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Skills diffdock
📦
diffdock
Content revision r1 Low Risk ⚙️ External commands🌐 Network access📁 Filesystem access⚡ Contains scripts
Run DiffDock Molecular Docking
Protein-ligand docking requires careful setup, parameter choices, and result interpretation. This skill guides DiffDock workflows for poses, confidence scores, and screening.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "diffdock" from https://skillstore.io/skills/k-dense-ai-diffdock.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-diffdock/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "diffdock". Dock aspirin against a prepared protein target and explain the expected outputs.
Expected outcome:
The response lists required inputs, recommends a single-complex DiffDock run, and explains ranked pose files plus confidence scores.
Using "diffdock". I have 200 ligands for one target protein. Plan a screening workflow.
Expected outcome:
- Create a batch input table with one row per ligand.
- Validate protein paths, ligand descriptions, and required columns.
- Precompute protein embeddings for repeated target use.
- Rank poses by confidence, then rescore and inspect top candidates.
Using "diffdock". My top DiffDock scores are all low. What should I try next?
Expected outcome:
The response explains likely causes, suggests more samples and ensemble docking, and warns that confidence is not binding affinity.
Security Audit
Low Riskv7 • 7/5/2026 Open versioned report
The static findings are mainly Markdown command examples, public reference URLs, and user-directed helper script input or output. I found no covert network calls, credential access, prompt injection, or unauthorized command execution, but the skill contains one low-risk external platform promotion that should be reviewed.
9
Files scanned
2,188
Lines analyzed
0
Review items
0
False positives ignored
Confirmed security concerns (1)
Low
External Platform Promotion In Skill Instructions
SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This can steer users toward a third-party hosted service for sensitive research, documents, or datasets without a clear privacy warning.
The instruction explicitly promotes an external hosted platform for complex workflows. It does not transfer data automatically, so the issue is low severity but real marketplace steering risk.
Risk Factors
⚙️ External commands (66)
references/workflows_examples.md:26 scripts/setup_check.py:191 SKILL.md:42-45 SKILL.md:45-52 SKILL.md:52-57 SKILL.md:57-60 SKILL.md:60-64 SKILL.md:64-82 SKILL.md:82-88 SKILL.md:88-91 SKILL.md:91-97 SKILL.md:97-100 SKILL.md:100-107 SKILL.md:107-117 SKILL.md:117-123 SKILL.md:123-126 SKILL.md:126-131 SKILL.md:131-134 SKILL.md:134-135 SKILL.md:135-136 SKILL.md:136-137 SKILL.md:137-141 SKILL.md:141-147 SKILL.md:147-152 SKILL.md:152-164 SKILL.md:164-170 SKILL.md:170-185 SKILL.md:185-212 SKILL.md:212-220 SKILL.md:220-230 SKILL.md:230-235 SKILL.md:235-239 SKILL.md:239-243 SKILL.md:243-244 SKILL.md:244-253 SKILL.md:253-261 SKILL.md:261-276 SKILL.md:276-279 SKILL.md:279-285 SKILL.md:285-292 SKILL.md:292-296 SKILL.md:296-325 SKILL.md:325-333 SKILL.md:333-337 SKILL.md:337-341 SKILL.md:341-349 SKILL.md:349-365 SKILL.md:365-368 SKILL.md:368-375 SKILL.md:375-377 SKILL.md:377-382 SKILL.md:382-388 SKILL.md:388-394 SKILL.md:394-396 SKILL.md:396-407 SKILL.md:407-420 SKILL.md:420-433 SKILL.md:433-435 SKILL.md:435-440 SKILL.md:440-448 SKILL.md:448-449 SKILL.md:449-464 SKILL.md:464-467 SKILL.md:467-470 SKILL.md:470-473 SKILL.md:62
🌐 Network access (10)
📁 Filesystem access (2)
⚡ Contains scripts (1)
Audited by: codex View Audit History →
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https://skillstore.io/skills/k-dense-ai-diffdock/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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APA citation
K-Dense-AI. (2026). diffdock security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-diffdock/audits/7BibTeX citation
@techreport{k-dense-ai-k-dense-ai-diffdock-2026,
author = {K-Dense-AI},
title = {diffdock security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/k-dense-ai-diffdock/audits/7},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "diffdock security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-diffdock/audits/7"
identifiers:
- type: other
value: "skillstore:k-dense-ai-diffdock:audit:7"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Highest Skillstore Score
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
davila7 Recommended
davila7-diffdock
Skillstore Score 85
Evidence Confidence High
Skillstore usage 27
Updated
2026-09-09
K-Dense-AI Current
k-dense-ai-diffdock
Skillstore Score 81
Evidence Confidence High
Skillstore usage 15
Updated
2026-09-09
Skillstore Score
Why this score Evidence Confidence: High 82
Architecture
85
Maintainability
87
Content
70
Community
83
Spec Compliance
What You Can Build
Dock One Candidate Ligand
Prepare a protein target and ligand input, run DiffDock, and inspect the top predicted binding poses.
Screen A Compound Set
Build and validate a batch input table, run multiple docking jobs, and rank outputs by confidence score.
Tune Docking Parameters
Adjust sampling, inference steps, and temperature settings for flexible ligands or difficult protein systems.
Try These Prompts
Begin Single Docking
Help me dock one ligand to one protein with DiffDock. I have a PDB file and a SMILES string.
Prepare Batch Inputs
Create a DiffDock batch plan for these protein-ligand pairs and tell me how to validate the CSV before docking.
Interpret Docking Results
Review my DiffDock confidence scores and pose files. Explain which predictions are most reliable and what needs validation.
Optimize A Screening Workflow
Design an efficient DiffDock virtual screening workflow with precomputed embeddings, sampling choices, and downstream rescoring.
Best Practices
- Validate protein structures, ligand formats, and batch CSV files before starting long docking jobs.
- Generate multiple samples and inspect top poses visually before downstream scoring.
- Use confidence scores for triage, then confirm with rescoring, controls, and experiments.
Avoid
- Treating DiffDock confidence as binding affinity or final activity evidence.
- Running large virtual screens before testing one known protein-ligand case.
- Ignoring missing residues, unrealistic protonation states, or poor ligand preparation.
Frequently Asked Questions
Does this skill predict binding affinity?
No. It predicts binding poses and model confidence. Use separate scoring or experimental assays for affinity.
Can I use a protein sequence instead of a PDB file?
Yes. DiffDock can use ESMFold to create a structure from sequence, but this adds runtime and uncertainty.
What inputs are supported for ligands?
The skill documents SMILES strings and structure files such as SDF or MOL2 when supported by RDKit.
Is a GPU required?
A GPU is not strictly required, but CPU runs can be too slow for practical docking or screening.
How should I use confidence scores?
Use them to rank and triage poses. Review structures and use complementary scoring before making decisions.
Can this handle virtual screening?
Yes. The skill includes batch CSV guidance and recommends precomputed embeddings for repeated target screening.
Developer Details
Author
K-Dense-AILicense
MIT license
Skillstore revision
r1
Version notice
The author did not declare a version.
Repository
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/diffdockRef
a25199bc7d6b82598536822d1738eb5d5f54025b
Maintenance freshness
7/18/2026
Usage
12 downloads · 209 views
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