deep-research

name	deep-research
description	Web research with Graph-of-Thoughts for fast-changing topics. Use when user requests research, analysis, investigation, or comparison requiring current information. Features hypothesis testing, source triangulation, claim verification, Red Team, self-critique, and gap analysis. Supports Quick/Standard/Deep/Exhaustive tiers. Creative Mode for cross-industry innovation.

Deep Research

Enhanced research engine for topics where training data is outdated.

Quick Start

Standard Mode

CLASSIFY → LANDSCAPE SCAN → RECENCY PULSE → SCOPE → HYPOTHESIZE → PLAN → [PLAN PREVIEW*] → RETRIEVE
→ GAP ANALYSIS → TRIANGULATE → SYNTHESIZE → RED TEAM → SELF-CRITIQUE → PACKAGE

*Deep+ tier only

LANDSCAPE SCAN (MANDATORY - Before Anything Else)

[Search for OVERVIEW first - NO known entity names in query!]
WebSearch: "[topic] landscape overview [current year]"
WebSearch: "top [topic] list [current year]"
WebSearch: "[topic] ecosystem players [current year]"

❌ WRONG: "DeepSeek Qwen performance 2025" (uses names you already know)
✅ RIGHT: "China open source LLM models list 2025" (discovers what exists)

→ Extract ALL entity names from results
→ List: Discovered (new to you) vs Confirmed (you knew)
→ THEN proceed to RECENCY PULSE

Why: You cannot research what you don't know exists. Scan the landscape FIRST.

RECENCY PULSE (MANDATORY - After Landscape Scan)

[Search for LATEST news — within days/weeks, not just "this year"]
WebSearch: "[topic] latest news this week [current month] [current year]"
WebSearch: "[topic] new release announcement [current month] [current year]"
WebSearch: "[upstream provider 1] latest release [current year]"
WebSearch: "[upstream provider 2] latest release [current year]"

→ Check: anything released in the last 7-30 days?
→ If yes: add to entity list, flag as BREAKING/RECENT
→ THEN proceed to SCOPE with complete picture

UPSTREAM CHECK (part of Recency Pulse):

For any product/platform research, identify the SUPPLY CHAIN:
- Who MAKES the underlying technology? (e.g., OpenAI → GPT, Anthropic → Claude)
- Who DISTRIBUTES it? (e.g., Microsoft → Copilot, GitHub → Copilot)
- Who COMPETES with it? (e.g., Google → Gemini)

Search EACH upstream provider directly — don't rely on downstream announcements.

Example for "Microsoft Copilot":
  Upstream: OpenAI (GPT models), Anthropic (Claude models)
  Downstream: Microsoft (Copilot products)
  → Search "OpenAI latest model [month] [year]"
  → Search "Anthropic latest release [month] [year]"
  → Search "Microsoft Copilot new features [month] [year]"

Why: Downstream products lag behind upstream releases. A new model from OpenAI/Anthropic may not appear in "Microsoft Copilot updates" for weeks. If you only search downstream, you miss what's coming or just arrived.

Anti-pattern: ค้นแค่ "Microsoft Copilot new features 2026" แล้วหยุด Better: ค้น upstream (OpenAI, Anthropic) + downstream (Microsoft) + "this week/month"

Creative Mode

ABSTRACT → MAP (3-5 domains) → SEARCH → GENERALIZE → SYNTHESIZE

Trigger: "creative mode", "cross-industry", "what do others do"

Example: "ทำยังไงให้คนมา engage กับ online course มากขึ้น?" → ABSTRACT: "retention + engagement ในกิจกรรมที่ทำซ้ำ" → MAP: Gaming (streaks, XP), Fitness apps (habit loops), YouTube (thumbnails, hooks), Loyalty programs (tiers) → SEARCH each domain → GENERALIZE patterns → SYNTHESIZE recommendations

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Classification

Type	When	Process	Example
A	Single fact	WebSearch → Answer	"Python 3.13 release date คือเมื่อไหร่?"
B	Multi-fact	Scan → Retrieve → Synthesize	"เปรียบเทียบ pricing ของ cloud GPU providers"
C	Judgment needed	Full 6 phases	"ควรใช้ Next.js หรือ Astro สำหรับ blog?"
D	Novel/conflicting	Full + Red Team	"AI จะแทนที่ data analyst ภายใน 3 ปีจริงไหม?"

