Registry indexed
Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite rev
Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods.
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Every observational study operationalizes abstract constructs (MASLD, CKD, emphysema, obesity, incidentaloma) into concrete rules against the available data dictionary. When that operationalization is invented ad-hoc from the dictionary alone, reviewers reject on construct validity regardless of downstream statistics.
This skill forces a literature-first pass: each variable is mapped to a canonical guideline/consensus definition, cross-checked against prior operationalizations in comparable cohorts, then mapped to available DB variables. Ad-hoc deviations are flagged explicitly and justified, not hidden.
Use it when:
Call after /design-study, before /write-protocol.
variable_operationalization.md in the project root (or path the user specifies).Missing inputs → ask once, then proceed.
Trigger: project has a project.yaml::db.dictionary_path field pointing to a machine-readable codebook (xlsx/csv/markdown), OR user supplied a dictionary path in inputs. If neither, skip to Tier 1.
For every candidate DB variable — before touching literature — open the dictionary and record, verbatim, the sheet name, row number, and code→meaning mapping. This prevents the single most common observational-study error: assuming a column code (status == 0, grade == 4) means what it intuitively reads like, when the codebook says otherwise.
Concrete procedure per variable:
Dict. sheet & row + Dict. verbatim columns of the operationalization table.Empirical checks (value distributions, cross-tabs with related columns) are useful for sanity testing after the verbatim codebook meaning is recorded — never as a substitute for it.
Project-level binding (recommended): commit a DICTIONARY_FIRST_POLICY.md at the project root (or shared-config path) capturing the canonical dictionary path + escalation contact. Cross-project rule template: ~/.claude/rules/dictionary-first.md.
Exit gate: check_dictionary_citations.py (or equivalent) PASS on the operationalization table before running Tier 1.
Check references/common_definitions.md (shipped with skill) for the variable. Covers high-frequency constructs:
If the variable hits Tier 1, record: guideline, year, canonical cutoff, BibTeX key. Done — no /search-lit call.
/search-lit (focused queries only)For variables NOT in Tier 1, OR when subgroup justification is needed (Asian-specific cutoff, pediatric, young-adult, pregnancy, etc.), call /search-lit with one query per variable — not a general sweep. Query pattern:
"{construct} definition {cohort type} {subgroup qualifier}"
e.g., "obstructive sleep apnea prevalence Korean health screening cohort"
Cap: 5 queries per session. Stop early if first 1-2 papers converge on the same definition.
Before finalizing, run /verify-refs on the accumulated BibTeX to confirm every citation exists in PubMed/CrossRef. Ad-hoc choices (no canonical source found) must be flagged Ad-hoc: yes and justified with 1-2 sentences — never hidden.
Write to {project_root}/variable_operationalization.md using templates/variable_operationalization.md. Required structure:
Header: research question, cohort type, date, author
Operationalization table — one row per variable:
| Variable | Role | Dict. sheet & row | Dict. verbatim | Canonical source | Definition | Cutoff | DB vars | Implementation | Ad-hoc? |
Role: exposure / outcome / covariate / eligibilityDict. sheet & row: e.g. 5-1.복부초음파 r12 — mandatory if a DB dictionary existsDict. verbatim: full code→meaning string copied from the dictionary — mandatory same conditionCanonical source: BibTeX key (e.g., @rinella2023_aasld_masld)Definition: one line, verbatim from guideline where possibleCutoff: numeric + unitsDB vars: exact dictionary column names usedImplementation: SQL/pandas-style pseudocode (e.g., bmi>=25 & (b_tg>=150 | b_hdl<40))Ad-hoc?: yes/no. If yes, justification below tableAd-hoc justifications — for each yes row
Mapping gaps — variables in the protocol with no DB equivalent; list proxy / omit / request decisions
References — BibTeX block
/analyze-stats/write-paper/clean-data/calc-sample-sizeintake-project → design-study → search-lit → define-variables → write-protocol → analyze-stats → write-paper
^^^^^^^^^^^^^^^
/orchestrate should insert this skill between /search-lit and /write-protocol for any observational cohort or registry study.
