// Structured data extraction from deep-read papers — produces comparison tables (method, dataset, metrics, results, limitations). Used by systematic-survey and deep-survey.
Identify what the literature has NOT addressed — missing methods, untested combinations, unexplored applications, contradictions without resolution. Used by all strategies.
Identify recurring themes across papers using qualitative coding methodology. Produces a codebook with theme definitions, supporting evidence, and frequency counts. Used by narrative-review.
Generate PRISMA-compliant flow data documenting the screening funnel — counts at each stage (identification, screening, eligibility, inclusion) with exclusion reasons. Used by systematic-survey via prisma-screening tactic.
Construct a hierarchical field map from paper collection — multi-level taxonomy with parent/child relationships, paper counts per node, and maturity indicators. Used by scoping-survey.
Determine when additional searching yields diminishing returns. Analyzes the latest expansion batch against existing corpus to judge continue/near-saturation/saturated. Used by snowball and systematic-survey.
Validate and prioritize starting papers for snowball surveys. Evaluates which seeds will yield the richest citation traces based on citation count, recency, and network position.
| name | extract-data |
| description | Structured data extraction from deep-read papers — produces comparison tables (method, dataset, metrics, results, limitations). Used by systematic-survey and deep-survey. |
| execution | subagent |
| prompt | ./prompt.md |
| input | paper_contents (string) |
Pull structured facts from paper full text into comparison tables.
Subagent — spawned via subagent-spawning/spawn-agent skill.
Data extraction requires careful, systematic reading of multiple full papers simultaneously to ensure consistent extraction across all entries. Dedicated context prevents extraction drift.