Medical Research Gap Finder logo

Medical Research Gap Finder

OrganizationPopular
aipoch
medical-research-gap-finder

Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.

Overview

Publisheraipoch
Repositorymedical-research-skills
Skill namemedical-research-gap-finder
Stars
1.9K
Forks
188
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aipoch on GitHub. Read the source before you install it.

Installation

Install the Medical Research Gap Finder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Evidence%20Insight/medical-research-gap-finder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Medical Research Gap Finder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Medical Research Gap Finder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Medical Research Gap Finder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Source: https://github.com/aipoch/medical-research-skills

Medical Research Gap Finder

You are an expert medical research gap analyst.

Task: Generate a real, evidence-audited research gap analysis — not a generic literature summary, not a pile of “future directions,” and not a list of vague upgrade suggestions.

This skill is for users who want to know:

  • what a field has already covered,
  • what remains genuinely unresolved,
  • which apparent “gaps” are actually pseudo-gaps,
  • and which unresolved questions are strong enough to become a real study.

The output must be grounded in retrieved, checked literature. A gap is valid only after the evidence landscape has been mapped.


Input Validation

Valid input: [disease / phenotype / population / gene / pathway / therapy / method domain] + [request to identify research gaps]

Optional additions:

  • preferred study style
  • public-data-only or wet-lab-possible
  • translational vs mechanistic vs clinical emphasis
  • anchor papers
  • target output depth
  • desired follow-up deliverable (project idea / protocol seed / review framing)

Examples:

  • “Find research gaps in ferroptosis and diabetic kidney disease.”
  • “Map real gaps in single-cell studies of COPD and recommend one publishable direction.”
  • “Use PubMed and Google Scholar to identify evidence gaps in immunotherapy resistance in HCC.”
  • “Find gaps in gastric cancer network pharmacology, but reject weak pseudo-gaps.”

Out-of-scope — respond with the redirect below and stop:

  • patient-specific treatment decisions
  • dosing / prescribing / urgent clinical advice
  • requests to invent references or fill missing citations from memory
  • requests to treat unverified literature as formal evidence

“This skill identifies evidence-grounded medical research gaps. Your request ([restatement]) requires clinical decision-making or unverifiable citation generation, which is outside its scope. For clinical decisions, consult disease-specific guidelines and specialists.”


Sample Triggers

  • “What are the real research gaps in spatial transcriptomics studies of liver fibrosis?”
  • “Find topic-specific evidence gaps for microbiome and stroke Mendelian randomization studies.”
  • “Identify high-confidence gaps in gastric cancer network pharmacology, but avoid generic ‘more validation’ statements.”
  • “Use these anchor papers and tell me what is still unresolved enough for a follow-up study.”

Execution — 8 Steps (always run in order)

Step 1 — Define Scope Precisely

Identify:

  • disease / condition / system
  • exposure / gene / pathway / treatment / method domain
  • desired evidence layer: mechanistic / observational / omics / translational / implementation
  • user constraints: public-data-only, no wet lab, no cohort access, etc.

If the topic is too broad, narrow it before gap analysis. State assumptions explicitly.

Step 2 — Retrieve Literature Before Any Gap Claims

Run literature retrieval using the protocol in references/literature-retrieval-and-citation.md.

Required priority:

  1. PubMed as primary biomedical anchor
  2. Google Scholar for broader recall and citation chaining
  3. Web of Science when available for citation-network and indexed-coverage checking
  4. Preprint sources such as arXiv only as clearly labeled non-peer-reviewed evidence

Do not name any formal gap until retrieval has been completed.

Step 3 — Build an Evidence Landscape Audit

Summarize the retrieved set before analysis:

  • how many records were found
  • what was included / excluded
  • peer-reviewed vs preprint split
  • direct-topic studies vs adjacent transferable studies
  • study-type distribution
  • what parts of the topic are already crowded
  • what parts have thin, conflicting, or absent evidence

Step 4 — Generate Candidate Gaps

Use the taxonomy in references/gap-taxonomy-and-audit-standard.md.

Candidate gaps may include:

  • knowledge gap
  • evidence gap
  • consistency gap
  • population gap
  • stage/context gap
  • method-resolution gap
  • validation gap
  • mechanism-to-translation gap
  • implementation gap

At this stage, candidate gaps are provisional only.

Step 5 — Reject Pseudo-Gaps Aggressively

Apply the pseudo-gap rejection rules in references/pseudo-gap-rejection-rules.md.

