Defining Cohort Phenotypes

What it does

When your agent uses it

• The user wants to define a patient cohort
• Write a computable phenotype
• OHDSI Phenotype Library logic
• Build concept sets

Example prompts

• “Use the defining-cohort-phenotypes skill to author computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM…”
• “/defining-cohort-phenotypes”

Requirements

• Python 3

Workflow steps

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

1. 1Start from a library definition if one exists (OHDSI Phenotype Library /
2. 2Build concept sets from standard OMOP concepts; set includeDescendants
3. 3Identify text-only criteria the codes miss; extract them with
4. 4Assemble the CIRCE JSON (concept sets + expression) — in ATLAS or directly.
5. 5Validate against OMOP CDM: generate SQL, run on a (synthetic/de-identified)
6. 6Document human-readable logic alongside the JSON for portability.

What it can do on your machine

Read from SKILL.md and the folder at commit 34d7b8c. 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 jsonc and python).

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

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • ohdsi.github.io
  • phekb.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

Defining Cohort Phenotypes loads about 1.9k tokens when it runs. Until then it costs about 203 tokens; SKILL.md has 676 words of instructions outside code blocks.

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

~203

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

~1.9k

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 34d7b8c, republished under its Apache-2.0 licence (© maziyarpanahi). 676 words, ~1,928 tokens.

name

defining-cohort-phenotypes

description

Authors computable phenotype and cohort definitions in the OHDSI ATLAS / CIRCE style over the OMOP CDM, combining standard concept sets with NLP-derived features that OpenMed extracts. Use when the user wants to define a patient cohort, write a computable phenotype, reuse PheKB or OHDSI Phenotype Library logic, build concept sets, or augment code-based criteria with text features. Trigger keywords: phenotype, cohort definition, OHDSI, ATLAS, CIRCE, OMOP CDM, concept set, PheKB, Phenotype Library, eMERGE, computable phenotype. Pairs adjacent to OpenMed: NLP features from openmed.analyze_text augment code-based phenotypes for entities that are poorly captured by structured codes. OMOP CDM and OHDSI tools are open source; restricted vocabularies (SNOMED, CPT) are user-supplied.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

research-genomics

metadata.pairs

adjacent

metadata.version

1.0

{
  "ConceptSets": [{
    "id": 0, "name": "Type 2 diabetes",
    "expression": { "items": [{
      "concept": { "CONCEPT_ID": 201826,           // OMOP standard concept
                   "CONCEPT_CODE": "44054006",      // SNOMED (user vocab)
                   "VOCABULARY_ID": "SNOMED" },
      "includeDescendants": true                    // pull the hierarchy
    }] }
  }],
  "PrimaryCriteria": {                              // entry event
    "CriteriaList": [{ "ConditionOccurrence": { "CodesetId": 0 } }],
    "ObservationWindow": { "PriorDays": 0, "PostDays": 0 },
    "PrimaryCriteriaLimit": { "Type": "First" }
  },
  "InclusionRules": [{
    "name": "Adult at index",
    "expression": { "Type": "ALL", "CriteriaList": [{
      "Criteria": { "ConditionEra": { "AgeAtStart": { "Value": 18, "Op": "gte" } } }
    }] }
  }]
}

import openmed

# 1) Extract the text feature OpenMed is good at (e.g. tobacco use, symptom)
note = "Patient is a current smoker, ~1 pack/day, with worsening dyspnea."
res = openmed.analyze_text(note, model_name="disease_detection_superclinical",
                           output_format="dict")

# 2) Write a derived OBSERVATION (or a custom cohort attribute) per patient,
#    mapping each extracted entity to a standard concept (grounded out-of-process).
#    e.g. Observation: "Current smoker" -> a SNOMED concept in your vocab.

# 3) Reference that concept in a CIRCE ConceptSet, so the phenotype combines
#    structured codes AND the NLP-derived flag in one inclusion rule.

© 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/defining-cohort-phenotypes of maziyarpanahi/openmed.

Open the folder on GitHubat commit 34d7b8c