create-research-brief

name	create-research-brief
version	2.0
description	Two-phase research design and consolidation skill for multi-LLM optimized research
triggers	["create research brief","design research strategy","decompose research question","multi-model research","consolidate research findings","research synthesis"]

Create Research Brief

A comprehensive two-phase skill for designing multi-LLM research strategies (Phase 1) and consolidating multi-model outputs into actionable intelligence (Phase 2).

---

1. Purpose

This skill provides 9 core capabilities:

#	Capability	Phase	Description
1	Decompose	1	Break research questions into MECE structures
2	Assign	1	Map question categories to optimal LLMs
3	Assess	1	Evaluate research risks at appropriate depth
4	Generate	1	Produce model-specific optimized prompts
5	Consolidate	2	Synthesize multi-model outputs into unified findings
6	Resolve	2	Handle conflicting information with WWHTBT protocol
7	Classify	2	Score evidence quality and tag uncertainty types
8	Detect	2	Identify coverage gaps and unknown unknowns
9	Produce	2	Generate tiered, decision-ready research reports

---

Checkpoints

This skill uses interactive checkpoints (see references/checkpoints.yaml) to resolve ambiguity:

• research_type_classification — When research type is ambiguous
• risk_depth_selection — When risk assessment depth not specified
• model_mode_selection — When model execution mode not specified
• hypothesis_priors_required — When multi_hypothesis enabled but priors missing
• conflict_resolution_approach — When model outputs have significant conflicts (Phase 2)

---

2. Two-Phase Workflow

Phase 1: Research Design (Before Research)

Step	Action	Output
1	Validate Objective	Confirm research question is answerable
2	Classify Research Type	market | competitive | technology | strategic
	CHECKPOINT: research_type_classification	If type ambiguous: AskUserQuestion
3	Define Scope	In-scope, out-of-scope, boundaries
4	Select MECE Pattern	5-category decomposition structure
5	Generate Sub-Questions	3-4 questions per category
6	Assess Risks	Quick | Standard | Comprehensive
	CHECKPOINT: risk_depth_selection	If depth not specified: AskUserQuestion
7	Assign Models	Map categories to Claude/Gemini/GPT
	CHECKPOINT: model_mode_selection	If mode not specified: AskUserQuestion
8	Frame Hypotheses	If multi_hypothesis=true
	CHECKPOINT: hypothesis_priors_required	If priors missing: AskUserQuestion
9	Recommend Expert Panel	If expert_panel=true
10	Produce Research Brief	XML-structured Phase 1 deliverable

Phase 2: Consolidation (After Research)

Step	Action	Output
1	Ingest Model Outputs	Parse all LLM research results
2	Score Evidence	Apply 5-point Evidence Strength Rubric
3	Detect Conflicts	Identify where models disagree
4	Resolve Conflicts	Apply WWHTBT for unresolved
5	Classify Uncertainty	Tag as epistemic/aleatory/model
6	Audit MECE Coverage	Check for coverage gaps
7	Probe Unknown Unknowns	Run 5 discovery probes
8	Tier Findings	Assign to Tier 1/2/3 by confidence
9	Build Decision Support	Create if-then decision tree
10	Define Kill Criteria	Conditions that invalidate research
11	Produce Report	XML-structured Phase 2 deliverable

---

3. Parameters

Parameter	Type	Default	Description
research_objective	string	required	The core research question or goal
research_type	enum	market	market | competitive | technology | strategic
model_mode	enum	parallel	parallel | sequential | convergent
openai_depth	enum	balanced	minimal | balanced | exhaustive
risk_depth	enum	standard	quick | standard | comprehensive
multi_hypothesis	bool	false	Enable hypothesis-driven framing
expert_panel	bool	false	Include expert panel recommendations
context	string	""	Additional context for research

---

4. Model Strengths & Assignment

Model Profiles

Model	Primary Strength	Best For	Limitation
Claude Opus 4.5	Judgment, synthesis, nuance	Strategic questions, conflict resolution, synthesis	May not surface all sources
Gemini Pro 3	Breadth, citations, grounding	Factual lookup, comprehensive sourcing, current data	Less depth on complex reasoning
GPT-5.2 Deep	Recency, depth, exhaustiveness	Technical details, narrow deep-dives, edge cases	Can miss broader context

Default Category Assignments

Research Type	Claude	Gemini	GPT
Market	Demand, Trends	Size, Structure, Supply	—
Competitive	Positioning, Strategy	Product, GTM, Org	Deep Dive
Technology	Fit, Risk	Maturity, Cost	Capability
Strategic	Options, Stakeholders	Environment	Implementation

---

5. Risk Assessment Depths

Quick (5 Factors)

Basic risk identification for time-sensitive research:

• Top 3 risks with likelihood/impact
• No mitigations or scenarios

Standard (+ Bias Audit)

Adds mitigation planning and cognitive bias check:

• Mitigations and contingencies per risk
• Early warning signals
• Bias audit: confirmation, availability, anchoring

Comprehensive (+ Base Rates)

Full risk analysis with historical grounding:

