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 PhenoML/phenoml-skills --skill rwe-analyzegit clone --depth 1 https://github.com/PhenoML/phenoml-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/phenoml/phenoml-skills/rwe-analyze)<a href="https://agentmods.dev/skills/phenoml/phenoml-skills/rwe-analyze"><img src="https://agentmods.dev/badge/skills/phenoml/phenoml-skills/rwe-analyze/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/phenoml/phenoml-skills/rwe-analyze"><img src="https://agentmods.dev/badge/skills/phenoml/phenoml-skills/rwe-analyze.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 | $0.00000 | $0.00982 |
| Opus 5 | $0.00000 | $0.00491 |
| Sonnet 5 | $0.00000 | $0.00196 |
| Haiku 4.5 | $0.00000 | $0.00098 |
Grade A, and why
rwe-analyze 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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RWE Cohort Analysis Skill
This skill provides real-world evidence (RWE) analysis using PhenoML APIs. It enables biopharma analysts to define patient cohorts, generate population statistics, compare cohorts, and assess study feasibility.
How It Works
A single script (fetch_cohort.py) fetches patient data and generates IPS (International Patient Summary) natural language summaries. YOU (Claude) then interpret these summaries to provide whatever analysis the user needs.
When to Use This Skill
Use this skill when users need to:
- Define and analyze a patient cohort from natural language criteria
- Generate population-level statistics (demographics, conditions, medications)
- Compare two patient cohorts (e.g., treatment vs control groups)
- Assess feasibility of a clinical study against a patient population
Prerequisites
Before using this skill, ensure:
- Python 3.10+ is installed
- Required packages are available:
python-dotenv,phenoml - PhenoML credentials are configured (PHENOML_USERNAME, PHENOML_PASSWORD)
Workflow
Step 0: Verify Environment
Always start by checking the environment configuration:
python skills/rwe-analyze/scripts/check_env.py --env-file .env
If credentials are missing, guide the user to set up their .env file with:
- PHENOML_USERNAME
- PHENOML_PASSWORD
- PHENOML_BASE_URL (defaults to https://experiment.app.pheno.ml)
Step 1: Fetch Patient Data
Use the single fetch script for all use cases:
Single cohort:
python skills/rwe-analyze/scripts/fetch_cohort.py \
--cohort "<natural language criteria>" \
--env-file .env
Two cohorts for comparison:
python skills/rwe-analyze/scripts/fetch_cohort.py \
--cohort "<first cohort>" \
--cohort-2 "<second cohort>" \
--env-file .env
Step 2: Analyze the IPS Summaries
The script outputs IPS natural language summaries. YOU (Claude) then analyze them based on what the user asked for:
Population Analysis:
- Total patient count
- Age distribution (mean, range, brackets)
- Gender breakdown
- Most common conditions with prevalence
- Most common medications with prevalence
What ships with it
2 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.
- 10d ago First seen · 121 lines · 0 tokens per session scan A 57df3bedd354
rwe-analyze is a skill published in the GitHub repository PhenoML/phenoml-skills (2 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 982 tokens. 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 2026-08-31.
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