Find-Cohort-Gap Skill

Output directory: user-specified (default: current working directory).

Phase 0: Cohort Intake

The cohort does not have to be one this skill has heard of. Route on what the user actually has.

Input adapter (local codebook / documents)

python3 "${CLAUDE_SKILL_DIR}/scripts/build_cohort_profile.py" \
  --codebook data_dictionary.csv \
  --context narrative_review.pdf --context https://example.org/guideline \
  --cohort-name "Institutional CT registry" --out-dir .

Formats: .csv / .tsv / .json / .md / .txt (stdlib), .xlsx (needs openpyxl), .pdf (needs pdftotext). A .csv is auto-detected as a codebook (rows are variables) or a data export (the header row is the variable list). Writes cohort_profile.md + cohort_profile.json (+ context_extract.md).

Do not read the codebook yourself and summarise it. A paraphrased, merged, or invented variable poisons every downstream claim — the intersection matrix, the feasibility gate, and eventually the manuscript's Methods. The adapter enumerates variables verbatim with provenance (file:row). Read cohort_profile.md; do not re-derive it.

The adapter infers, and shows its work for, the variable cluster map, serial / repeated-measure groups (evidence for P1 Longitudinal Advantage), and endpoint candidates (evidence for P2 Endpoint Upgrade). A variable matching no cluster keyword is left unclassified — review those, since the lexicon is not exhaustive.

What the adapter cannot know — ASK, never guess

A codebook does not state any of the following; each is emitted as [UNKNOWN - ask the user]:

1. Sample size (N at baseline, N with follow-up)
2. Time span (enrollment period, follow-up duration, measurement intervals)
3. Known limitations (healthy volunteer bias, attrition, missing-data patterns)
4. Existing publications from this cohort (to avoid duplicating them)
5. IRB status and data-access route

Collect these from the user before Phase 2, because a guessed N flows into the Phase 5 feasibility gate and makes it pass or fail for a reason unrelated to the cohort. Also confirm the setting (institution type, country, population type) and any special strengths the variable names cannot reveal — registry linkage, biobank availability, a distinctive population.

Gate: Present the cohort profile summary, including the [UNKNOWN] list and the unclassified variables. Confirm before proceeding.

Phase 1: PI/CA Profiling

Profile the intended PI or corresponding author to find topic-expertise alignment. If no PI is specified, skip this phase and use variable clusters alone in Phase 2.

1. Search PubMed for the PI's recent publications (last 5 years) with /search-lit's

E-utilities script: bash "${CLAUDE_SKILL_DIR}/../search-lit/references/pubmed_eutils.sh" search "AuthorLastName AuthorFirstInitial[Author]" 30 Extract top keyword clusters from titles/abstracts.

1. Identify specialty signals: academic society positions (president, board member,

editor), subspecialty focus areas, preferred journal tiers.

1. Build a PI keyword map: 5-10 keyword clusters ranked by publication frequency.

Output: PI profile card (name, affiliation, top keywords, society roles, preferred journals).

Phase 2: Intersection Matrix

Cross cohort variable clusters with PI expertise to generate candidate topics.

Method

Create a matrix: rows = DB variable clusters, columns = PI keyword clusters. Score each cell 0-3:

• 3: PI has published in this exact intersection (direct match)
• 2: PI's subspecialty covers this area (strong relevance)
• 1: Tangential connection (possible but needs framing)
• 0: No connection

Candidate Generation

1. Extract all cells scoring 2-3 as primary candidates.
2. For cells scoring 1, apply the A-B substitution test: "Has someone published

[this analysis] with [a different exposure/outcome] in a similar cohort?" If yes, substituting the PI's specialty variable creates a viable candidate.

1. Generate 20-40 candidate topic statements in PICO format — P population from the

cohort, E exposure/predictor variable(s), C comparison group, O outcome (preferably hard endpoint).

Discipline Alignment Filter

Before saturation scanning, identify the intended first author's department/specialty. The primary exposure variable must belong to that discipline (e.g., radiology first author → imaging variable as the primary exposure). Kill candidates where the primary exposure is outside the first author's discipline — a strong PI match alone is insufficient if the first author cannot claim ownership of the core variable.

Gate: Present the intersection matrix and top 20 candidates (post-discipline filter). User selects 8-12 for saturation scanning.

Phase 3: Literature Saturation Scan

For each selected candidate, determine how saturated the literature is.

