{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "maziyarpanahi-openmed--build-a-de-identification-pipeline-with-openmed-agent-skills",
 "repo": "maziyarpanahi/openmed",
 "repo_url": "https://github.com/maziyarpanahi/openmed",
 "repo_slug": "maziyarpanahi-openmed",
 "title": "Have your coding agent build a de-identification pipeline with OpenMed skills",
 "page_title": "How to have your coding agent build a de-identification pipeline with OpenMed skills with Openmed",
 "summary": "Install OpenMed's Agent Skills and ask Claude Code or Codex for an on-device pipeline.",
 "who": "A developer new to clinical NLP who wants a working pipeline without learning every model name.",
 "problem": "Picking models, chaining de-identification and NER, and wiring outputs takes research time.",
 "outcome": "An on-device pipeline that de-identifies a note and then extracts medication entities.",
 "steps": [
  "Run `git clone https://github.com/maziyarpanahi/openmed && cd openmed`",
  "Run `./install-skills.sh` to install the skills for Claude Code, Codex and OpenCode",
  "Ask: Build a local OpenMed pipeline that de-identifies a synthetic discharge note and extracts medication entities",
  "Let the agent produce the Openmed.deidentify then Openmed.analyze_text pipeline",
  "Run it once to download the models, then run it offline"
 ],
 "agent_prompt": "Using the maziyarpanahi/openmed Agent Skills, build a local pipeline that de-identifies <synthetic note> and extracts medication entities, then show the output and the models it used.",
 "agents": [
  "claude-code",
  "codex"
 ],
 "difficulty": "Beginner",
 "time": "20 minutes",
 "basis": "readme",
 "caveat": "This card is written from the maziyarpanahi/openmed README, not from a test run by Deduped. Before you start, make sure you have Python 3.10+ and pip. Check the repo for newer instructions before you rely on these steps.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python 3.10+ and pip",
  "Optional: Apple Silicon for MLX, Android with ONNX Runtime Mobile, or a browser with WebGPU",
  "Model downloads from the Hugging Face Hub, or local model folders for offline use"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/maziyarpanahi-openmed/build-a-de-identification-pipeline-with-openmed-agent-skills/",
 "md": "https://dedupedai.com/use-cases/maziyarpanahi-openmed/build-a-de-identification-pipeline-with-openmed-agent-skills/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/maziyarpanahi-openmed--build-a-de-identification-pipeline-with-openmed-agent-skills.json",
 "api_version": "v1"
}