create-research-brief

Create Research Brief

Design multi-model research strategies for Claude Opus 4.6, Gemini 3.1 Pro Deep Research, and optionally GPT-5.2 Deep Research (site-restricted) or GPT-5.2 Chat across 9 research patterns.

Workflow

Phase 1: Research Design

1. Detect input type -- Direct request OR Research Request Specification (from research-interviewer)
2. Validate input -- Clarify if vague (skip if Specification present)
3. Detect model mode -- DUAL (default) / FULL / SINGLE
4. Detect OpenAI depth -- Deep Research (default for FULL) or Chat
5. Classify research pattern -- Use decision tree from references/pattern-registry.md to select one of 9 patterns
6. If Best Practices -- Load references/best-practices-dimensions.md and references/technology-profiles.md for 8-dimension framework
7. Assess topic risks -- Recency, contestation, source availability, false confidence, coverage gaps
8. Plan context budget -- Estimate combined output size, select context tier
9. Assign model roles -- Load Pattern x Model Configuration Matrix from references/model-profiles.md; assign roles, capabilities, thinking tiers, effort levels per pattern
10. Configure GPT-5.2 site restrictions -- If FULL mode, load site restriction library from references/model-profiles.md Section 6 and customize for topic
11. Configure Gemini file_search -- If user has relevant documents, load file search guidance from references/model-profiles.md Section 7 and recommend uploads
12. Design consolidation strategy -- Select pattern-default mode from Pattern Registry, allow user override
13. Generate prompts -- Use templates from references/model-profiles.md Section 5; for Best Practices, assemble dimension-specific fragments from references/best-practices-dimensions.md
14. Generate consolidation manifest -- Produce YAML manifest block per references/consolidation-manifest-schema.md
15. Produce brief -- Use output template from references/output-templates.md
16. Include merge prompt -- Append merge prompt referencing the manifest from references/output-templates.md

Phase 2: Consolidation (after user executes prompts)

17. Receive outputs -- User pastes/attaches research results
18. Consolidate -- Follow workflow in references/consolidation.md

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Model Overview

Model	Role	Key Capabilities
Claude Opus 4.6	Primary Researcher	84% BrowseComp, 68.8% ARC-AGI-2, 1M context, 128K output, self-correction, cross-domain synthesis
Gemini 3.1 Pro Deep	Structured Cataloger	85.9% BrowseComp, 77.1% ARC-AGI-2, 1M context, 64K output, file_search (uploaded docs as sources), autonomous 5-30 min agent
GPT-5.2 Deep Research	Targeted Investigator	Site-restricted search (unique), leading MRCR v2 at 128-256K, 5-30 min autonomous agent, mid-session intervention
GPT-5.2 Chat	Recency Validator	Quick recent developments, community signals

Capability uniqueness: Claude = self-correction + cross-domain synthesis. Gemini = file_search. GPT-5.2 Deep = site-restricted search. Web research is a shared capability (Claude 84%, Gemini 85.9%); differentiate by approach, not access.

For detailed profiles, prompt templates, role assignment, and per-pattern configuration: references/model-profiles.md

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Mode Detection

Model Mode

User Signal	Mode
No model specification	DUAL (default)
"all three models" / "full research" / "include OpenAI" / "comprehensive"	FULL
"without OpenAI" / "skip OpenAI" / "Claude and Gemini only"	DUAL (confirm)
"dual model" / "two model"	DUAL
"Claude only" / "quick research" / "fast analysis"	SINGLE

OpenAI Depth (FULL mode only)

GPT-5.2 Deep Research is the default for FULL mode. Site-restricted search is its unique differentiator -- use Chat only when explicitly downgraded.

User Signal	Depth
No specification	Deep Research (default)
"quick OpenAI" / "lite" / "lightweight" / "skip deep research"	Chat
"OpenAI deep research" / "comprehensive OpenAI" / "site-restricted"	Deep Research (confirm)

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Research Pattern Classification

Use the decision tree in references/pattern-registry.md to classify. Summary table:

Pattern	Trigger Signals	Primary Deliverable	Default Consolidation
Landscape Mapping	"map the landscape", "who's out there", "ecosystem overview"	Taxonomy + player inventory + white space map	breadth_first
Comparative Evaluation	"compare X vs Y", "which should I choose", "trade-offs"	Weighted decision matrix + sensitivity analysis	confidence_weighted
Implementation Pattern	"how do I implement", "architecture patterns", "reference architecture"	Architecture decision catalog + pattern catalog + anti-pattern register	depth_first
Best Practices	"best practices for", "idiomatic patterns", "gotchas", "anti-patterns"	8-dimension technology knowledge base	gap_driven
Competitive Intelligence	"competitive moat", "positioning analysis", "competitive dynamics"	Per-competitor strategic profile + dynamics analysis	adversarial
Market Research	"market size", "TAM", "segments", "entry strategy"	TAM/SAM/SOM + segmentation framework + dynamics	standard
User Research	"user needs", "JTBD", "persona", "pain points"	Persona profiles + JTBD map + ranked unmet needs	depth_first
Economic Analysis	"ROI", "TCO", "cost-benefit", "business case"	Financial model + sensitivity analysis + benchmarks	confidence_weighted
Compliance & Requirements	"compliance", "regulatory", "GDPR", "audit requirements"	Requirements register + constraint map + governance	gap_driven