• Risk scenarios with trigger conditions
• Risk dependencies and cascades
• Base rate comparison from similar research
• Pre-mortem analysis

---

6. MECE Decomposition Patterns

Pattern 1: Market Research

Category	Focus	Model
Market Size & Dynamics	TAM/SAM/SOM, growth rates	Gemini
Market Structure	Segmentation, value chain	Gemini
Demand Characteristics	Buyers, use cases, criteria	Claude
Supply & Competition	Players, barriers, substitutes	Gemini
Market Evolution	Trends, regulatory, disruption	Claude

Pattern 2: Competitive Intelligence

Category	Focus	Model
Product & Offering	Features, pricing, roadmap	GPT
Customers & Positioning	Segments, win/loss, messaging	Claude
Go-to-Market	Sales, marketing, partnerships	Gemini
Organization & Operations	Team, tech stack, cost structure	Gemini
Strategy & Trajectory	Direction, investments, SWOT	Claude

Pattern 3: Technology Evaluation

Category	Focus	Model
Capability & Performance	Features, benchmarks, limits	GPT
Maturity & Ecosystem	Stability, community, tools	Gemini
Fit & Integration	Use case alignment, migration	Claude
Cost & Investment	TCO, licensing, infrastructure	Gemini
Risk & Governance	Technical, vendor, compliance	Claude

Pattern 4: Strategic Research

Category	Focus	Model
Current State	Position, strengths, weaknesses	Claude
External Environment	Industry, macro, technology	Gemini
Strategic Options	Directions, trade-offs, requirements	Claude
Stakeholder Considerations	Customer, competitor, employee	Claude
Implementation Requirements	Capabilities, investments, timeline	GPT

---

7. Multi-Hypothesis Framing

When to Enable

• Testing predictions or forecasts
• Evaluating competing theories
• Decision involves binary or multi-way choice
• Need to avoid confirmation bias

Process

1. Define core question as testable prediction
2. Generate 2-4 MECE hypotheses covering all outcomes
3. Assign prior probabilities (must sum to 100%)
4. Define supporting and refuting evidence for each
5. Research gathers evidence against criteria
6. Update posteriors based on evidence strength

Example

<hypotheses question="Will enterprise adopt GenAI for customer service by 2027?">
  <hypothesis id="H1" position="broad" prior="30%">
    >50% enterprise adoption
  </hypothesis>
  <hypothesis id="H2" position="selective" prior="50%">
    10-50% adoption in specific use cases
  </hypothesis>
  <hypothesis id="H3" position="limited" prior="20%">
    <10% adoption due to barriers
  </hypothesis>
</hypotheses>

---

8. Evidence Strength Tribunal

5-point scale for evaluating source quality:

Score	Name	Definition	Examples
5	Primary	Direct from entity being researched	SEC filings, earnings calls, official docs
4	Auth. Secondary	Major analysts with citations	Gartner, Forrester, WSJ investigative
3	Credible Secondary	Reputable sources, some sourcing	TechCrunch, industry publications
2	Weak Secondary	Unsourced, outdated, anonymous	LinkedIn self-reports, old reports
1	Speculative	No verifiable basis	Rumors, predictions, fabrications

Time Decay: Apply -1 for technology data >6 months, market data >1 year.

Reference: See references/evidence-strength-rubric.md for full scoring guidelines.

---

9. Conflict Resolution: WWHTBT

When models or sources disagree and resolution isn't clear, apply What Would Have To Be True analysis:

<conflict claim="Market size for X">
  <position holder="Gartner" value="$50B">
    <evidence score="4">2024 market report with methodology</evidence>
  </position>
  <position holder="IDC" value="$35B">
    <evidence score="4">Different scope definition</evidence>
  </position>

  <wwhtbt>
    <for_gartner>
      <condition>Adjacent markets included in scope</condition>
      <condition>Projected vs. realized revenue counted</condition>
    </for_gartner>
    <for_idc>
      <condition>Only core product category</condition>
      <condition>Realized revenue only</condition>
    </for_idc>
  </>

  
    Report range ($35-50B) with scope dependency noted.
    For our purposes, IDC definition more aligned.
  

---

10. Uncertainty Decomposition

Type	Definition	Can Reduce?	Action
Epistemic	Knowledge gaps that COULD be closed	YES	Research further
Aleatory	Inherent randomness that CANNOT be predicted	NO	Quantify range, build scenarios
Model	Framework/definition dependencies	DEPENDS	Make choices explicit

Classification Questions

• Epistemic: "Does someone, somewhere know this?"
• Aleatory: "Even with perfect info, would this still be uncertain?"
• Model: "Would a different definition change the answer?"

Reference: See references/uncertainty-taxonomy.md for full classification protocol.

---

11. Gap Analysis

Part 1: MECE Coverage Audit

Compare findings against expected coverage matrix for research type. Flag:

• Critical gaps: Core dimensions missing or Score ≤2
• Significant gaps: Supporting dimensions weak
• Minor gaps: Context items missing

Part 2: Unknown Unknowns Probes

Probe	Question
Adjacent Domain	What lessons from related industries apply?
Stakeholder Blind Spot	Whose voice is missing from sources?
Time Horizon	What historical precedents or future implications are ignored?
Failure Mode	What would have to be true for conclusions to be wrong?
Second-Order Effects	If findings are true, what else must follow?

