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 agentmods add commands/bacc-labs/cynthia-generator/generategit clone --depth 1 https://github.com/BACC-Labs/cynthia-generatorWrote 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/commands/bacc-labs/cynthia-generator/generate)<a href="https://agentmods.dev/commands/bacc-labs/cynthia-generator/generate"><img src="https://agentmods.dev/badge/commands/bacc-labs/cynthia-generator/generate.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.03436 |
| Opus 5 | $0.00008 | $0.01718 |
| Sonnet 5 | $0.00003 | $0.00687 |
| Haiku 4.5 | $0.00002 | $0.00344 |
Grade A, and why
generate scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -o /tmp/cynthia_resp.json -w "%{http_code}" \ How it starts
The opening of the file, as written. The whole thing — 425 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Synthetic Patient Data
Generate FHIR R4-compliant synthetic healthcare data with realistic longitudinal patient histories driven by diagnoses.
Overview
This command creates synthetic patient records for EHR testing without exposing real patient information. Data generation is diagnosis-driven, creating clinically appropriate patterns including encounters, observations, medications, procedures, and other FHIR resources based on the specified conditions.
How to Use This Command
The command works interactively, gathering required information through questions if not provided as arguments.
Interactive Mode (Recommended)
Simply run:
/generate
Claude will ask:
- How many patients to generate
- Primary diagnosis (natural language or ICD-10 code)
- Secondary diagnoses (optional)
- Age range for patients
- Gender distribution
- Ethnicity (optional)
- Time span for patient history
- Whether to export immediately after generation
Command-Line Arguments Mode
Provide parameters upfront:
/generate --patients 10 --diagnosis "Type 2 Diabetes" --age-range 45-65 --gender mixed --timespan 3y
Available Arguments:
--patients N: Number of patients to generate (default: 1)--diagnosis "CONDITION": Primary diagnosis (natural language or ICD-10 code like "E11.9")--secondary "COND1,COND2": Comma-separated secondary diagnoses--age-range MIN-MAX: Age range in years (e.g., "45-65")--gender male|female|other|mixed: Gender distribution--ethnicity "VALUE": Ethnicity (optional)--timespan Ny: Years of patient history (e.g., "3y" for 3 years)--mode local|api: Generation mode (default: local, API integration future)
Instructions for Claude
When executing this command:
Step 1: Gather Parameters
If arguments not provided, use AskUserQuestion to interactively gather:
-
Number of patients (default: 1)
- Ask: "How many patients should I generate?"
- Valid range: 1-100
-
Primary diagnosis
- Ask: "What is the primary diagnosis? (You can use natural language like 'Type 2 Diabetes' or ICD-10 codes like 'E11.9')"
- Accept both natural language and ICD-10 codes
- Use the FHIR healthcare data skill to map natural language to ICD-10
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.
- 6d ago First seen · 425 lines · 15 tokens per session scan A 3b12bea43439
generate is a command published in the GitHub repository BACC-Labs/cynthia-generator (5 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 3,436 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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audit-prompt
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develop-image-prompt.eval
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