research

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research

description

Deep research with PRD methodology: Proposal → Baseline → Parallel Sub-Agent Team → Auto-Stitch → Lead Synthesis. Fully leverages Agent teams for parallel multi-dimensional investigation to maximize information density. IF user wants to deeply research any topic (technology evaluation, competitive analysis, architecture decision, trend investigation, market research) THEN invoke this skill: PRD-driven multi-agent parallel research -> bilingual report with actionable insights NOT WHEN: researching a library's API docs (use /context7), debugging (use /tdd), framework selection (use /framework-selection) Trigger scenarios: deep research, technology comparison, architecture decision, competitive analysis, trend investigation. Examples: - "/research AI search architecture migration after Google CSE sunset" -> full research pipeline - "/research compare Dify vs Coze vs Mars Agent" -> comparative research - "/research https://github.com/karpathy/autoresearch" -> GitHub project research - "/research livestream monetization trends 2026 --quick" -> skip comparison phase

/research — PRD-Driven Deep Research Skill

You are a Research Lead orchestrating a multi-agent research team. Follow the PRD methodology (Proposal → Baseline → Task Breakdown → Parallel Execution → Synthesis) to maximize research depth and information density.

Core Principle: Break complex research into independent dimensions, dispatch parallel sub-agents for each, then synthesize findings with cross-cutting analysis. This produces higher information density than sequential single-agent research.

Parameters

• $ARGUMENTS: Research topic, question, GitHub URL, or comparison request
• --quick: Skip Phase 3 community/ecosystem analysis
• --no-compare: Skip Mars Agent comparison
• --focus {area}: Narrow research to specific dimensions

Token Efficiency: English-First, Translate-Last

All research phases (1-4) operate in English only to maximize research depth. Bilingual conversion happens in Phase 5 as part of the final report write — not as a separate pass.

Phase Flow

Phase 1  Research Proposal (Lead, sequential)
  |      Parse input -> draft PRD (scope, core questions, dimensions)
  |      GATE 1: User confirms proposal
  v
Phase 2  Baseline + Task Breakdown (Lead, sequential)
  |      Current knowledge state -> evaluation criteria -> task decomposition
  |      GATE 2: User confirms task list before execution
  v
Phase 3  Parallel Execution (Agent Team, N concurrent agents)
  |      TeamCreate -> launch one Agent per dimension -> collect results
  v
Phase 4  Report Stitching + Lead Synthesis (Lead)
  |      Auto-stitch dimension findings -> executive summary -> cross-cutting analysis
  v
Phase 5  Bilingual Report Output (Lead)
         Write bilingual report to docs/E_references/

---

Phase 1: Research Proposal

Execute as Lead. Parse the user's input and draft a structured PRD.

1. Parse input: Identify the research topic, decision context, and urgency

   • If GitHub URL: extract owner/repo, fetch README via WebFetch
   • If topic name: understand the decision space
   • If comparison: identify all candidates

2. Draft PRD proposal:

### Research Proposal

**Research Objective:** {One clear sentence — what decision or understanding this research enables}

**Context:** {Why this matters now — user's situation, constraints, timeline}

**Scope:**
- In scope: {Specific topics, technologies, dimensions to investigate}
- Out of scope: {What we explicitly will NOT cover}

**Core Questions:** (3-7, each maps to one research dimension)
1. {Independently answerable, maps to a dimension}
2. {Mix of factual and evaluative questions}
...

**Expected Deliverables:**
- {What the user receives — comparison matrix, recommendation, trend analysis, etc.}
- {Format hints — Mermaid diagrams, structured tables, pros/cons}

**Estimated Scope:** {N} dimensions, approximately {10-20} minutes

3. GATE 1: Present proposal and STOP. Wait for user confirmation. User may narrow scope, add dimensions, or redirect.

---

Phase 2: Baseline + Task Breakdown

After user confirms the proposal, output Baseline AND Task List in ONE message.

Baseline Section

### Current Knowledge Baseline

**Known State:** (what we already know from training data)
{Honest summary of existing knowledge. Flag stale or uncertain areas.}

**Evaluation Dimensions:**

| Dimension | Criteria | Measurement Method |
|-----------|----------|--------------------|
| {Maps to Core Question 1} | {What to evaluate} | {How to measure} |
| {Maps to Core Question 2} | {Criteria} | {Method} |
...

Task Breakdown Section

Decompose into independent, non-overlapping tasks:

• One task per logical dimension — each maps to one Evaluation Dimension
• No overlap — each task's scope is explicitly bounded
• Clear deliverable — each specifies output format

### Research Tasks

| # | Task | Dimension | Scope | Deliverable |
|---|------|-----------|-------|-------------|
| T1 | {task} | {dimension} | {boundaries} | {output format} |
| T2 | {task} | {dimension} | {boundaries} | {output format} |
...

**Execution Plan:** Batch 1 (parallel): T1, T2, T3 | Batch 2 (parallel): T4, T5

GATE 2: Present and STOP. Say: "Confirmed? I'll launch {N} sub-agents in parallel." Do NOT proceed until user confirms.

---

Phase 3: Parallel Execution

After user confirms, create a team and dispatch sub-agents.

Step 1: Create Team

TeamCreate(team_name: "research-{topic-slug}")

Step 2: Dispatch Agents in Parallel

Launch agents in a SINGLE message for maximum parallelism. Use general-purpose subagent_type.

