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 BACC-Labs/cynthia-generator --skill fhir-healthcare-datagit 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/skills/bacc-labs/cynthia-generator/fhir-healthcare-data)<a href="https://agentmods.dev/skills/bacc-labs/cynthia-generator/fhir-healthcare-data"><img src="https://agentmods.dev/badge/skills/bacc-labs/cynthia-generator/fhir-healthcare-data/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/bacc-labs/cynthia-generator/fhir-healthcare-data"><img src="https://agentmods.dev/badge/skills/bacc-labs/cynthia-generator/fhir-healthcare-data.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 | $0.00111 | $0.02825 |
| Opus 5 | $0.00056 | $0.01412 |
| Sonnet 5 | $0.00022 | $0.00565 |
| Haiku 4.5 | $0.00011 | $0.00282 |
Grade A, and why
FHIR Healthcare Data Generation 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 12d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FHIR Healthcare Data Generation
Overview
Generate synthetic healthcare data following FHIR (Fast Healthcare Interoperability Resources) R4 specifications. This skill provides knowledge for creating realistic longitudinal patient histories driven by primary and secondary diagnoses, ensuring data patterns match real-world clinical workflows while maintaining HIPAA compliance through synthetic generation.
FHIR is the healthcare industry standard for exchanging electronic health information. All generated data must conform to FHIR R4 resource specifications to ensure compatibility with EHR systems, FHIR servers, and healthcare applications.
When to Use This Skill
Use this skill when:
- Generating synthetic patient data for EHR testing
- Creating FHIR-compliant healthcare records
- Building longitudinal patient histories based on diagnoses
- Simulating realistic clinical patterns and workflows
- Producing test data that avoids HIPAA violations
- Validating healthcare applications with realistic data
Core FHIR Resources
The following FHIR resources form the foundation of comprehensive patient records:
Patient
Demographic information and patient identifiers. Every healthcare record starts with a Patient resource containing name, gender, birth date, address, contact information, and identifiers (MRN, SSN).
Encounter
Healthcare visits including office visits, hospital admissions, emergency department visits, and telehealth appointments. Encounters link to the reason for visit (Condition), observations made, and procedures performed.
Observation
Clinical measurements and lab results including vital signs (blood pressure, heart rate, temperature, oxygen saturation), laboratory values (A1C, glucose, lipid panels, kidney function), and clinical assessments.
Condition
Diagnoses and health problems including primary diagnoses, secondary conditions, complications, and resolved conditions. Each Condition includes ICD-10 codes, onset date, clinical status, and verification status.
What ships with it
15 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.
- examples/allergy.json 2.3 KB
- examples/care-plan.json 6.6 KB
- examples/condition.json 1.7 KB
- examples/diagnostic-report.json 1.7 KB
- examples/encounter.json 2.2 KB
- examples/immunization.json 2.1 KB
- examples/medication.json 1.9 KB
- examples/observation-labs.json 2.0 KB
- examples/observation-vitals.json 1.8 KB
- examples/patient.json 2.0 KB
- examples/procedure.json 1.6 KB
- references/clinical-patterns.md 13 KB
- references/condition-template.md 15 KB
- references/fhir-specifications.md 13 KB
- references/icd10-mappings.md 5.1 KB
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.
- 12d ago First seen · 258 lines · 111 tokens per session scan A 5f16ee0a8b1d
FHIR Healthcare Data Generation is a skill published in the GitHub repository BACC-Labs/cynthia-generator (5 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 2,825 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…
pyhealth
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…
torchdrug
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
deepspot-m
Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
pick-a-pii-model
Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.