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thematic-coding

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Aktualisiert16. Mai 2026 um 08:23

Identify recurring themes across papers using qualitative coding methodology. Produces a codebook with theme definitions, supporting evidence, and frequency counts. Used by narrative-review.

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Quelle

yogsoth-ai

yogsoth-ai/literature-survey

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

DatenwissenschaftlerInformatik- und Mathematikberufe·SOC 15-2051

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2 Dateien

SKILL.md

readonly

name	thematic-coding
description	Identify recurring themes across papers using qualitative coding methodology. Produces a codebook with theme definitions, supporting evidence, and frequency counts. Used by narrative-review.
execution	subagent
prompt	./prompt.md
input	paper_notes (string)

Thematic Coding

Identify recurring themes across papers using qualitative coding methodology.

Execution

Subagent — spawned via subagent-spawning/spawn-agent skill.

Why Subagent

Thematic coding requires iterative comparison across all papers simultaneously — reading, coding, comparing, refining codes. Dedicated context for this qualitative analysis.

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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.

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saturation-detection

yogsoth-ai/literature-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.

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seed-selection

yogsoth-ai/literature-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.

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extract-data

yogsoth-ai/literature-survey

Structured data extraction from deep-read papers — produces comparison tables (method, dataset, metrics, results, limitations). Used by systematic-survey and deep-survey.

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