pip install cognis-codemap
codemap scan . # → prioritized findings in seconds-
Install:
pip install -e . -
Validate a code (the coding system is auto-detected) with the
validatesubcommand:codemap validate E11.9
Validate a batch from a file (one code per line) for CI-friendly output:
codemap validate --input codes.txt --format json
-
Crosswalk a code to equivalent concepts in other terminologies (ICD-10 / LOINC / RxNorm / CPT). Use
--toto limit the target system and--tableto supply your own terminology CSV:codemap crosswalk E11.9 --to RXNORM --format json
-
Read the result.
validatereports per-codeformat(valid/INVALID) and whether it isknown, exiting 1 if any code is invalid/unknown.crosswalklists mapped concepts and exits 1 when there are zero matches.detectidentifies the coding system of raw codes:codemap detect 4548-4
-
Use it in CI — fail when a code set contains anything invalid:
codemap validate --input codes.txt --format json || { echo "Invalid/unknown medical codes present"; exit 1; }
- Why codemap? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing
Offline, scriptable terminology crosswalk — turns a painful UMLS-portal lookup into a piped one-liner every clinical data engineer will star.
codemap 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.
- ✅ Normalize Code
- ✅ Detect System
- ✅ Load Table
- ✅ Validate Code
- ✅ Lookup
- ✅ Crosswalk
- ✅ Load Default
- ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
- ✅ Ports in Python, JavaScript, Go, and Rust (
ports/)
pip install cognis-codemap
codemap --version
codemap scan . # scan current project
codemap scan . --format json # machine-readable
codemap scan . --fail-on high # CI gate (non-zero exit)$ codemap scan .
[HIGH ] COD-001 example finding (./src/app.py)
[MEDIUM ] COD-002 another signal (./config.yaml)
2 findings · risk score 5 · 38ms
flowchart LR
IN[input] --> P[codemap<br/>analyze + score]
P --> OUT[report]
codemap is interoperable with every popular way of using AI:
- MCP server —
codemap mcp(Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet) - OpenAI-compatible / JSON — pipe
codemap scan . --format jsoninto 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
| Cognis codemap | OHDSI Athena + UMLS | |
|---|---|---|
| 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 OHDSI Athena + UMLS, re-framed the Cognis way. Missing a credit? Open a PR.
Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (codemap mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.
pip install "git+https://github.com/cognis-digital/codemap.git" # pip (works today)
pipx install "git+https://github.com/cognis-digital/codemap.git" # isolated CLI
uv tool install "git+https://github.com/cognis-digital/codemap.git" # uv
pip install cognis-codemap # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/codemap:latest --help # Docker
brew install cognis-digital/tap/codemap # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/codemap/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/codemap |
DEPLOY.md (AWS/Azure/GCP/k8s) |
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.fhirlint— Validate FHIR R4/R5 resources and bundles against profiles (US Core, etc.) with precise, line-level error reporting.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.
Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram
PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.
{} 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.
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 (licensing@cognis.digital). See LICENSE.