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    Research Gap

    1

    Systematically identifies research gaps and unexplored dimensions across six technical domains. Find the questions nobody answered, analyze your papers and uncover the gaps the community missed.

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    research-gap

    research-gap

    Example session with this skill installed

    Analyzed 24 papers (2018–2025) across segmentation methods and found 5 concrete gaps, ranked by impact and difficulty. The most promising: generalization to out-of-distribution scanners and domain shift.

    • Read your context and instructions
    • Compiled the research-gap
    • Generated the document

    I have analyzed the provided 15 papers on GNNs for fraud detection. The community focuses heavily on static accuracy (AUC/F1) but largely ignores temporal decay and adversarial robustness.

    Identified research gaps

    GapWhy it mattersDifficultyImpact
    Cross-scanner generalizationModels degrade 15–30% on unseen scanners/centersHighHigh
    Few-shot organ segmentationRare anatomies lack labeled dataMediumHigh
    Uncertainty-aware segmentationPoint estimates hide model confidenceMediumMedium
    Multi-modal fusion (CT+MRI+text)Single-modality limits clinical useHighHigh
    Explainability for cliniciansTrust blocks deploymentLowMedium

    Where the community is stuck

    • Missing datasets: no large public benchmark with multi-center, multi-scanner labels for abdominal organs.
    • Incomplete comparisons: most papers benchmark on single-center data (e.g., BTCV, LiTS) and don't report domain-shift performance.
    • Under-explored: test-time adaptation for segmentation is rarely combined with foundation models (e.g., SAM).

    Recommended direction

    Few-shot, cross-scanner segmentation with a foundation-model backbone — combines the two highest-impact gaps and has no dominant existing solution.

    Next steps

    • Run novelty-analysis to validate this direction against prior work.
    • Run methodology-designer to plan the benchmark and baselines.

    research-gap.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Identify novel PhD thesis directions by mapping unexplored problem variants.Strengthen paper submissions with an evidence-backed Research Gaps section.Discover high-impact, low-effort publication opportunities in data space gaps.Prioritize research agendas using a feasibility versus impact matrix.

    About this skill

    The problem

    The difference between a rejected paper and a published one is usually the gap it fills. This skill finds it systematically.

    What it does

    • Analyzes your paper collection across six unexplored dimensions: problems, methods, data, evaluation, theory, and domain.
    • Surfaces open problems, missing datasets, and under-explored research questions.
    • Flags incompletely compared methods, missing benchmarks, and unreproduced results.
    • Ranks gaps by impact vs. feasibility and recommends concrete research directions.
    • Identifies unexplored problem variants and real-world edge cases existing methods fail to address.
    • Analyzes methodological combinations and cross-disciplinary techniques not yet applied to the domain.
    • Maps missing datasets, modality gaps, and benchmark limitations across the current literature.
    • Detects evaluation flaws, including missing head-to-head comparisons and unmeasured performance metrics like robustness or fairness.
    • Prioritizes identified gaps into a feasibility-versus-impact matrix for strategic research planning.

    Why this beats prompting it yourself

    Generic prompts often return vague suggestions like "more data is needed." This skill uses a six-dimension framework to systematically interrogate the research space, ensuring technical gaps like theoretical guarantees and scale limitations are surfaced with specific evidence.

    Use cases

    • Defining a PhD thesis topic or a high-impact research direction.
    • Writing the "Problem Statement" or "Research Gaps" section of a manuscript.
    • Validating the novelty of a proposed contribution before starting implementation.
    • Identifying "low-hanging fruit" for quick publication through missing method combinations.

    Known limitations

    Accuracy depends entirely on the quality and breadth of the provided paper collection. It cannot identify gaps in work that has not been published or indexed.

    Important

    It identifies gaps; it does not validate a specific idea as novel. Pair it with novelty-analysis for that.

    Why buy it

    It turns "what's been done" into "what I should do." Use it right after literature-discovery or state-of-the-art to land your contribution in clear, unclaimed territory.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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      Ask your agent to use it

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    Recently published to Agensi

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    Verified clean 14 days ago

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