Intensity Tiers

Tier	Sources	When
Quick	5-10	Simple question
Standard	10-20	Multi-faceted
Deep	20-30	Novel, high stakes
Exhaustive	30+	Critical decision

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Search & Evidence

Parallel Search (MANDATORY)

[Single message — always 2-3 queries at once]
WebSearch: "[topic] [current year]"
WebSearch: "[topic] limitations"
WebSearch: "[topic] vs alternatives"

Claim Types

Type	Requirements	Example
C1 (Key claim)	Quote + 2+ sources + confidence	"Next.js มี market share 42%"
C2 (Supporting)	Citation required	"Vercel เป็นผู้พัฒนา Next.js"
C3 (Common knowledge)	Cite if contested	"React เป็น library ยอดนิยม"

Confidence Format (C1 claims)

**Claim:** [Statement]
**Confidence:** HIGH/MEDIUM/LOW
**Reason:** [Why this confidence level]
**Sources:** [1][2]

Anti-Hallucination

• Every C1 cites [N] immediately
• Use "According to [1]..."
• Admit: "No sources found for X"

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Research Sufficiency

"เมื่อไหร่ถึงจะพอ?"

Signal	หมายความว่า
Saturation	3 sources ต่อเนื่องไม่ให้ข้อมูลใหม่ → พอแล้ว
Convergence	หลาย sources สรุปเหมือนกัน → confidence สูง
Contradiction	Sources ขัดแย้งกัน → ต้อง dig deeper หรือ flag uncertainty
Diminishing returns	เพิ่ม search แต่ได้แค่ rephrase ของเดิม → หยุดได้

Quick tier: หยุดเมื่อ saturation Standard: หยุดเมื่อ convergence + gap analysis ไม่เจอ gap สำคัญ Deep/Exhaustive: หยุดเมื่อ Red Team challenge ไม่พบจุดอ่อนใหม่

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Facilitation Guide

Progress Reporting

ทุกๆ 5-8 sources → update ผู้ใช้:
"สรุปที่พบจนถึงตอนนี้: [key findings]
ยังมีคำถามค้าง: [gaps]
จะ search ต่อเรื่อง [next direction] นะคะ"

When to Ask User

สถานการณ์	ถามว่า
Topic กว้างเกินไป	"อยากเน้นมุมไหนคะ? [option A] หรือ [option B]?"
เจอ sub-topic น่าสนใจ	"เจอเรื่อง X ที่เกี่ยวข้อง — อยากให้ขุดลึกไหมคะ?"
Sources ขัดแย้ง	"แหล่ง A บอกว่า X แต่แหล่ง B บอกว่า Y — พี่ระ lean ทางไหนคะ?"
Deep+ tier, plan ready	"นี่คือ plan สำหรับ research — approve ก่อนไปต่อนะคะ"

Don't Ask — Just Do

• Type A questions → ตอบเลย
• Choosing search queries → ทำเลย ไม่ต้องถาม
• Formatting output → ใช้ template ได้เลย

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Tools & Fallbacks

URL Fallback

If WebFetch returns 403:

curl -s --max-time 60 "https://r.jina.ai/https://example.com"

GitHub Repository Research

เจอ repo น่าสนใจ → ถาม user ก่อน clone:

"เจอ repo ที่น่าสนใจ: [repo-name] — ต้องการให้ clone มาศึกษา code ไหมคะ?"

If agreed:

mkdir -p /mnt/d/githubresearch && cd /mnt/d/githubresearch && git clone [repo-url]

Key files: package.json/pyproject.toml → src/ main logic → README.md

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References

Topic	File	Grep Pattern
Phase details	standard-mode.md	grep -n "^## Phase"
Creative mode	creative-mode.md	grep -n "^## Phase C"
Agent prompts	agent-templates.md	grep -n "^## "
Progress/recovery	progress-recovery.md	—
Report template	report_template.md	—
Query generation	query-framework.md	QUEST Matrix
Perspective audit	perspective-checklist.md	COMPASS Checklist
Researcher thinking	researcher-thinking.md	THINK Protocol

Script	Purpose
scripts/validate_report.py	9-check quality validation

Output File (MANDATORY)

After completing research, ALWAYS save to markdown file:

research/[topic-slug]-[YYYY-MM-DD].md

Example: research/china-opensource-ai-2025-01-04.md

• Create research/ folder if it doesn't exist
• Why: Research takes effort. Save it for future reference.

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Related Skills

• /boost-intel — Apply critical thinking to research findings
• /generate-creative-ideas — Creative Mode for cross-industry innovation
• /skill-creator-thepexcel — Research domain expertise for skill creation
• /extract-expertise — Research to prepare expert interviews