Research Literature Gap-Matrix Analyzer
9-agent gap detection engine powered & gap-geometry analysis
Research Literature Gap-Matrix Analyzer
9-agent gap detection engine powered & gap-geometry analysis
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Description
Detects, classifies, and justifies literature gaps across 9 dimensions using semantic similarity and gap-geometry analysis. Transforms a research question plus 10–50 abstracts into a structured academic report with confidence scores, gap geometry matrix, relationship graph, filling strategies, and publication-ready sentence templates. Step 2-step (Gap Geometry + Deep Analysis) Sub-agent 9 (6 detection + 3 synthesis) Control Point Adaptive — 1 for ≤20 summaries, 2 for >20 summaries Language English Framework components of the skill: The bridge metaphor — the mathematical formulation X ≠ Y but X ≈ Y 4-region gap geometry (Redundancy / Gap Region / Hidden Gap / Irrelevant) 9-sub-agent architecture — Context, Method, Variable, Theory, Time, Finding, Semantic-Gap, Composition-Gap, Dependency-Gap Relationship graph — similar_to, belong_to, compose_with, depend_on Threshold calibration by discipline — social sciences (0.60), engineering (0.70), default (0.65) Set of rules for elements to be included or excluded (5+5) Selection based on confidence score — 70%+ threshold, maximum of 3 gaps Sample text ready for publication — clearly marked as AI-generated Adaptive checkpoint — approval mechanism that adjusts automatically based on the number of summaries
Related Skills
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ResearchResearch Topic & Gap Finder
Suggests original, workable research topics and gaps from your area of interest and literature knowledge; provides a research-question draft and feasibility note for each. Who it's for: graduate students and researchers looking for a thesis or article topic.
ResearchBibliometric Findings Writer
Converts the raw numbers you obtain from VOSviewer or Biblioshiny (Bibliometrix/R) outputs (keyword frequencies, citation network clusters, yearly publication trend, country/institution/journal distribution) into publication-ready, interpreted academic text. Uses an academic tone that narrates where the literature is heading rather than repeating raw numbers; avoids speculative causal claims. Suitable for: researchers writing a thesis/paper who need a bibliometric analysis section but struggle to translate raw data into academic language. Usage: paste your cluster/keyword list from VOSviewer or your Biblioshiny summary table directly; the skill thematically names and interprets the keyword clusters, and evaluates the citation network and country collaboration pattern.
ResearchMeta-Analysis Full Report Writer
Produces a complete Methods + Results + Limitations text compliant with PRISMA 2020 and the Cochrane Handbook from your meta-analysis statistics (pooled effect size, I², Q, tau², publication bias indicators). Never misinterprets the I² value (including the "percentage of variance" framing); avoids overstated causal language (uses cautious phrasing like "findings are consistent with..." instead of "evidence demonstrates that"). At the end, it also offers at least 3 questions a reviewer might ask along with defense sentences. Suitable for: researchers writing a meta-analysis paper who want to strengthen the methods section before journal submission. What it does not do: it does not perform statistical calculations from raw data (you must already have your R/RevMan/CMA output) — it only converts and interprets your results into academic text.
Information
- Version
- v2
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
