Mapping Loinc

What it does

When your agent uses it

• The user wants to code lab tests
• Observations to LOINC
• Resolve a test name plus specimen and method to the correct LOINC part-model code
• Attach UCUM units

Example prompts

• “/mapping-loinc”

Requirements

• Python 3

Workflow steps

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

1. 1Extract observation/analyte mentions with OpenMed.
2. 2Assemble the axes you have from surrounding text: component (what),
3. 3Search candidates via $expand?filter= (or Regenstrief search).
4. 4Disambiguate by matching specimen and property. "Glucose" alone is
5. 5Validate the chosen code with $validate-code, then $lookup to pull the
6. 6Emit {system: "http://loinc.org", code, display} plus the UCUM unit for

What it can do on your machine

Read from SKILL.md and the folder at commit 9dca507. 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:

  • loinc.org
  • fhir.loinc.org

  Also links to:

  • hl7.org
  • ucum.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

Mapping Loinc loads about 2k tokens when it runs. Until then it costs about 205 tokens; SKILL.md has 640 words of instructions outside code blocks.

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

~205

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 9dca507, republished under its Apache-2.0 licence (© maziyarpanahi). 640 words, ~1,976 tokens.

name

mapping-loinc

description

Maps laboratory and clinical observation names extracted by OpenMed to LOINC codes using the public Regenstrief LOINC and FHIR terminology APIs. Use when the user wants to code lab tests, vital signs, or observations to LOINC, resolve a test name plus specimen and method to the correct LOINC part-model code, attach UCUM units, or build a US Core Laboratory Result Observation. Trigger keywords: LOINC, lab coding, observation code, UCUM units, specimen, method, US Core lab, FHIR Observation, lab result mapping, panel vs analyte. Pairs after OpenMed NER: consume Disease/Chemical/lab-name entities from openmed.analyze_text and map each measurement to a LOINC code. LOINC is free to use under the Regenstrief license (registration/terms-of-use, no fee); UMLS/SNOMED stay user-supplied and out-of-process.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

terminology-coding

metadata.pairs

after

metadata.version

1.0

import requests
from requests.auth import HTTPBasicAuth

FHIR = "https://fhir.loinc.org"
AUTH = HTTPBasicAuth("YOUR_LOINC_USER", "YOUR_LOINC_PASSWORD")  # free account
LOINC_SYSTEM = "http://loinc.org"

def lookup(code: str) -> dict:
    """$lookup: return the fully specified name + axes for a LOINC code."""
    r = requests.get(
        f"{FHIR}/CodeSystem/$lookup",
        params={"system": LOINC_SYSTEM, "code": code},
        auth=AUTH, headers={"Accept": "application/fhir+json"}, timeout=15,
    )
    r.raise_for_status()
    return r.json()

def validate(code: str, display: str) -> bool:
    r = requests.get(
        f"{FHIR}/CodeSystem/$validate-code",
        params={"url": LOINC_SYSTEM, "code": code, "display": display},
        auth=AUTH, headers={"Accept": "application/fhir+json"}, timeout=15,
    )
    r.raise_for_status()
    params = {p["name"]: p.get("valueBoolean") for p in r.json().get("parameter", [])}
    return bool(params.get("result"))

print(lookup("2823-3"))     # Potassium [Moles/volume] in Serum or Plasma

def expand_filter(text: str, count: int = 10) -> list[dict]:
    """Text-filter the LOINC code system to candidate concepts."""
    r = requests.get(
        f"{FHIR}/ValueSet/$expand",
        params={"url": "http://loinc.org/vs", "filter": text, "count": count},
        auth=AUTH, headers={"Accept": "application/fhir+json"}, timeout=20,
    )
    r.raise_for_status()
    return r.json().get("expansion", {}).get("contains", [])

import openmed

note = "Labs: serum potassium 5.1 mmol/L, hemoglobin A1c 7.8 %."
result = openmed.analyze_text(
    note,
    model_name="chemical_detection_pubmed",   # Chemical category (analytes)
    output_format="dict",
)

for ent in result["entities"]:
    name = ent["text"]                         # e.g. "potassium"
    candidates = expand_filter(name, count=5)  # LOINC candidates
    # carry OpenMed offsets so the code is traceable to the source span
    print(name, ent["start"], ent["end"], "->",
          [(c["code"], c["display"]) for c in candidates[:3]])

© 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/mapping-loinc of maziyarpanahi/openmed.

Open the folder on GitHubat commit 9dca507