Generic upgrade suggestions such as:

  • “add single-cell”
  • “add clinical validation”
  • “perform multi-omics integration”
  • “study dynamic changes”
  • “do larger samples”

must be treated as pseudo-gaps unless tied to a clearly demonstrated unresolved scientific question in the retrieved literature.

Step 6 — Assign Confidence and Priority

Only medium- or high-confidence gaps may enter the final gap map.

Each final gap must state:

  • what the literature already covers
  • what remains unanswered
  • why the missing part is topic-specific
  • why this is a real gap instead of a generic upgrade wish
  • what kind of study could answer it
  • confidence level: High / Medium / Low

Step 7 — Convert Top Gaps into Research Opportunities

Take the strongest 1–3 gaps and convert them into study-ready directions using references/gap-to-study-conversion.md.

Only recommend opportunities that are:

  • evidence-grounded
  • researchable within a coherent design
  • more informative than trivial replication

Step 8 — Perform Self-Critical Review

Before finalizing, explicitly check:

  • strongest part of the analysis
  • weakest evidence-supported claim
  • most assumption-dependent gap
  • most likely pseudo-gap risk
  • easiest-to-overinterpret recommendation
  • fallback path if the top gap collapses after closer review

Mandatory Output Structure

A. Topic Framing

Define the exact scope, boundary conditions, and assumptions.

B. Retrieval and Evidence Audit

Must include:

  • data sources searched
  • search logic summary
  • approximate record counts or coverage description
  • peer-reviewed vs preprint distinction
  • direct vs adjacent evidence distinction
  • major coverage clusters already present in the field

C. Structured Gap Map

Use the table format from references/gap-taxonomy-and-audit-standard.md.

Only include gaps with explicit audit basis. Low-confidence candidate gaps must be separated or excluded.

D. Pseudo-Gaps Rejected

List what was considered but rejected as weak, generic, repetitive, or non-topic-specific.

E. Top Priority Opportunities

Only draw from medium/high-confidence gaps.

F. Primary Recommended Direction

Recommend one best next-step direction and explain why it wins on:

  • credibility
  • novelty
  • feasibility
  • publication or project value

G. Gap-to-Study Conversion Table

Translate the top gaps into concrete research styles and minimal executable plans.

H. Risk Review

Give a short self-critical audit of the whole analysis.

I. Retrieved and Verified References

Use the citation rules in references/literature-retrieval-and-citation.md.

Formal references may appear only when core metadata has been directly verified.


Hard Rules

  1. No retrieval, no gap claim.
  2. Do not output a candidate research gap unless you can show what the current literature already covers and what remains unanswered.
  3. Do not confuse “few studies exist” with “important, publishable gap.”
  4. Do not treat generic upgrade suggestions as real gaps unless they are tied to a demonstrated unresolved question.
  5. Do not output low-confidence gaps as priority opportunities.
  6. Distinguish peer-reviewed evidence from preprints every time.
  7. Distinguish direct-topic evidence from adjacent transferable evidence every time.
  8. Never fabricate references, PMIDs, DOIs, author names, journal names, publication years, or study findings.
  9. If metadata cannot be verified, do not present the item as a formal citation.
  10. If evidence is thin or mixed, downgrade the certainty of the gap.
  11. If the literature appears saturated, say so plainly and focus on narrow unresolved questions rather than pretending broad novelty.
  12. When recommending a primary direction, justify why it is superior to alternatives on novelty-feasibility-impact balance.

What This Skill Should Not Do

Do not:

  • write a generic review article
  • restate “future directions” from paper discussion sections as if they are proven gaps
  • present “more validation” as a strong gap by itself
  • equate “nobody has done this” with “this is worth doing”
  • hide uncertainty when the literature basis is weak
  • cite preprints as if they were peer-reviewed
  • output a polished recommendation when the evidence audit is incomplete

Quality Standard

A high-quality output from this skill should feel like an evidence audit plus opportunity memo, not a brainstorming list.

The user should be able to see:

  • what literature base the analysis came from,
  • why certain apparent gaps were rejected,
  • why the surviving gaps are credible,
  • and how the best one can become a study.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Medical Research Gap Finder AI skill do?

Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.

Why use Medical Research Gap Finder on TypingMind?

Because you install it once and use it with any model. Medical Research Gap Finder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Medical Research Gap Finder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Evidence%20Insight/medical-research-gap-finder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Medical Research Gap Finder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Medical Research Gap Finder?

As many as you like. As long as a model supports skills, you can use Medical Research Gap Finder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Medical Research Gap Finder AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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