- Home
- Skills
- Technical Documentation
- research-gap
Works with the AI tools you already use
Research Gap
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.
$7
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
| Gap | Why it matters | Difficulty | Impact |
|---|---|---|---|
| Cross-scanner generalization | Models degrade 15–30% on unseen scanners/centers | High | High |
| Few-shot organ segmentation | Rare anatomies lack labeled data | Medium | High |
| Uncertainty-aware segmentation | Point estimates hide model confidence | Medium | Medium |
| Multi-modal fusion (CT+MRI+text) | Single-modality limits clinical use | High | High |
| Explainability for clinicians | Trust blocks deployment | Low | Medium |
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-analysisto validate this direction against prior work. - Run
methodology-designerto plan the benchmark and baselines.
research-gap.pdf
PDF · document
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
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.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean 14 days ago
- Passed all security checks, Safe to install