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

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

Phase 2: Consolidation (after user executes prompts)

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

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

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)

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:

Environmental Context -- Runtime, config, auth, secrets, project structure
Idiomatic Patterns -- How practitioners write it vs. how it merely works
Anti-Patterns & Guardrails -- What fails at scale, root cause reasoning
Testing & Validation -- Emulators, mocks, integration tests, gaps
Dependencies & Versions -- Compatibility matrices, packages to avoid
Operational Awareness -- Cost drivers, scaling, cold starts, monitoring
Decision Trees -- When to use which approach, architectural boundaries
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.

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:

Research objective: What specific question(s) do you want answered?

Research pattern (optional): Landscape / Comparative / Implementation / Best Practices / Competitive / Market / User / Economic / Compliance?

Context (optional): Background, constraints, or intended use?

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

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

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.

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

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."

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.

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

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

create-research-brief

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