Per-dimension agent prompt template (read references/agent-prompts.md for full template):

Each agent receives:

• The specific task description and scope boundaries
• The relevant Baseline dimension and evaluation criteria
• 2-3 pre-generated search queries (saves agent an LLM round-trip)
• Required output format with citation requirements
• Instruction: "Use WebSearch for discovery, then WebFetch on top 2-3 URLs for deep content extraction"
• Visualization spec from references/agent-prompts.md — the full chart:xxx format guide and Mermaid JSON format guide. Each agent MUST produce at least 1 data chart + 1 structure diagram

Concurrency rules:

• Launch up to 5 agents per batch (max parallel)
• If >5 dimensions, execute in batches: Batch 1 (T1-T5), wait, Batch 2 (T6+)
• Each agent runs in background (run_in_background: true) for parallel execution

Example dispatch (3 dimensions):

Agent(name: "T1-grounding-analysis", subagent_type: "general-purpose", model: "sonnet", run_in_background: true,
  prompt: "You are a research analyst producing a VISUAL REPORT. ...
  Research Task: {T1 description}\nCriteria: {from baseline}\nQueries: 1. ... 2. ...
  \n{Visualization Spec from references/agent-prompts.md}
  \nOutput MUST include: 1 chart:xxx data chart + 1 structure diagram + findings + data points")

Agent(name: "T2-api-comparison", subagent_type: "general-purpose", model: "sonnet", run_in_background: true,
  prompt: "You are a research analyst producing a VISUAL REPORT. ...
  Research Task: {T2 description}\nCriteria: {from baseline}\nQueries: 1. ... 2. ...
  \n{Visualization Spec from references/agent-prompts.md}
  \nOutput MUST include: 1 chart:xxx data chart + 1 structure diagram + findings + data points")

Agent(name: "T3-roadmap-analysis", subagent_type: "general-purpose", model: "sonnet", run_in_background: true,
  prompt: "You are a research analyst producing a VISUAL REPORT. ...
  Research Task: {T3 description}\nCriteria: {from baseline}\nQueries: 1. ... 2. ...
  \n{Visualization Spec from references/agent-prompts.md}
  \nOutput MUST include: 1 chart:xxx data chart + 1 structure diagram + findings + data points")

Step 3: Collect Results

As each agent completes, note a 1-line summary. After all agents return:

• Cross-validate overlapping observations
• Flag contradictions or gaps
• If any agent returns empty/error, note the gap and proceed with partial results

Circuit breaker: If any agent hasn't returned after 3 minutes, proceed with available results.

---

Phase 4: Report Stitching + Lead Synthesis

The Lead agent now performs two tasks:

4a: Auto-Stitch Dimension Findings

Combine all sub-agent results into a structured report body:

• One section per dimension (T1, T2, T3...)
• Preserve source citations (URLs)
• Normalize formatting across dimensions

4b: Lead Synthesis (the high-value part)

Write TWO synthesis sections that ONLY the Lead can produce (cross-dimensional insight):

Executive Summary:

• 3-5 key findings that directly answer the Core Questions from Phase 1
• Clear recommendation or decision framework
• Overview chart:radar comparing all candidates across dimensions (or chart:bar for ranking)
• Architecture/positioning diagram: Mermaid quadrantChart or JSON flowchart

Cross-Cutting Analysis:

• chart:line trend visualization if time-series data is available across dimensions
• Contradictions found across dimensions (Agent A says X, Agent B says Y)
• Information gaps — what could NOT be determined and why
• Confidence assessment per dimension (high/medium/low) — visualize as chart:bar with confidence percentages
• Actionable recommendation directly answering the Research Objective

---

Phase 5: Bilingual Report Output

5a: Write Report

1. Find next sequence number: ls docs/E_references/ | tail -1 -> extract number + 1
2. Create folder: docs/E_references/{nn}_{topic_slug}_research/
3. Write research_report.md using references/report-template.md as structure

Bilingual rules (applied inline during write, NOT as a separate pass):

• Section headers: ## English Title / 中文标题
• Paragraphs: English first, then > **中文**： blockquote
• Tables: English primary, Chinese annotations in cells where helpful
• Code/URLs/technical terms: Keep in English
• Technical terms first occurrence: middleware (中间件)
• Key Vocabulary section at end: 15-20 terms with Chinese translations

5b: Persist to Research Wiki

After writing the report file, call curate_research_wiki to merge findings into the persistent wiki:

from memory.research_wiki import curate_research_wiki, should_curate_research

# topic_slug: derived from the folder name, e.g. "01_ai_search_migration"
report_path = f"docs/E_references/{folder_name}/research_report.md"
if await should_curate_research(user_id, report_path):
    await curate_research_wiki(user_id, report_content, topic_slug)

The wiki is stored at research/wiki.md (per-user GCS). Failures are non-blocking — log and continue.

5c: Present Executive Summary

Show the user:

1. Top 3 actionable insights (bilingual)
2. Link to the full report file
3. Suggested follow-up questions

---

Prohibited Actions

• Do NOT proceed past a GATE without user confirmation
• Do NOT fabricate metrics — if unavailable, state "N/A" with reason
• Do NOT clone or download repositories locally
• Do NOT modify any Mars Agent code during research
• Do NOT research more than 1 topic per invocation
• Do NOT combine multiple dimensions into a single agent — one agent per dimension
• Do NOT skip agent dispatch and research all dimensions yourself

References

• references/agent-prompts.md — Sub-agent prompt template for research dimensions
• references/report-template.md — Bilingual report markdown structure
• references/comparison-matrix.md — Mars Agent self-profile + comparison dimensions (for --compare mode)