OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
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.
npx skills add LeoYeAI/openclaw-master-skills --skill adme-property-predictorgit clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsWrote 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.
[](https://agentmods.dev/skills/leoyeai/openclaw-master-skills/adme-property-predictor)<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/adme-property-predictor"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/adme-property-predictor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/adme-property-predictor"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/adme-property-predictor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.05626 |
| Opus 5.5 | $0.00024 | $0.02250 |
| Sonnet 5.5 | $0.00012 | $0.01125 |
| Haiku 4.5 | $0.00006 | $0.00563 |
Grade A, and why
adme-property-predictor 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 601 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADME Property Predictor
Overview
Comprehensive pharmacokinetic prediction tool that assesses drug-likeness and ADME properties of small molecules using validated cheminformatics models, molecular descriptors, and structure-property relationships.
Key Capabilities:
- Multi-Property Prediction: Absorption, Distribution, Metabolism, Excretion
- Drug-Likeness Scoring: Lipinski's Rule of 5, Veber rules, QED score
- Batch Processing: Analyze compound libraries efficiently
- Structure-Based Insights: Identify liability hotspots and optimization opportunities
- Comparative Analysis: Rank candidates by predicted PK profile
When to Use
✅ Use this skill when:
- Screening compound libraries for drug-like properties in early discovery
- Prioritizing lead compounds for advancement based on predicted PK
- Identifying ADME liabilities requiring structural optimization
- Comparing analogs to select candidates with optimal ADME profiles
- Filtering virtual screening hits before synthesis
- Generating ADME data for regulatory pre-submission packages
- Teaching pharmacokinetics and drug design principles
❌ Do NOT use when:
- Exact PK parameters needed for dosing → Use experimental PK studies
- Biologics (antibodies, proteins) → Use
antibody-pk-predictor - Natural products with complex structures → Models trained on synthetic small molecules
- Prodrugs requiring metabolic activation → Use
prodrug-activation-predictor - Prediction for clinical dosing decisions → CRITICAL: Experimental validation required
- Assessing toxicity or safety → Use
toxicity-structure-alertoradmetox-predictor
Related Skills:
- 上游:
chemical-structure-converter(structure preparation),lipinski-rule-filter(rule-based filtering) - 下游:
drug-candidate-evaluator(integrated scoring),molecular-dynamics-sim(detailed binding)
Integration with Other Skills
Upstream Skills:
chemical-structure-converter: Convert between SMILES, InChI, MOL formatslipinski-rule-filter: Initial rule-based drug-likeness screeningchemical-structure-converter: Generate 3D conformers for structure-based predictionssmiles-de-salter: Remove salt counterions before analysis
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 601 lines · 61 tokens per session scan A 569bf79f213d
adme-property-predictor is a skill published in the GitHub repository LeoYeAI/openclaw-master-skills (2,168 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 5,626 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
adme-property-predictor compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| adme-property-predictorLeoYeAI/openclaw-master-skills | Skill | 2.2k | 5d ago | MIT | A | 61 | Claude Code |
| arboretoagent-skills-hub/agent-skills-hub | Skill | 128 | 1mo ago | MIT | A | 66 | Claude Code, Codex |
| adaptyvagent-skills-hub/agent-skills-hub | Skill | 128 | 1mo ago | MIT | A | 95 | Claude Code, Codex |
| claude-ally-healthagent-skills-hub/agent-skills-hub | Skill | 128 | 1mo ago | MIT | A | 22 | Claude Code, Codex |
| academic-paper-to-chinese-insight-pdfdavidtoby/agent-skills | Skill | 11 | yesterday | MIT | A | 0 | Claude Code, Codex |
| chinese-pdf-document-workflowsdavidtoby/agent-skills | Skill | 11 | yesterday | MIT | A | 83 | Claude Code, Codex |
| audit-training-experimentbastos/skills | Skill | 7 | 3mo ago | MIT | A | 52 | Codex |