Best Practices Pattern

When pattern = Best Practices, activate the 8-dimension framework:

1. Environmental Context -- Runtime, config, auth, secrets, project structure
2. Idiomatic Patterns -- How practitioners write it vs. how it merely works
3. Anti-Patterns & Guardrails -- What fails at scale, root cause reasoning
4. Testing & Validation -- Emulators, mocks, integration tests, gaps
5. Dependencies & Versions -- Compatibility matrices, packages to avoid
6. Operational Awareness -- Cost drivers, scaling, cold starts, monitoring
7. Decision Trees -- When to use which approach, architectural boundaries
8. Escape Hatches -- Known bugs, workarounds with expiration, when to eject

Weight dimensions by technology family from references/technology-profiles.md. Assemble prompts using dimension fragments from references/best-practices-dimensions.md.

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Input Detection

Research Request Specification (from research-interviewer)

Detect by: YAML block with research_request: root containing objective:, questions:, scope:, constraints:.

When detected: Skip validation. Extract objective, questions.primary/secondary, scope.in_scope/out_of_scope, constraints.model_mode, constraints.depth_vs_breadth, context.prior_knowledge, metadata.suggested_research_type (verify, don't blindly accept).

Direct Request Validation

Required: Research objective specific enough to derive key questions.

Optional (with defaults): Research pattern (infer), context (none), timeline (standard), output use (general decision support), model mode (DUAL), OpenAI depth (Deep Research).

When insufficient and no Specification present:

To design an effective research strategy, I need:

1. Research objective: What specific question(s) do you want answered?
2. Research pattern (optional): Landscape / Comparative / Implementation / Best Practices / Competitive / Market / User / Economic / Compliance?
3. Context (optional): Background, constraints, or intended use?

Alternatively: Say "interview me about [topic]" to clarify your needs first.

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Risk Assessment

Rate each factor High / Medium / Low:

Risk Factor	Assessment Criteria	Design Impact
Recency sensitivity	How quickly does info change?	DUAL has reduced concern (Claude 84% + Gemini 85.9% BrowseComp). Flag only for live events, fast-moving regulation, or topics needing site-restricted precision (GPT-5.2).
Contestation level	Genuine disagreement?	May need adversarial consolidation
Source availability	Well-documented or sparse?	Affects coverage expectations
False confidence risk	Shared in LLM training?	Requires cross-validation
Coverage gap risk	Emerging/niche topic?	May need multiple search passes

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Context Budget Planning

Output Estimates by Model

Model	Standard Output	Complex Output	Maximum
Claude Opus 4.6	15-40K tokens	40-80K tokens	128K tokens
Gemini 3.1 Pro	30-60K tokens	60-80K tokens	64K tokens
GPT-5.2 Deep Research	30-60K tokens	60-80K tokens	~80K tokens
GPT-5.2 Chat	2-5K tokens	5-10K tokens	~15K tokens

Combined Budget by Mode

Mode	Expected Combined	Tier	Strategy
SINGLE	15-80K tokens	Standard	Single output, no consolidation needed
DUAL	50-160K tokens	Standard	Both outputs unabridged, single consolidation pass
FULL	100-240K tokens	Standard or Extended	All outputs unabridged
FULL (complex)	150-300K+ tokens	Extended (beta)	Use 1M context, all unabridged

Principle: Always prefer full unabridged outputs. Opus 4.6's 76% MRCR v2 long-context retrieval can attend throughout the window.

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Consolidation Modes

Mode	When to Use
Standard	Moderate stakes, stable topics, quantitative findings reconcilable across sources
Adversarial	High stakes, contested topics, outputs seem too aligned, confirmation bias risk
Gap-Driven	Comprehensive requirements, explicit coverage checklists (e.g., 8-dimension BP framework)
Confidence-Weighted	Executive-facing, evidence quality paramount, selection decisions
Depth-First	Strategic decisions, insight > coverage, behavioral reasoning
Breadth-First	Landscape mapping, unfamiliar domains, completeness > depth
Agentic	All models completed, topic well-bounded, minimal human intervention needed