Search Strategy

For each candidate:

1. Build a PubMed query: (exposure terms) AND (outcome terms) AND (cohort OR longitudinal OR prospective)
2. Execute search via /search-lit E-utilities.
3. Count total results, then ask the Critical Filter question — **"Has anyone published

this with serial/repeated measurements?"** — and check whether a meta-analysis exists. Grade:

"So What" Test

For each candidate, articulate 2-3 potential clinical implications of the findings. If you cannot state why a clinician or policymaker would care about the result, the topic fails regardless of gap score.

Output: Saturation table with grade, paper count, longitudinal gap status, and "So What" statement for each candidate.

Gate: Present saturation results. User selects 3-5 finalists for deep scoring.

Phase 4: 6-Pattern Scoring + Comparison Table

Apply the 6-Pattern framework to each finalist. Score each pattern 0 or 1.

6 Patterns (Universal)

Read the detailed rubric at ${CLAUDE_SKILL_DIR}/references/pattern_scoring_rubric.md and score each finalist on P1 Longitudinal Advantage, P2 Endpoint Upgrade, P3 Cohort Uniqueness, P4 PI-Topic Alignment (skip if no PI specified), P5 Comparison Table Gaps (3+ features unique to THIS STUDY in the table below), and P6 Complementary Design.

Comparison Table Construction

For each finalist, build a table comparing the top 3-5 existing papers against THIS STUDY, one row per feature (design, N, serial data, hard endpoint, population, ethnicity, subgroup analysis, …) — the rubric's P5 lists the construction steps and differentiator categories. Cite existing papers only with a /search-lit-confirmed DOI or PMID; otherwise mark the reference [UNVERIFIED - NEEDS MANUAL CHECK].

Score Interpretation

Gate: Present scoring results and comparison tables. User approves final ranking.

Phase 5: Feasibility Gate

For each scored finalist, verify practical feasibility.

Checks

1. Sample size adequacy:

• Cox and logistic regression: minimum 10 events per predictor variable (EPV rule)
• For large cohorts (N>100K) with many outcome events: warn that small effects become

statistically significant (power depends on the number of events, not N — check the event count first), so focus on effect size thresholds (e.g., HR >1.2 or <0.8 for clinical relevance)

• Consider negative control strategy (EPCV) for very large samples

1. Missing data: key exposure variable <20% missing acceptable; key outcome <5% missing;

if serial data, assess the attrition pattern (MCAR/MAR/MNAR)

1. Follow-up adequacy: the outcome must have plausible latency within available

follow-up — cancer outcomes minimum 5 years, CVD events minimum 3 years, mortality minimum 5 years

1. Operational definition:

• Can the exposure be defined from available variables?
• For claims data: ICD codes alone = 40-60% accuracy. Require combination

strategy (diagnosis + prescription + visit frequency + special codes)

• Cross-check expected prevalence against known epidemiological data
• Flag any clinical definition, diagnostic criterion, or guideline claim you cannot

source with [VERIFY] and ask the user.

1. IRB/ethics: is the data already IRB-approved for this type of analysis? Any

additional approvals needed for data linkage?

1. Disease Novelty Bonus (informational, not Go/No-Go): idiopathic etiology or debated

mechanism → higher journal interest; established mechanism → needs stronger methodological novelty

Decision

• Go: All checks pass.
• Conditional Go: Minor issues solvable (e.g., missing data manageable with imputation).
• No-Go: Fatal flaw (insufficient events, no valid endpoint, key variable unavailable).

Output: Feasibility report for each finalist with Go/Conditional/No-Go status.

Phase 6: Output — Ranked Proposals + One-Pagers

Ranked Summary Table

| Rank | Topic (PICO) | Saturation | 6-Pattern Score | Feasibility | Target Journal | Timeline |
|------|--------------|------------|-----------------|-------------|----------------|----------|
| 1 | ... | Green (0 longitudinal) | 5/6 | Go | JACC | 6 months |
| 2 | ... | Blue (0 papers) | 3/6 | Conditional | Radiology | 8 months |

One-Pager for Each Finalist

Fill every section of the template at ${CLAUDE_SKILL_DIR}/references/onepager_template.md; give the target journal with its rationale (PI alignment, scope match, gap fit). Save one-pagers as markdown files: {output_dir}/gap_proposal_{rank}_{short_topic}.md

Downstream: output feeds into /design-study → /write-paper.