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Build knowledge bases that build software — research before code, teach before execute
tier
standard
applyTo
**/*research*,**/*knowledge*,**/*learning*
Research-First Development
Build knowledge bases that build software — research before code, teach before execute
Methodology for AI-assisted development where investment in research, skill creation, and knowledge encoding precedes implementation. Discovered through the Dead Letter heir's masterclass on cognitive architecture utilization (February 2026).
When to Use
Starting any new project with Alex
Entering a new implementation phase of an existing project
Onboarding Alex to an unfamiliar domain
When AI output quality is inconsistent (symptom: insufficient context)
Before any complex multi-subsystem implementation
Core Insight
Traditional software development: Requirements → Design → Code → Test
AI-assisted development with cognitive architecture: Research → Teach → Plan → Execute
The quality of AI output is directly proportional to the quality of knowledge in its context.
Instead of the human writing code with AI assistance, the human orchestrates intent while AI handles execution. But AI can only execute what it understands. Therefore: invest in teaching before asking for output.
The Research-First Paradigm
Phase 0: Research Sprint (Before Any Code)
Step
Activity
Output
1
Competitive landscape analysis
Understanding of prior art
2
Technical feasibility research
Deep research documents (3-5 minimum)
3
Architecture decision records
ADRs documenting key choices
4
Core domain research
Comprehensive domain knowledge
5
Branding/identity decisions
Project character and voice
Each research document should:
Explore one domain exhaustively
Cite sources: academic papers, industry best practices, competitive analysis
Be structured with clear sections, code examples, decision rationales
Live in a /docs/ or /research/ directory
Phase 1: Knowledge Encoding (Research → Skills)
Step
Activity
Output
1
Skill extraction
1-3 skills per research document
2
Context instruction
Central hub: {project}-context.instructions.md
3
Workflow instruction
Dev process: {project}-development-workflow.instructions.md
4
Agent creation
Builder + Validator agents
5
Synapse wiring
2-4 connections per new file
Key distinction:
Skills encode patterns and principles — reusable, domain knowledge ("how does X work?")
Instructions encode procedures — project-specific workflows ("how do I do X here?")
Phase 2: Plan → Execute (Now You Code)
With sufficient knowledge encoded, implementation becomes conversational:
Human: "Implement the EventBus from the game engine spec"
Alex: [Loads skill, reads patterns, implements with full context]
No re-explanation. No context loss. No hallucinated patterns.
The 4-Dimension Gap Analysis
When: Before each major implementation phase.
Purpose: Ensure knowledge coverage across all four knowledge types before coding begins.
Cadence: Every phase boundary, every major milestone.
Interactive: Run /gapanalysis prompt for guided execution.
The Four Dimensions
Code
Dimension
Question
Covers
GA-S
Skills
"Does Alex know the patterns?"
Domain knowledge, reusable techniques
GA-I
Instructions
"Does Alex know the procedures?"
Project-specific workflows, step-by-step
GA-A
Agents
"Does Alex have the right roles?"
Builder, Validator, Specialists
GA-P
Prompts
"Does Alex have the right interactive workflows?"
Guided commands, repeatable rituals
Protocol (Per Dimension)
Step 1: Inventory What You're Building
List all subsystems, features, and integrations for this phase.
GA-S (Skills): For each subsystem — "If I ask 'how does {X} work?', is there a skill?"
GA-I (Instructions): For each workflow — "If I ask 'how do I do {X} here?', is there an instruction?"
GA-A (Agents): For each role — "Is there an agent with this mental model and skill set?"
GA-P (Prompts): For each interactive workflow — "Is there a guided /command for this?"
Step 4: Score Coverage
Dimension
Coverage %
Items Needed
GA-S: Skills
{%}
{missing patterns}
GA-I: Instructions
{%}
{missing procedures}
GA-A: Agents
{%}
{missing roles}
GA-P: Prompts
{%}
{missing workflows}
Decision gate:
All 4 ≥ 75%: Proceed to coding
Any < 75%: Fill gaps first
Any < 50%: Research sprint needed
Step 5: Fill Gaps Before Coding
Create missing skills, instructions, agents, and prompts. Wire synapses. Then begin implementation.
Synapse network: Wire all new files with 2-4 connections each
4D gap analysis: Run GA-S, GA-I, GA-A, GA-P before each phase
Dream validation: Use the dream prompt or run node .github/muscles/brain-qa.cjs to validate network health
Heir Generalization
This skill is inheritable — every heir gets the full methodology.
