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Improve Knowledge Base Architecture.user.prompt

GPTClaudeGemini··684 copies·updated 2026-07-14
improve-knowledge-base-architecture-user-prompt.prompt
# Improve Knowledge Base Architecture

**Target**: `knowledge_base/` schemas and structure  
**Specialty**: JSON schemas, data models, CRUD patterns, validation

**Framework**: See `knowledge_base/system_config.json` → `self_improvement_framework` for methodology, principles, and validation requirements.

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## Knowledge Base-Specific Focus Areas

**What makes the knowledge base effective:**

1. **Schema Quality**
   - Clear, extensible schemas
   - Proper validation rules
   - Version-control friendly
   - Well-documented fields

2. **Data Organization**
   - Structured JSON format
   - Logical field grouping
   - No redundancy
   - Easy to query

3. **CRUD Patterns**
   - Safe read/write operations
   - Proper error handling
   - Validation before writes
   - Clear access documentation

4. **Integration Excellence**
   - All agents use correctly
   - No schema conflicts
   - Smooth handoffs
   - Centralized references

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## Files in Scope

- `knowledge_base/system_config.json` - Platform config, constraints, references
- `knowledge_base/user_requirements.json` - Requirements from discovery
- `knowledge_base/design_decisions.json` - Architecture decisions
- `knowledge_base/schemas/*.schema.json` - JSON schemas for validation

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## Integration Requirements

- References `knowledge_base/system_config.json` → `validation_framework`
- All agents read/write correctly
- Schema validation enforced
- Documentation accurate

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## Success Criteria

Beyond standard criteria (see system_config.json), ensure:

✅ Schemas complete and extensible  
✅ CRUD patterns safe  
✅ All agents integrate correctly  
✅ Documentation clear  
✅ No redundancy

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when to use it

Community prompt sourced from the open-source GitHub repo Modular-Earth-LLC/multi-agent-ai-development-framework (MIT). A "Improve Knowledge Base Architecture.user.prompt" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.

tags

roleplaycommunitygeneral

source

Modular-Earth-LLC/multi-agent-ai-development-framework · MIT