Pattern-Default Mapping

Pattern	Default Mode	Override Trigger
landscape_mapping	breadth_first	User says "deep-dive on key players" -- depth_first
comparative_evaluation	confidence_weighted	User says "quick comparison" -- standard
implementation_pattern	depth_first	User says "comprehensive pattern catalog" -- breadth_first
best_practices	gap_driven	User says "focus on anti-patterns only" -- depth_first
competitive_intelligence	adversarial	User says "just the facts" -- standard
market_research	standard	User says "high-stakes investment decision" -- confidence_weighted
user_research	depth_first	User says "broad needs survey" -- breadth_first
economic_analysis	confidence_weighted	User says "rough estimate is fine" -- standard
compliance_requirements	gap_driven	User says "focus on highest-risk areas" -- depth_first

For mode details and full consolidation workflow: references/consolidation.md

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Effort & Thinking Directives

Model	Directive	Values	Research Default	Consolidation Default
Claude Opus 4.6	effort	low / medium / high / max	max	max
Gemini 3.1 Pro	thinking	Low / Medium / High	High	--
GPT-5.2 Deep Research	thinking_effort	low / medium / high / extended	extended	--
GPT-5.2 Chat	mode	Instant / Thinking	Instant	--

Include in Claude Opus 4.6 prompts:

# Claude API Configuration
model: "claude-opus-4-6"
thinking:
  type: "adaptive"
effort: "max"
max_tokens: 16000  # Up to 128000 for comprehensive output

If over-thinking detected: add "Use deep reasoning for strategic analysis; move efficiently through factual compilation."

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Consolidation Manifest

Every research brief must include a consolidation manifest YAML block generated per references/consolidation-manifest-schema.md. The manifest travels from design through execution to consolidation.

Required fields: research_id, pattern, topic, objective, model_mode, created_at, models[] (per model: model_id, role, capabilities), pattern_metadata, coverage_matrix, consolidation (recommended_mode, pattern_default_mode, verification_priorities), research_chain, freshness (topic_volatility, confidence_half_life, staleness_indicators, recommended_refresh).

If this research follows prior research, populate research_chain.upstream_id with the previous manifest's research_id, research_chain.upstream_pattern, and research_chain.inherited_constraints.

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Quality Gates

Before completing research brief:

• Input type detected (direct OR Specification)
• If Specification: fields extracted, interview_confidence noted
• If direct: objective is specific and actionable
• Model mode correctly detected (default: DUAL)
• Research pattern correctly classified (9 patterns, using decision tree)
• If Best Practices: 8 dimensions assessed, technology family identified, dimension weights applied
• Risk assessment completed (5 factors)
• Context budget estimated, tier selected
• Model roles assigned per Pattern x Model Configuration Matrix
• Claude prompt includes: effort directive, web search activation, cross-domain synthesis, self-review mandate
• Gemini prompt includes: thinking tier, structured data directives, citation requirements, table/matrix requests
• If FULL: GPT-5.2 site restrictions generated and customized for topic
• Gemini file_search recommendations included (if applicable)
• Consolidation mode matches pattern default (or documented override)
• Consolidation manifest generated with all required fields
• If sequential research: manifest includes research chain predecessors
• Freshness model populated (topic_volatility, confidence_half_life, staleness_indicators, recommended_refresh)
• Coverage matrix covers all key questions
• If from research-interviewer: prior_knowledge included in prompt context
• Merge prompt included at end of brief

Before completing consolidation:

• All outputs processed (full, unabridged if within budget)
• Claims triaged by type with provenance tags
• Disagreements resolved per protocol
• False confidence audit applied to unanimous high-risk claims
• Confidence tiers assigned (Tier 1 >75%, Tier 2 50-75%, Tier 3 <50%)
• Self-review pass completed
• Cross-domain synthesis section populated
• Gaps documented with recommended actions
• Effort level set to "max"
• Context tier documented in methodology

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References

File	Load When	Contents
references/model-profiles.md	Generating prompts, assigning roles, configuring site restrictions, file_search guidance	Model capabilities, role assignments, Pattern x Model Configuration Matrix, prompt templates, site restriction library, file search guidance, effort/thinking directives
references/pattern-registry.md	Classifying pattern, designing consolidation strategy, resolving compound intent	9 pattern definitions, decision tree, trigger signals, deliverables, sequencing, pattern-default consolidation mapping, interrelationship matrix
references/consolidation-manifest-schema.md	Generating the consolidation manifest	Full YAML schema with required/optional fields, validation rules, examples
references/output-templates.md	Producing the research brief	DUAL/FULL/SINGLE mode templates, pattern-specific output templates, merge prompt
references/consolidation.md	Phase 2: consolidating research outputs	Consolidation workflow, disagreement protocol, self-review, output template, consolidation prompt
references/best-practices-dimensions.md	Pattern = Best Practices	8-dimension framework, per-dimension Claude & Gemini prompt fragments, assembly instructions
references/technology-profiles.md	Pattern = Best Practices	Pre-built scope templates and dimension weighting for technology families