Fhirlint

pip install cognis-fhirlint
fhirlint scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ fhirlint-emit --version
fhirlint 0.1.0

$ fhirlint-emit --help
usage: fhirlint [-h] [--version] {validate} ...

FHIRLINT - fast, JSON-native FHIR R4 resource/bundle linter with line-level errors.

positional arguments:
  {validate}
    validate  validate one or more FHIR R4 JSON files (use '-' for stdin)

options:
  -h, --help  show this help message and exit
  --version   show program's version number and exit

Examples:
  python -m fhirlint validate patient.json
  python -m fhirlint validate bundle.json --format json
  cat patient.json | python -m fhirlint validate -

Blocks above are real fhirlint output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Activity",
        "description": "Anomalous network traffic detected from IP 192.168.1.100",
        "severity": "high",
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Malware Detection",
        "description": "Malware detected on system with IP 192.168.1.101",
        "severity": "critical",
        "created_at": "2023-02-15T14:31:00Z"
    }
]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install fhirlint
   

2. Validate a single FHIR R4 resource (use - for stdin):

   fhirlint validate patient.json
   cat patient.json | fhirlint validate -
   

3. Validate a bundle or several files at once:

   fhirlint validate bundle.json
   fhirlint validate a.json b.json c.json
   

4. Read the output: the table prints OK per clean file, or per-finding lines with SEVERITY, line number, path, message and [code], plus a count summary. Use --format json to read files[].findings[] and the top-level ok flag. Exit codes: 0 clean (warnings allowed), 1 error-severity findings, 2 usage/file error.
5. Gate CI on validity:

   fhirlint validate bundle.json --format json > fhir-report.json
   

Contents

• Why fhirlint? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why fhirlint?

A fast, JSON-native linter with developer-friendly output and a GitHub Action — replaces the clunky Java validator in CI pipelines.

fhirlint is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Lint Obj
• ✅ Lint Text
• ✅ Lint File
• ✅ Has Errors
• ✅ Summarize
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-fhirlint
fhirlint --version
fhirlint scan .                       # scan current project
fhirlint scan . --format json         # machine-readable
fhirlint scan . --fail-on high        # CI gate (non-zero exit)

Example

$ fhirlint scan .
  [HIGH    ] FHI-001  example finding             (./src/app.py)
  [MEDIUM  ] FHI-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[target / manifest] --> P[fhirlint<br/>checks + rules]
  P --> OUT[findings (JSON / SARIF)]

Use it from any AI stack

fhirlint is interoperable with every popular way of using AI:

• MCP server — fhirlint mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe fhirlint scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis fhirlint	HL7 FHIR Validator + ESLint
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of HL7 FHIR Validator + ESLint, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (fhirlint mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/fhirlint.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/fhirlint.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/fhirlint.git" # uv
pip install cognis-fhirlint                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/fhirlint:latest --help        # Docker
brew install cognis-digital/tap/fhirlint                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/fhirlint/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/fhirlint	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• phiscrub — Stream-scan logs, CSVs, and free-text notes for PHI (names, MRNs, SSNs, dates, addresses) and redact or tokenize in place.
• dicomsweep — De-identify DICOM imaging studies per the DICOM PS3.15 Annex E profile, scrubbing tags and burned-in pixel text.
• hl7tap — Parse, pretty-print, diff, and replay HL7 v2 messages over MLLP from the terminal.
• consentledger — Maintain a tamper-evident, hash-chained audit log of patient-data access and consent events.
• synthcohort — Generate statistically realistic synthetic patient cohorts (FHIR/CSV) from a schema spec for dev and testing.
• trialwatch — Query, diff, and monitor ClinicalTrials.gov records, alerting on status, enrollment, or result changes.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If fhirlint saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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