Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawnpx agentmods add skills/zaoqu-liu/scienceclaw/clinical-decision-supportWrote 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/zaoqu-liu/scienceclaw/clinical-decision-support)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/clinical-decision-support"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/clinical-decision-support/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/zaoqu-liu/scienceclaw/clinical-decision-support"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/clinical-decision-support.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00097 | $0.06054 |
| Opus 5 | $0.00048 | $0.03027 |
| Sonnet 5 | $0.00019 | $0.01211 |
| Haiku 4.5 | $0.00010 | $0.00605 |
Grade A, and why
clinical-decision-support 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 10d 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.
This is a copy
94% identical to clinical-decision-support — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 512 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Decision Support Documents
Description
Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:
- Patient Cohort Analysis - Biomarker-stratified group analyses with statistical outcome comparisons
- Treatment Recommendation Reports - Evidence-based clinical guidelines with GRADE grading and decision algorithms
All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.
Note: For individual patient treatment plans at the bedside, use the treatment-plans skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.
Writing Style: For publication-ready documents targeting medical journals, consult the venue-templates skill's medical_journal_styles.md for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance.
Capabilities
Document Types
Patient Cohort Analysis
- Biomarker-based patient stratification (molecular subtypes, gene expression, IHC)
- Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes)
- Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR)
- Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI)
- Survival analysis with Kaplan-Meier curves and log-rank tests
- Efficacy tables and waterfall plots
- Comparative effectiveness analyses
- Pharmaceutical cohort reporting (trial subgroups, real-world evidence)
Treatment Recommendation Reports
- Evidence-based treatment guidelines for specific disease states
- Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C)
- Quality of evidence assessment (high, moderate, low, very low)
- Treatment algorithm flowcharts with TikZ diagrams
- Line-of-therapy sequencing based on biomarkers
- Decision pathways with clinical and molecular criteria
- Pharmaceutical strategy documents
- Clinical guideline development for medical societies
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.
- 10d ago First seen · 512 lines · 97 tokens per session scan A dba3621421df
clinical-decision-support is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 97 tokens to every session and 6,054 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to clinical-decision-support, differing in 10 lines, and is treated as a copy.
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