Parsing Trial Eligibility

What it does

When your agent uses it

• The user wants to turn a ClinicalTrials.gov eligibility block into machine-readable rules
• Screen a synthetic patient for trial fit
• Explain why a patient does
• Does not meet criteria

Example prompts

• “Use the parsing-trial-eligibility skill to parse free-text clinical-trial eligibility criteria into structured inclusion and exclusion logic, then…”
• “/parsing-trial-eligibility”

Requirements

• Python 3

Workflow steps

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

1. 1Apply the typed gates (sex, minimumAge, maximumAge) — cheap, exact.
2. 2Split the free text into inclusion / exclusion bullets.
3. 3Structure each bullet into a Criterion (kind, polarity, target, op,
4. 4Evaluate each criterion against the OpenMed-derived patient profile to
5. 5Report a verdict with a per-criterion rationale and an explicit list of

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 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):

  • clinicaltrials.gov

  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

Parsing Trial Eligibility loads about 2.1k tokens when it runs. Until then it costs about 201 tokens; SKILL.md has 571 words of instructions outside code blocks.

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

~201

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

~2.1k

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). 571 words, ~2,056 tokens.

name

parsing-trial-eligibility

description

Parses free-text clinical-trial eligibility criteria into structured inclusion and exclusion logic, then matches them against patient facts that OpenMed extracted. Use when the user wants to turn a ClinicalTrials.gov eligibility block into machine-readable rules, screen a synthetic patient for trial fit, or explain why a patient does or does not meet criteria. Trigger keywords: eligibility criteria, inclusion, exclusion, trial matching, patient screening, criteria parsing, eligibilityModule, age/sex gates. Pairs after OpenMed and after searching-clinicaltrials: consume the eligibilityModule text from a study, structure it, and match against conditions, medications, labs, and demographics from openmed.analyze_text. Decision-support only — never autonomous enrollment.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

research-genomics

metadata.pairs

after

metadata.version

1.0

# Study from ClinicalTrials.gov v2 (see searching-clinicaltrials)
elig = study["protocolSection"]["eligibilityModule"]

raw = elig["eligibilityCriteria"]            # free text, often markdown bullets
sex = elig.get("sex", "ALL")                 # ALL | FEMALE | MALE
min_age = elig.get("minimumAge")             # e.g. "18 Years"
max_age = elig.get("maximumAge")             # e.g. "75 Years"
healthy_ok = elig.get("healthyVolunteers")   # bool

def split_criteria(text: str) -> dict[str, list[str]]:
    """Split the prose into inclusion / exclusion bullet lists."""
    sections, current = {"inclusion": [], "exclusion": []}, None
    for line in text.splitlines():
        low = line.strip().lower()
        if "inclusion criteria" in low:
            current = "inclusion"; continue
        if "exclusion criteria" in low:
            current = "exclusion"; continue
        bullet = line.strip(" -*•\t")
        if bullet and current:
            sections[current].append(bullet)
    return sections

criteria = split_criteria(raw)

from dataclasses import dataclass

@dataclass
class Criterion:
    kind: str            # "condition" | "lab" | "age" | "sex" | "medication" | "other"
    polarity: str        # "include" | "exclude"
    text: str            # original bullet
    target: str | None   # e.g. "ECOG", "diabetes", "metformin"
    op: str | None = None  # "<=", ">=", "==", "present", "absent"
    value: float | str | None = None

patient = {
    "age": 61, "sex": "FEMALE",
    "conditions": {"type 2 diabetes", "hypertension"},   # OpenMed Disease spans
    "medications": {"metformin", "lisinopril"},          # OpenMed Pharmaceutical
    "labs": {"hba1c": 8.1, "ecog": 1},                   # from a labs extractor
}

def evaluate(c: Criterion, p: dict) -> str:
    if c.kind == "sex" and c.target:
        return "pass" if p["sex"] == c.target or c.target == "ALL" else "fail"
    if c.kind == "condition" and c.target:
        has = c.target.lower() in {x.lower() for x in p["conditions"]}
        ok = has if c.polarity == "include" else not has
        return "pass" if ok else "fail"
    if c.kind == "lab" and c.target and c.target.lower() in p["labs"]:
        v = p["labs"][c.target.lower()]
        cmp = {"<=": v <= c.value, ">=": v >= c.value, "==": v == c.value}
        return "pass" if cmp.get(c.op, False) else "fail"
    return "unknown"   # fact not present → needs human review, never assume pass

© 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/parsing-trial-eligibility of maziyarpanahi/openmed.

Open the folder on GitHubat commit 34d7b8c