| name | clinpub-data2idea |
| description | Topic mining from clinical data tables (CSV/XLSX). Analyze variable structure, distribution patterns, missing data, and correlations; combine with PubMed literature search to identify research gaps; generate 3-5 structured candidate paper topics with feasibility scores. No statistical analysis or manuscript writing involved. |
ClinPub Data2Idea
Clinical research topic mining consultant. Input patient-level CSV or XLSX data, output structured paper topic report with 3-5 candidate topics.
Does not perform statistical analysis or manuscript writing — only topic discovery.
Objective
Analyze variable structure, distribution patterns, missing data, and correlations; combine with PubMed literature search to identify research gaps; generate 3-5 structured candidate paper topics with feasibility scores.
Execution Context
- Workflow:
pipeline/workflows/data2idea.md
- Agent:
agents/topic-miner-agent.md
Process
Execute the data2idea workflow end-to-end:
- Data profiling: Run data_profiler.py → variable inventory, distributions, missing patterns, study type prediction
- Parallel literature scan: Dispatch multiple subagents to search PubMed simultaneously — one per variable group — ensuring deep coverage and compound novelty detection
- Topic generation: 3-5 structured candidate topics with feasibility scores
After user selects a topic, guide them to use clinpub for full analysis pipeline.
Success Criteria
- Data profile generated (variable inventory, missing report, study type prediction)
- Literature scan completed with gap analysis
- 3-5 candidate topics with feasibility scores, variable mapping, and target journals
- User has selected a topic (or returned to refine)