Every variable definition, cutoff, and era anchor must be grounded in a verified source — a clinical guideline, a peer-reviewed paper with DOI, or an established registry data dictionary. Never invent a phenotype threshold from the model's prior; if the source is unknown, mark the row Ad-hoc: yes and require user confirmation before it propagates into /write-protocol or /analyze-stats. When citing papers to justify a cutoff, verify the citation via /search-lit or /verify-refs — do not carry references from memory alone. The output table must carry explicit source, year, and guideline_version columns so downstream skills can re-verify.
status == 0, grade == 4) by its surface reading without consulting the codebook. Tier 0 exists specifically to prevent this. Distinguish from Failure #1: Tier 0 says "once you've picked the DB column, quote the codebook verbatim before using its values." Failure #1 says "don't pick DB columns before picking definitions from literature." Both rules co-exist.Ad-hoc: yes flag.Role = covariate and Implementation = "IF status == 'never' THEN dose = 0 ELSE measured_value" — and adjust on the categorical status variable, reserving the continuous dose for an exposed-only secondary analysis. /clean-data (categorical-implied-zero flag) and /analyze-stats ("Covariate Pitfalls") enforce this downstream.Some passages in this skill cite a path of the form ~/.claude/rules/<name>.md. Those are the
maintainer's personal global rules, kept outside this repository. They are not shipped with
this skill and will not exist on your machine; they appear only as provenance for where a
convention came from. If one of them looks like it is standing in for an instruction you actually
need, that is a bug — please open an issue, because the instruction belongs here.
name: define-variables description: > Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods. triggers: variable definition, phenotype definition, operationalization, cutoff justification, inclusion criteria, case definition, grouping criteria, literature-grounded definition, canonical definition, 변수 정의, 정의 근거 tools: Read, Write, Edit, Bash, Grep, Glob model: inherit
---
name: define-variables
description: >
Literature-grounded variable operationalization for observational research. Turns a data dictionary +
research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and
DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges
/search-lit output into /write-protocol Methods.
triggers: variable definition, phenotype definition, operationalization, cutoff justification, inclusion criteria, case definition, grouping criteria, literature-grounded definition, canonical definition, 변수 정의, 정의 근거
tools: Read, Write, Edit, Bash, Grep, Glob
model: inherit
---
# Define-Variables Skill
## Purpose
Every observational study operationalizes abstract constructs (MASLD, CKD, emphysema, obesity, incidentaloma) into concrete rules against the available data dictionary. When that operationalization is invented ad-hoc from the dictionary alone, reviewers reject on construct validity regardless of downstream statistics.
This skill forces a **literature-first** pass: each variable is mapped to a canonical guideline/consensus definition, cross-checked against prior operationalizations in comparable cohorts, then mapped to available DB variables. Ad-hoc deviations are flagged explicitly and justified, not hidden.
Use it when:
- a study question is known and variables are being selected
- inclusion/exclusion criteria or phenotype definitions need citation backing
- a data dictionary has ambiguous or derived variables (eGFR formula, BMI class, liver steatosis criteria, etc.)
- a reviewer asked "why this cutoff?"
- a retrospective audit reveals drifted definitions across projects in the same cohort
Call after `/design-study`, before `/write-protocol`.
## Communication Rules
- Communicate in the user's preferred language.
- All variable names, guideline names, cutoffs in English.
- Produce one artifact: `variable_operationalization.md` in the project root (or path the user specifies).
## Inputs
1. **Research question** (one sentence)
2. **Candidate variables** — exposure, outcome, key covariates, eligibility filters
3. **Data dictionary path** (xlsx / csv / markdown) OR explicit list of available DB columns
4. **Cohort type** (e.g., health-screening, NHANES-like, claims, registry) — informs which prior-art cohort to compare against
Missing inputs → ask once, then proceed.
## 4-Tier Pipeline (DB codebook + token-efficient literature)
### Tier 0 — DB codebook lookup (mandatory for DB-backed observational studies)
**Trigger**: project has a `project.yaml::db.dictionary_path` field pointing to a machine-readable codebook (xlsx/csv/markdown), OR user supplied a dictionary path in inputs. If neither, skip to Tier 1.
For every candidate DB variable — **before** touching literature — open the dictionary and record, verbatim, the sheet name, row number, and code→meaning mapping. This prevents the single most common observational-study error: assuming a column code (`status == 0`, `grade == 4`) means what it intuitively reads like, when the codebook says otherwise.
Concrete procedure per variable:
1. Locate the variable in the dictionary by exact column name.
2. Copy verbatim: the sheet title, row number, and full code→meaning mapping (or unit/range statement for continuous vars).
