filter:** Apply Lipinski/QED/ADMET heuristics; include docking setup when requested. 4. **Rank & explain:** Combine efficacy, developability, novelty; summarize SAR learnings. 5. **Deliver outputs:** Emit JSON/CSV plus narrative recommendations; mark as in silico.

Guardrails

  • Clearly state outputs are hypothetical and need wet-lab validation.
  • Flag PAINS/reactive motifs automatically.
  • Record data/model versions for audit trails.

References

  • Detailed parameter tables and dependencies listed in `README.md`.

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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The largest open-source medical AI skill library for OpenClaw.

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Repo: FreedomIntelligence/OpenClaw-Medical-Skills