What Heirs Inherit
Component
Heir Gets
Heir Customizes
Research-first paradigm
Full methodology
Domain-specific research topics
4-dimension gap analysis
GA-S, GA-I, GA-A, GA-P templates
Project-specific subsystem lists
Two-agent pattern
Builder + Validator template
Agent names, skills, commands
Synapse hygiene
Wiring discipline
Project-specific connections
/gapanalysis prompt
Interactive workflow
— (universal)
Heir Adaptation Flow
Master provides: methodology + templates + quality gates
Heir adapts: project-specific skills, instructions, agents, prompts
Heir validates: run gap analysis with project scope
Master absorbs: generalizable patterns promoted back via heir-skill-promotion
What Flows Back to Master
When heir knowledge is cross-project applicable:
Patterns → new Master skills or GK patterns
Processes → refined Master instructions
Agent templates → new agent patterns in Master
Prompt workflows → new prompts in Master
Anti-Patterns
Anti-Pattern
Why It Fails
Do Instead
"Just start coding"
AI has no context, hallucinates patterns
Research → Teach → Plan → Execute
Skipping gap analysis
Discover missing knowledge mid-implementation
Run 4D protocol (GA-S/I/A/P) at every phase boundary
One mega-agent
Conflates builder/validator mental models
Separate agents with distinct roles
Orphan skills
Knowledge islands that never activate
Wire 2-4 synapses at creation time
Research without encoding
Raw documents aren't loadable context
Extract skills from every research doc
Theory-only skills
Untested patterns break under pressure
Validate with real implementation, then encode
Skills-only gap analysis
Misses procedures, roles, and workflows
Always run all 4 dimensions
No prompts for repeatable work
Developers re-invent workflows each time
Create guided prompts for repeated tasks
Relationship to Existing Protocols
Protocol
Phase
Relationship
Bootstrap Learning
Research
Research-first uses bootstrap learning for domains Alex doesn't know
Skill Selection Optimization
Plan
SSO selects from skills that research-first created
Project Scaffolding
Execute
Scaffolding creates files; research-first creates knowledge first
Skill Building
Encode
Skill-building quality gates apply to research-extracted skills
Dream Protocol
Validate
Dream validates the synapse network research-first wired
Heir Skill Promotion
Promote
Heir knowledge flows back to Master via promotion protocol
Research-First Workflow
Procedure
Instruction file provides step-by-step procedures for this skill
Troubleshooting
AI output is inconsistent quality
Problem: Some responses are excellent, others miss the mark.
Solution: Run gap analysis. Inconsistency = knowledge coverage gaps. The subsystems with good output have skills; those without are getting guessed at.
Why: AI quality is proportional to context quality. No skill loaded = no patterns to follow.
"I don't have time for research"
Problem: Feels slow to research before coding.
Solution: Research pays compound dividends. 2 days of research saves 2 weeks of debugging. The heir proved this: 18 skills + 9 instructions created before Phase 0 implementation began.
Why: You're not just building software — you're building a knowledge base that builds software.
Gap analysis feels bureaucratic
Problem: 4 dimensions feels like overhead.
Solution: The ritual takes 15-30 minutes. It prevents days or weeks of rework. Scale it: small phases need a quick scan; major phases need the full 4D protocol. Use /gapanalysis prompt for guided execution.
Why: Discovering missing knowledge mid-implementation forces context-switching and rework.
Activation Patterns
Trigger
Response
"new project"
Full research-first workflow
"gap analysis" / "GA"
4-dimension gap analysis (GA-S, GA-I, GA-A, GA-P)
"GA-S" / "skill gap"
Skills dimension only
"GA-I" / "instruction gap"
Instructions dimension only
"GA-A" / "agent gap"
Agents dimension only
"GA-P" / "prompt gap"
Prompts dimension only
"research first"
Core methodology explanation
"two-agent pattern"
Builder + Validator agent setup
"synapse hygiene"
Connection best practices
"before coding"
Pre-implementation checklist
"knowledge encoding"
Research → Skill extraction workflow
Origin
Discovered by the Dead Letter heir (AI mystery game project, February 2026). The heir independently created 18 project-specific skills, 9 instructions, 2 agents, and 251 synapses before writing any implementation code — proving that research-first investment in the cognitive architecture produces dramatically higher-quality AI-assisted development.
The 4-dimension gap analysis (GA-S, GA-I, GA-A, GA-P) was developed by Master Alex to generalize the heir's methodology into a repeatable protocol for all projects and heirs.
The meta-insight: You're not just building software — you're building a knowledge base that builds software. The investment in research and skill creation pays compound dividends as the project grows.