3. Paste into the `Dict. sheet & row` + `Dict. verbatim` columns of the operationalization table.
4. If the variable is not found, OR the codebook is silent on a specific code value, file a question to the DB owner / data steward. Do NOT infer from cross-tabs, do NOT guess, do NOT proceed with that variable until a verbatim answer exists.
Empirical checks (value distributions, cross-tabs with related columns) are useful for sanity testing **after** the verbatim codebook meaning is recorded — never as a substitute for it.
Project-level binding (recommended): commit a `DICTIONARY_FIRST_POLICY.md` at the project root (or shared-config path) capturing the canonical dictionary path + escalation contact. Cross-project rule template: `~/.claude/rules/dictionary-first.md`.
**Exit gate**: `check_dictionary_citations.py` (or equivalent) PASS on the operationalization table before running Tier 1.
### Tier 1 — Canonical index lookup (no API calls)
Check `references/common_definitions.md` (shipped with skill) for the variable. Covers high-frequency constructs:
- Liver: MASLD (AASLD 2023), MetALD (AASLD 2023), MAFLD (2020), NAFLD (legacy), ALD, viral hepatitis (AASLD 2022/2024 HBV, AASLD-IDSA HCV)
- Metabolic: T2DM (ADA 2024), prediabetes (ADA 2024), metabolic syndrome (IDF 2009 / NCEP ATP III / K-NCEP), obesity/BMI (WHO Asian 2004 + WHO global), HTN (ACC/AHA 2017 + JNC-8), dyslipidemia (NCEP ATP III, 2023 AHA/ACC)
- Renal: CKD (KDIGO 2024), eGFR formulas (CKD-EPI 2021 race-free, MDRD legacy), incidental renal mass (ACR 2018 white paper, Bosniak 2019)
- Pulmonary: COPD (GOLD 2024), emphysema imaging (Fleischner 2015)
- CV: CAC scoring (Agatston 1990, MESA percentiles), CAD risk (2018 ACC/AHA cholesterol, PREVENT 2023)
- Cancer: gastric cancer H. pylori (Maastricht VI 2022), thyroid nodule (ACR TI-RADS 2017), gallbladder polyp (European 2022 joint guideline)
- Imaging incidentalomas: adrenal (ACR 2023), pancreas (ACR 2017), renal (ACR 2018), thyroid (ACR 2017)
If the variable hits Tier 1, record: guideline, year, canonical cutoff, BibTeX key. Done — no `/search-lit` call.
### Tier 2 — Targeted `/search-lit` (focused queries only)
For variables NOT in Tier 1, OR when subgroup justification is needed (Asian-specific cutoff, pediatric, young-adult, pregnancy, etc.), call `/search-lit` with **one query per variable** — not a general sweep. Query pattern:
```
"{construct} definition {cohort type} {subgroup qualifier}"
e.g., "obstructive sleep apnea prevalence Korean health screening cohort"
```
Cap: 5 queries per session. Stop early if first 1-2 papers converge on the same definition.
### Tier 3 — Verification
Before finalizing, run `/verify-refs` on the accumulated BibTeX to confirm every citation exists in PubMed/CrossRef. Ad-hoc choices (no canonical source found) must be flagged `Ad-hoc: yes` and justified with 1-2 sentences — never hidden.
## Output Template
Write to `{project_root}/variable_operationalization.md` using `templates/variable_operationalization.md`. Required structure:
1. **Header**: research question, cohort type, date, author
2. **Operationalization table** — one row per variable:
| Variable | Role | Dict. sheet & row | Dict. verbatim | Canonical source | Definition | Cutoff | DB vars | Implementation | Ad-hoc? |
- `Role`: exposure / outcome / covariate / eligibility
- `Dict. sheet & row`: e.g. `5-1.복부초음파 r12` — mandatory if a DB dictionary exists
- `Dict. verbatim`: full code→meaning string copied from the dictionary — mandatory same condition
- `Canonical source`: BibTeX key (e.g., `@rinella2023_aasld_masld`)
- `Definition`: one line, verbatim from guideline where possible
- `Cutoff`: numeric + units
- `DB vars`: exact dictionary column names used
- `Implementation`: SQL/pandas-style pseudocode (e.g., `bmi>=25 & (b_tg>=150 | b_hdl<40)`)
- `Ad-hoc?`: yes/no. If yes, justification below table
3. **Ad-hoc justifications** — for each yes row
4. **Mapping gaps** — variables in the protocol with no DB equivalent; list proxy / omit / request decisions
5. **References** — BibTeX block
## Non-Goals
- Statistical analysis → `/analyze-stats`
- Manuscript drafting → `/write-paper`
- Data cleaning / missingness → `/clean-data`
- Sample size → `/calc-sample-size`
## Pipeline Position
```
intake-project → design-study → search-lit → define-variables → write-protocol → analyze-stats → write-paper
^^^^^^^^^^^^^^^
```
`/orchestrate` should insert this skill between `/search-lit` and `/write-protocol` for any observational cohort or registry study.
