gap-analysis

name	gap-analysis
description	Use when the user asks to analyze research gaps, find research gaps, generate research ideas, or analyze papers for gaps. Read a set of papers, summarize methods and limitations, identify open questions, and propose executable research ideas aligned with the user's profile.

Research Gap Analysis

Preconditions

• Read ../../memory.md first.
• If there is no CV profile yet, first suggest that the user run cv-analyze, because feasibility judgment depends on the user's ability background.

Language Rules

• Support three output modes: zh, en, and bilingual.
• If the user explicitly specifies the output language, prioritize the current request.
• Otherwise read preferred_language from memory.md.
• If it is still unclear, follow the user's current conversation language.
• Academic proper nouns such as paper titles, method names, and conference names may remain in the original language, while the analysis and conclusions should follow the selected language.

Optional Linkage: Life Science Research

• If the user's question clearly belongs to life sciences / biomedical research, prioritize treating life-science-research as the research evidence layer, while the current skill serves as the application-oriented synthesis layer.
• This is especially suitable for linkage in scenarios such as:
  • needing to first sort out gene / protein / disease / pathway / expression / clinical evidence
  • needing to first find public datasets, preprints, or omics resources before discussing research gaps
  • needing to turn public evidence in biomedical directions into research ideas that are usable for applications, interviews, or proposals
• After linkage, this skill is responsible for:
  • summarizing cross-evidence signals
  • evaluating feasibility together with memory.md
  • narrowing research gaps into 3 to 5 application-oriented ideas
• If the user has already provided a clear paper set and only wants local gap comparison without additional background expansion, do not trigger that linkage.

Clarify the Input Source First

First confirm the paper source with the user:

• local folder
• single / multiple PDFs
• Zotero collection or item

If the user wants to use Zotero and the current environment has usable Zotero tools, use them; otherwise fall back to local files.

Processing Workflow

1. List the papers to be analyzed and confirm the scope with the user.
2. Extract the following from each paper:
   • core question
   • method
   • experiments / results
   • limitations
3. Perform cross-paper comparison, cutting in from at least four types of gaps:

• method gaps
• application gaps
• theoretical gaps
• engineering / efficiency gaps

4. Combine the skill profile in memory.md to propose 3 to 5 research ideas and judge feasibility.

Parallel Strategy

• By default, sequential or batched local processing is sufficient.
• Only when the user explicitly asks for "parallel", "sub-agents", or "delegation" may spawn_agent be used for per-paper parallel work.
• Even when parallelized, the final comparison, conflict judgment, and synthesized conclusion must still be completed by the main agent.

Output Requirements

• First provide a gap summary.
• Then provide 3 to 5 ideas, each of which includes:
  • research question
  • method idea
  • basis for novelty
  • required skills / resources
  • feasibility：High / Medium / Low
  • potential submission direction

Constraints

• Do not directly package future work explicitly discussed by the paper authors themselves as a "novel idea".
• Feasibility evaluation must explicitly reference the user's existing skills, rather than giving a vague score.