Reference: See references/gap-analysis-protocol.md for full audit process.

---

12. Output Specifications

Phase 1 Deliverable: Research Brief

research-brief.xml
├── Header (ID, type, mode, parameters)
├── Section 1: Research Classification
├── Section 2: MECE Question Decomposition
├── Section 3: Multi-Hypothesis Framing (if enabled)
├── Section 4: Risk Assessment
├── Section 5: Expert Panel (if enabled)
├── Section 6: Model Role Assignments
├── Section 7: Ready-to-Execute Prompts
├── Section 8: Consolidation Strategy
├── Section 9: Verification Priorities
└── Section 10: Effort Estimates

Phase 2 Deliverable: Consolidated Report

consolidated-report.xml
├── Header (quality summary)
├── Part 1: Executive Summary (≤5 findings, bottom line)
├── Part 2: Tiered Findings (1: >75%, 2: 50-75%, 3: <50%)
├── Part 3: Evidence Quality Assessment
├── Part 4: Contested Claims & Conflict Resolution
├── Part 5: Uncertainty Analysis
├── Part 6: Gap Analysis
├── Part 7: Model Contribution Analysis
├── Part 8: Decision Support (if-then tree)
├── Part 9: Kill Criteria
├── Part 10: Methodology Transparency
├── Part 11: Appendices
└── CRITICAL CONSTRAINTS (at end for context retention)

Templates: See templates/research-brief-template.md and templates/consolidated-report-template.md

---

13. Expert Panel Integration

When to Enable

• High-stakes decisions
• Multi-disciplinary topics
• Need for challenge/red-teaming
• Regulatory or compliance implications

Process

1. Identify panel size (3-8 experts) and balance
2. Select domain-appropriate experts
3. Define deliberation format (round-robin, debate, Delphi)
4. Assign challenger role for assumption testing
5. Synthesize panel perspectives into findings

Expert Selection by Domain

Domain	Recommended Experts
Market	Market analyst, Customer representative, Industry veteran
Competitive	Competitive intel analyst, Former competitor employee, Sales leader
Technology	Technical architect, Security specialist, Operations lead
Strategic	Strategy consultant, Board member, Industry analyst

---

14. Quality Gates

Phase 1 Gates (Research Design)

#	Gate	Criterion
1	Objective Clarity	Single, answerable research question
2	MECE Validity	Categories non-overlapping and exhaustive
3	Question Quality	All sub-questions researchable
4	Model Fit	Assignments match model strengths
5	Prompt Executability	Prompts can run without modification
6	Completeness	All required sections populated

Phase 2 Gates (Consolidation)

#	Gate	Criterion
1	Evidence Scored	All findings have evidence scores
2	Conflicts Surfaced	No hidden disagreements
3	Uncertainty Classified	All gaps tagged by type
4	Coverage Audited	MECE matrix reviewed
5	Probes Executed	≥3 of 5 unknown-unknowns probes run
6	Tiers Justified	Confidence matches evidence profile
7	Decision Support	Actionable if-then structure
8	Constraints Verified	All 7 critical constraints checked

---

15. Use Cases

Use Case	Type	Mode	Risk	Hypothesis	Panel
Market sizing	market	parallel	quick	no	no
Competitor deep-dive	competitive	sequential	standard	no	no
Build vs buy	technology	convergent	comprehensive	yes	yes
Strategic planning	strategic	parallel	comprehensive	yes	yes
Trend monitoring	market	parallel	quick	no	no
Investment due diligence	competitive	convergent	comprehensive	yes	yes

---

16. Workflow Integration

This skill integrates with the broader research workflow:

┌─────────────────────┐
│ research-interviewer│  Elicit research requirements
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│create-research-brief│  ◀── THIS SKILL (Phase 1)
│     (Phase 1)       │  Design multi-LLM research strategy
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│   Execute Research  │  Run prompts across models
│  (Manual or Agent)  │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│create-research-brief│  ◀── THIS SKILL (Phase 2)
│     (Phase 2)       │  Consolidate into report
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ consolidate-research│  Additional synthesis if needed
└─────────────────────┘

---

17. References and Templates

Reference Files

File	Purpose
references/evidence-strength-rubric.md	5-point evidence scoring with special cases
references/uncertainty-taxonomy.md	3 uncertainty types with classification protocol
references/gap-analysis-protocol.md	MECE audit + 5 unknown-unknowns probes
references/mece-decomposition-guide.md	Full decomposition patterns with examples

Template Files

File	Purpose
templates/research-brief-template.md	Phase 1 output structure (XML)
templates/consolidated-report-template.md	Phase 2 output structure (XML)

---

Quick Start

Phase 1: Create Research Brief

/create-research-brief
research_objective: "What is the market opportunity for AI legal research tools?"
research_type: market
risk_depth: standard

Phase 2: Consolidate Research

/create-research-brief --phase=2
input: [model outputs from Phase 1 execution]