Research Clustering — Domain Identification, Partitioning & Structuring

If $ARGUMENTS does NOT contain --auto, proceed with the normal interactive workflow below.

Workflow

Step 1: Parse Input

Analyze the user's input and extract research seeds.

Accepted input formats (support all):

• Markdown file (keyword list, research theme description, etc.)
• Conversational text (natural-language description of the research topic)
• Any combination of the above

Elements to extract:

• Core keywords (technical terms, proper nouns, abbreviations)
• Research purpose and context (why do they want to investigate this?)
• Target domains (academic, patent, business, technology trends, etc.)
• Known constraints (excluded fields, language, region, etc.)

Step 1.5: Preliminary Scan (Quick Scan)

Before the user hearing, run a lightweight web search on the input keywords to form a hypothesis about the research landscape. The goal is to improve the quality of the hearing — instead of asking vague questions like "what do you want to research?", present concrete options like "here's the field structure I see — which direction should we go deeper?"

Preliminary search (2–3 queries, keep it lightweight):

• "{core keyword} survey" or "{core keyword} overview" to grasp the overall landscape
• "{core keyword} applications" or "{core keyword} use cases" to check application areas
• If the input keywords are in Japanese, translate to English and search in both languages

Information to extract from the preliminary scan:

• Major sub-areas of the field (tentative cluster candidates)
• Recent trends and hot topics
• Potentially related adjacent fields
• Whether the field is paper-heavy, patent-heavy, or practice/case-study-heavy

Present the hypothesis: Summarize the preliminary scan results concisely and present them during the hearing as: "Based on a quick scan, the field appears to be structured as follows..." This lets the user compare against their own research intent and give more precise direction.

Step 2: User Hearing

--auto mode: Skip this entire step. Use the default values from the Auto Mode table above.

Confirm research parameters via AskUserQuestion, informed by the preliminary scan results. Reflect scan-derived hypotheses in default values and option descriptions to reduce user burden.

Hearing 1: Research Type

AskUserQuestion:
  question: "What type of research would you like to conduct? (multiple selection)"
  header: "Research Type"
  multiSelect: true
  options:
    - label: "Academic Paper Survey"
      description: "Focus on academic papers (arXiv, IEEE, ACM, etc.)"
    - label: "Patent Search"
      description: "Focus on patent literature (USPTO, EPO, JPO, etc.)"
    - label: "Technology Trend Analysis"
      description: "Tech blogs, conference talks, OSS projects, etc."
    - label: "Business Case Study"
      description: "Enterprise adoption cases, market reports, industry trends"

Hearing 2: Time Range

AskUserQuestion:
  question: "What time range should the research cover?"
  header: "Time Range"
  multiSelect: false
  options:
    - label: "Last 4 years (default)"
      description: "Results from 2022 to present"
    - label: "Last 2 years"
      description: "Focus on the latest trends only"
    - label: "Last 7 years"
      description: "Broader coverage"
    - label: "Custom range"
      description: "Specify a custom time range"

If "Custom range" is selected, ask a follow-up AskUserQuestion for the specific years.

Hearing 3: Search Languages

AskUserQuestion:
  question: "Which languages should be used for web searches? (multiple selection)"
  header: "Search Languages"
  multiSelect: true
  options:
    - label: "English"
      description: "Search in English (covers most academic and international sources)"
    - label: "Japanese"
      description: "Search in Japanese (useful for JPO patents, domestic cases, Japanese papers)"
    - label: "Chinese"
      description: "Search in Chinese (useful for Chinese patents, CNKI papers)"
    - label: "Other"
      description: "Specify additional languages"

If "Other" is selected, ask a follow-up AskUserQuestion for the specific languages. Pre-select defaults based on the input: if the input contains Japanese text, default to English + Japanese; otherwise default to English only.

Hearing 4: Output Granularity

AskUserQuestion:
  question: "What level of detail do you want in the output?"
  header: "Output Granularity"
  multiSelect: false
  options:
    - label: "Standard (recommended)"
      description: "Domain partitioning + overview, keywords, and research strategy per cluster"
    - label: "Detailed"
      description: "Standard + representative papers/patents/cases per cluster"
    - label: "Overview only"
      description: "Domain partitioning and keywords only — quick big-picture view"

Step 3: Domain Identification (Web Search)

Based on input keywords and confirmed parameters, use WebSearch to investigate related academic/technical domains.

Search strategy:

1. Initial search: Broad search using core keyword combinations

   • "{keyword1} {keyword2} survey" / "{keyword1} {keyword2} review"
   • "{keyword} taxonomy" / "{keyword} classification"
   • If keywords are in Japanese, translate to English and search in both languages

2. For academic paper surveys, prioritize review/survey papers

   • Review and survey papers provide an efficient overview of the entire field, so search for these first
   • Example queries: "{topic} survey paper {year}", "{topic} systematic review", "{topic} literature review"
   • Use the taxonomy/classification from discovered survey papers as the starting point for domain partitioning

3. Expansion search: Discover related fields from initial results

   • Identify adjacent domains from survey paper sections and classification schemes
   • Discover new axes from frequently co-occurring keywords

4. For patent searches: Also investigate IPC/CPC classification codes for technology classification reference

Compile a comprehensive list of candidate academic/technical domains related to the input theme.

Step 4: Domain Clustering (Partitioning)

Partition the research targets into meaningful clusters based on information collected in Step 3.