## Anti-Hallucination
Every variable definition, cutoff, and era anchor must be grounded in a verified source — a clinical guideline, a peer-reviewed paper with DOI, or an established registry data dictionary. Never invent a phenotype threshold from the model's prior; if the source is unknown, mark the row `Ad-hoc: yes` and require user confirmation before it propagates into `/write-protocol` or `/analyze-stats`. When citing papers to justify a cutoff, verify the citation via `/search-lit` or `/verify-refs` — do not carry references from memory alone. The output table must carry explicit `source`, `year`, and `guideline_version` columns so downstream skills can re-verify.
## Failure Modes to Avoid
0. **Ad-hoc DB code interpretation** (the single most costly observational-study error). Interpreting a column value (`status == 0`, `grade == 4`) by its surface reading without consulting the codebook. Tier 0 exists specifically to prevent this. Distinguish from Failure #1: Tier 0 says "once you've picked the DB column, quote the codebook verbatim before using its values." Failure #1 says "don't pick DB columns before picking definitions from literature." Both rules co-exist.
1. **Dictionary-first framing** — starting from what columns exist, then picking a definition that matches. Always flip: definition first, then map.
2. **Cutoff drift** — using a different cutoff than the cited guideline without justification (e.g., BMI≥23 cited as WHO Asian while text says ≥25).
3. **Mixing eras** — 2020 MAFLD criteria with 2023 MASLD criteria in the same analysis. Pick one and note why.
4. **Silent ad-hoc** — introducing a novel cutoff without the `Ad-hoc: yes` flag.
5. **Sweep-style /search-lit** — running a generic lit search instead of one focused query per gap variable. Wastes tokens and buries the signal.
6. **Dose/duration structural-missingness** — operationalizing a dose/duration covariate (pack-years, cessation-years, alcohol grams/week) anchored to a categorical exposure (smoking status, alcohol use) without specifying what the *reference level* (never-smoker, never-drinker) does to the dose. A never-smoker's pack-years is a structural zero, not a missing value; conflating the two collapses the analytic sample under complete-case modeling and lets MICE fabricate a non-zero dose for the unexposed. Operationalize it explicitly — add a row with `Role = covariate` and `Implementation = "IF status == 'never' THEN dose = 0 ELSE measured_value"` — and adjust on the categorical **status** variable, reserving the continuous **dose** for an exposed-only secondary analysis. `/clean-data` (categorical-implied-zero flag) and `/analyze-stats` ("Covariate Pitfalls") enforce this downstream.
## Global-rule references
Some passages in this skill cite a path of the form `~/.claude/rules/<name>.md`. Those are the
maintainer's personal global rules, kept outside this repository. They are **not shipped with
this skill** and will not exist on your machine; they appear only as provenance for where a
convention came from. If one of them looks like it is standing in for an instruction you actually
need, that is a bug — please open an issue, because the instruction belongs here.
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
71/100
Strong
Trust
63/100
Sandbox only
Audit
78/100
Needs review
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"description": "Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods.",
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"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No major risk signals from current metadata",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
],
"agent_contract": {
"task_input": "Use define-variables in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 71/100 Manual review",
"Audit: 78/100 Needs review",
"Safety: 30/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "aperivue-define-variables (define-variables)",
"install_command": "npx skills add Aperivue/medsci-skills --skill define-variables",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "aperivue-define-variables",
"task": "Use define-variables in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/aperivue-define-variables",
"api": "https://www.openagentskill.com/api/agent/skills/aperivue-define-variables",
"audit": "https://www.openagentskill.com/skills/aperivue-define-variables/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=aperivue-define-variables&task=Use%20define-variables%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20define-variables%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20define-variables%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/aperivue-define-variables/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/aperivue-define-variables"
}
}Listing source
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