Normalizing Rxnorm

What it does

When your agent uses it

• The user wants to code
• De-duplicate medication names
• Resolve a brand/generic/ingredient to a stable RxCUI
• Link strength+dose-form to an SCD/SBD

Example prompts

• “Use the normalizing-rxnorm skill to normaliz drug mentions extracted by OpenMed to RxNorm RxCUIs using the free public RxNav/RxNorm REST API”
• “/normalizing-rxnorm”

Requirements

• Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Extract drug spans with OpenMed (pharma_detection_superclinical).
2. 2Parse each span into name + strength + dose form when present
3. 3Exact match the cleaned name with /rxcui.json?name=. If empty, fall back
4. 4Pick the right term type (TTY) for your use case
5. 5Validate by reading /rxcui/{rxcui}/properties.json and confirming the
6. 6Emit {system: "http://www.nlm.nih.gov/research/umls/rxnorm", code, display}.

What it can do on your machine

Read from SKILL.md and the folder at commit ea920f3. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • rxnav.nlm.nih.gov
  • nlm.nih.gov

  Also links to:

  • hl7.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Normalizing Rxnorm loads about 2k tokens when it runs. Until then it costs about 187 tokens; SKILL.md has 620 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~187

When it runs · the whole SKILL.md, loaded when a task matches

~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit ea920f3, republished under its Apache-2.0 licence (© maziyarpanahi). 620 words, ~2,022 tokens.

name

normalizing-rxnorm

description

Normalizes drug mentions extracted by OpenMed to RxNorm RxCUIs using the free public RxNav/RxNorm REST API. Use when the user wants to code, standardize, or de-duplicate medication names, resolve a brand/generic/ingredient to a stable RxCUI, link strength+dose-form to an SCD/SBD, attach NDCs, or build a US Core Medication resource. Trigger keywords: RxNorm, RxCUI, RxNav, drug normalization, medication coding, NDC, ingredient, SCD, SBD, brand vs generic, getApproximateMatch. Pairs after OpenMed NER: consume Pharmaceutical/Chemical entities from openmed.analyze_text and map each drug span to an RxCUI. RxNorm and RxNav are fully public and free — no API key, no license barrier, the lowest-friction terminology in this set.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

terminology-coding

metadata.pairs

after

metadata.version

1.0

import requests

BASE = "https://rxnav.nlm.nih.gov/REST"

def rxcui_for(name: str) -> str | None:
    """Exact-match RxCUI lookup for a normalized drug name."""
    r = requests.get(f"{BASE}/rxcui.json", params={"name": name}, timeout=10)
    r.raise_for_status()
    ids = r.json().get("idGroup", {}).get("rxnormId", [])
    return ids[0] if ids else None

def approximate(name: str, max_entries: int = 3) -> list[dict]:
    """Fuzzy match for misspelled or abbreviated drug text."""
    r = requests.get(
        f"{BASE}/approximateTerm.json",
        params={"term": name, "maxEntries": max_entries},
        timeout=10,
    )
    r.raise_for_status()
    return r.json().get("approximateGroup", {}).get("candidate", [])

print(rxcui_for("metformin"))                       # -> '6809' (ingredient)
print(approximate("metformin 500"))                 # fuzzy -> candidate RxCUIs

# getApproximateMatch / getRxConceptProperties give term type (TTY)
def properties(rxcui: str) -> dict:
    r = requests.get(f"{BASE}/rxcui/{rxcui}/properties.json", timeout=10)
    r.raise_for_status()
    return r.json().get("properties", {})

# Find the SCD ("metformin 500 MG Oral Tablet") from the ingredient:
def related_by_tty(rxcui: str, tty: str) -> list[dict]:
    r = requests.get(
        f"{BASE}/rxcui/{rxcui}/related.json", params={"tty": tty}, timeout=10
    )
    r.raise_for_status()
    groups = r.json().get("relatedGroup", {}).get("conceptGroup", [])
    out = []
    for g in groups:
        out.extend(g.get("conceptProperties", []) or [])
    return out

ndcs = requests.get(f"{BASE}/rxcui/{rxcui}/ndcs.json").json()   # package codes

import openmed, requests

note = "Patient on metformin 500 mg BID and atorvastatin 20 mg nightly."
result = openmed.analyze_text(
    note,
    model_name="pharma_detection_superclinical",   # Pharmaceutical category
    output_format="dict",
)

DRUG_LABELS = {"DRUG", "MEDICATION", "CHEM"}        # OpenMed Pharmaceutical labels
for ent in result["entities"]:
    if ent["label"] in DRUG_LABELS:
        span = ent["text"]                          # e.g. "metformin"
        rxcui = rxcui_for(span) or (
            (approximate(span) or [{}])[0].get("rxcui")
        )
        print(span, "->", rxcui, f"(conf {ent['confidence']:.2f})")

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/normalizing-rxnorm of maziyarpanahi/openmed.

Open the folder on GitHubat commit ea920f3