Partitioning principles:

• Each cluster should be independently researchable
• Cluster boundaries should be clear (minimize overlap)
• Aim for 3–8 clusters (not too coarse, not too fine)
• Use up to 2 levels of hierarchy (major → minor) when the structure naturally calls for it

Partitioning perspectives (select based on research type):

• By method/approach (e.g., statistical methods / deep learning / rule-based)
• By application domain (e.g., healthcare / finance / manufacturing)
• By timeline (e.g., classical methods / recent developments / latest trends)
• By problem formulation (e.g., classification / regression / generation)

Step 5: Cluster Elaboration

For each cluster, produce:

• Cluster name: A concise, descriptive title
• Overview: 2–5 sentences explaining the scope of the cluster
• Keywords: Technical terms and concepts belonging to the cluster (5–15 items)
• Research strategy: Recommended approach for deeper investigation of this cluster
• Representative resources (only for "Detailed" granularity): A few survey papers, key papers/patents/cases

For academic paper surveys, list the survey/review papers found in Step 3 as "seed resources" for the relevant cluster. Also show how the survey paper's taxonomy maps to the cluster structure.

Step 6: Output File Generation

Select the output format based on the research scale:

Small to medium scale (5 or fewer clusters): Single file

Consolidate everything into one Markdown file.

Large scale (6 or more clusters): Directory structure

{output-dir}/
  index.md          # Overall overview + cluster list
  cluster-01-xxx.md # Detailed info for each cluster
  cluster-02-xxx.md
  ...

Output Template (single file / index.md)

# {Research Theme}

## Research Parameters

- **Research type**: {Academic Paper Survey / Patent Search / ...}
- **Time range**: {YYYY – YYYY}
- **Generated on**: {YYYY-MM-DD}
- **Input keywords**: {original keyword list}

## Big Picture

{3–5 sentences on the overall positioning of the research theme, current state of the field, and major trends}

## Reference Survey/Review Papers

{For academic paper surveys. List discovered survey papers. Note that their taxonomies informed the domain partitioning}

| Title | Year | Summary | Link |
|-------|------|---------|------|
| {title} | {year} | {one-line summary} | {url} |

## Domain Map

{Conceptual diagram showing inter-cluster relationships. Use ASCII art or Mermaid notation}

## Cluster Summary

| # | Cluster Name | Keyword Count | Summary |
|---|-------------|---------------|---------|
| 1 | {name} | {n} | {one-line summary} |

## Cluster Details

### Cluster 1: {Cluster Name}

**Overview**: {2–5 sentence description}

**Keywords**:
`keyword1`, `keyword2`, `keyword3`, ...

**Research Strategy**:
- {Recommended search queries and information sources}
- {Notable research groups or companies}
- {Recommended reading order from survey papers}

**Representative Resources** (detailed granularity only):
| Title | Type | Year | Summary |
|-------|------|------|---------|
| {title} | Paper/Patent/Case | {year} | {summary} |

---

{Repeat for each cluster}

Step 7: Output Confirmation and Next Actions

--auto mode: Skip this step entirely. Treat the result as "Done" and finish.

After output is complete, confirm the next action via AskUserQuestion.

AskUserQuestion:
  question: "The domain map is complete. What would you like to do next?"
  header: "Next Action"
  multiSelect: false
  options:
    - label: "Done"
      description: "Finalize the current output"
    - label: "Adjust clusters"
      description: "Change partitioning granularity or classification axes and regenerate"
    - label: "Deep-dive into a specific cluster"
      description: "Investigate papers/patents for a selected cluster in detail"
    - label: "Generate research prompts"
      description: "Generate research prompt files for each cluster (integrates with research-prompt-builder)"

If "Deep-dive into a specific cluster" is selected, show a follow-up AskUserQuestion with cluster names as options. For deep-dive research, also suggest integrating with the research-retrieval skill to generate detailed paper lists per cluster.

Output Location

MUST READ FIRST: Before deciding the output path, read docs/research/README.md (the single source of truth for the research directory layout) and .claude/rules/research.md.

Path resolution

1. Identify the domain (<domain>, snake_case):

   • Infer from input file path if it lives under docs/research/(runs|domains)/<domain>/...
   • Otherwise infer from the research theme or ask the user

2. If docs/research/domains/<domain>/domain.yaml exists and defines output_paths.clustering, use it.

3. Otherwise use the default path:

   docs/research/runs/<domain>/clustering/<YYYYMMDD>/
   

   • Output index.md and cluster-NN-*.md files inside this directory.
   • <YYYYMMDD> is today's date (UTC or JST is acceptable, use whichever the pipeline uses).

4. Never write directly under docs/research/domains/<domain>/ — that layer is composed of symlinks pointing into runs/.

5. Never overwrite or modify existing files under runs/<domain>/clustering/<old_date>/ — clustering is append-only. Re-clustering creates a NEW dated directory.

After writing

After the new clustering run is written, update (or instruct the pipeline/user to update) the latest pointer:

ln -snf <YYYYMMDD> docs/research/runs/<domain>/clustering/latest
ln -snf ../../runs/<domain>/clustering/latest docs/research/domains/<domain>/clustering

If the domains/<domain>/ directory does not yet exist, create it (it is just a view layer).

Parallel Processing

Run multiple web search queries in parallel for efficiency. Use the Agent tool to spawn subagents for concurrent searches.

Integration with Other Skills

• research-prompt-builder: Can generate research prompts for each cluster
• research-retrieval: Can produce detailed paper lists per cluster

Language

• User interactions (AskUserQuestion, etc.) follow the project's response language setting
• Technical terms, paper titles, and proper nouns are kept in their original language
• Web searches are conducted in the languages selected during Hearing 3 (Search Languages)
• All user-facing output, reports, and summaries must be written in Japanese(すべてのユーザーへの出力は日本語にしてください)