Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
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/aipoch/medical-research-skillsnpx agentmods add skills/aipoch/medical-research-skills/decision-curve-analysisWrote 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/aipoch/medical-research-skills/decision-curve-analysis)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/decision-curve-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/decision-curve-analysis/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/aipoch/medical-research-skills/decision-curve-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/decision-curve-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Data Exfiltration · line 148 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00064 | $0.02778 |
| Opus 5 | $0.00032 | $0.01389 |
| Sonnet 5 | $0.00013 | $0.00556 |
| Haiku 4.5 | $0.00006 | $0.00278 |
Grade A, and why
decision-curve-analysis 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 13d 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Curve Analysis
When to Use
Use this skill when you need to:
- evaluate whether a binary prediction model adds clinical net benefit across threshold probabilities;
- visualize decision curves and clinical-impact curves from a clinical cohort;
- export an auditable DCA model object together with summary text and PDFs.
Typical user requests:
- "Run decision-curve analysis on this binary outcome and risk-score dataset."
- "Generate DCA and clinical-impact plots for this prediction model."
- "Compare net benefit across thresholds for this case-control cohort."
When Not to Use
Do not use this skill for:
- time-to-event or survival outcomes;
- ROC-only discrimination analysis without decision-curve outputs;
- nomogram construction or calibration-curve analysis;
- multiclass outcomes or non-binary endpoints.
When to Read External Files
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
Statistical methods and formulas |
| Need to run analysis | scripts/main.R |
Get the complete command |
| Encounter errors | references/troubleshooting.md |
Find solutions |
| Need CLI examples | references/cli-guide.md |
Parameter usage examples |
Usage
Rscript scripts/main.R \
--data_file ./clinical_dca_data.csv \
--outcome_col fustat \
--predictor_col riskScore \
--output_dir ./output/
Arguments
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-d |
--data_file |
character | required | Clinical CSV file with row names as sample IDs |
--outcome_col |
character | fustat |
Binary outcome column encoded as 0/1 |
|
--predictor_col |
character | riskScore |
Numeric predictor column used in the logistic DCA model | |
--study_design |
character | case-control |
Study design: case-control or cohort |
|
--population_prevalence |
double | 0.3 |
Population prevalence for case-control DCA (ignored for cohort design) | |
--threshold_by |
double | 0.01 |
Threshold step size; values below 0.005 significantly increase computation time | |
--confidence_level |
double | 0.95 |
Confidence level passed to rmda::decision_curve() |
|
--population_size |
integer | 1000 |
Population size used in the clinical-impact plot | |
--n_cost_benefits |
integer | 8 |
Number of cost-benefit labels in the clinical-impact plot | |
--show_confidence_intervals |
flag | FALSE |
Show confidence intervals on the decision curve | |
--standardize_net_benefit |
flag | FALSE |
Report standardized net benefit (sNB) instead of raw net benefit (NB) |
|
--decision_curve_color |
character | #E64B35 |
Decision-curve line color | |
--impact_colors |
character | #E64B35,#4DBBD5 |
Two comma-separated colors for the clinical-impact plot | |
--plot_width |
double | 6 |
PDF width in inches | |
--plot_height |
double | 5.5 |
PDF height in inches | |
--font_family |
character | sans |
PDF font family | |
--plot_title |
character | Decision Curve Analysis |
Decision-curve plot title | |
--base_cex |
double | 0.9 |
Base text-size multiplier | |
-o |
--output_dir |
character | ./output/ |
Output directory |
--overwrite |
flag | FALSE |
Allow writing into a non-empty output directory | |
-s |
--seed |
integer | 42 |
Random seed for reproducibility |
-T |
--timeout_seconds |
integer | 0 |
Elapsed time limit in seconds; 0 disables timeout |
What ships with it
11 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.
- eval_report_decision-curve-analysis_result.json 13 KB
- references/algorithm.md 2.6 KB
- references/cli-guide.md 2.6 KB
- references/troubleshooting.md 2.4 KB
- scripts/functions.R 4.3 KB
- scripts/main.R 5.4 KB
- scripts/run_analysis.R 4.4 KB
- scripts/utils.R 4.5 KB
- tests/data/dca_data.csv 19 KB
- tests/run_smoke_test.R 1.9 KB
- tests/run_smoke_test.sh 127 B runs code
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.
- 13d ago First seen · 294 lines · 64 tokens per session scan A a029cf4473fe
decision-curve-analysis is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 2,778 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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