https://github.com/eesb99/universal-ideation-v3
Science-grounded autonomous ideation system with Triple Generator, DARLING learning, 8-dimension scoring, and NovAScore atomic novelty
https://github.com/eesb99/universal-ideation-v3
ai-ideation autonomous-agents brainstorming claude claude-code creativity innovation llm machine-learning python
Last synced: 30 days ago
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Science-grounded autonomous ideation system with Triple Generator, DARLING learning, 8-dimension scoring, and NovAScore atomic novelty
- Host: GitHub
- URL: https://github.com/eesb99/universal-ideation-v3
- Owner: eesb99
- License: mit
- Created: 2025-12-29T03:50:33.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-01-01T05:50:28.000Z (about 1 month ago)
- Last Synced: 2026-01-02T11:39:13.457Z (about 1 month ago)
- Topics: ai-ideation, autonomous-agents, brainstorming, claude, claude-code, creativity, innovation, llm, machine-learning, python
- Language: Python
- Size: 1.2 MB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# Universal Ideation v3.2
Science-grounded autonomous ideation system with self-improving mechanisms.
## Features
- **Triple Generator** - Explorer, Refiner, Contrarian modes
- **8-Dimension Scoring** - Novelty, Feasibility, Market, Complexity, Scenario, Contrarian, Surprise, Cross-Domain
- **DARLING Learning** - Diversity-aware reward calculation
- **Atomic Novelty (NovAScore)** - 0.94 accuracy claim-level novelty detection
- **Verification Gates** - Quality checkpoints
- **Reflection Learning** - Self-improving pattern extraction
- **Plateau Escape** - Avoid local optima
- **Web Search Integration** - Real-time market intelligence via Perplexity API
- **Interview Context Integration** - Enriched ideation using universal-interview skill
- **Batch Mode** - Generate multiple ideas per API call (5x faster)
## Architecture
## Installation
### As Claude Code Skill
1. Copy folder to `~/.claude/skills/universal-ideation-v3/`
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Invoke with:
```bash
/universal-ideation-v3 "your domain"
```
### Optional: Vector Search
For semantic similarity features:
```bash
pip install qdrant-client sentence-transformers
docker run -p 6333:6333 qdrant/qdrant
```
## Quick Start
### Via Skill Command
```bash
/universal-ideation-v3 "e-commerce innovation"
/universal-ideation-v3 "sustainable opportunity in Malaysia"
/universal-ideation-v3 "ai start up innovation"
/universal-ideation-v3 "sustainable packaging innovation"
```
### Via CLI (Stub Mode)
```bash
cd ~/.claude/skills/universal-ideation-v3
python3 scripts/run_v3.py "your domain" --verbose
```
### Via LLM Runner (Full Mode with Claude API)
Requires Anthropic API key in `~/.env`:
```bash
CLAUDE_API_KEY=sk-ant-xxxxx
```
Optional: Add Perplexity API key for web search:
```bash
PERPLEXITY_API_KEY=pplx-xxxxx
```
Run with full LLM integration + storage:
```bash
cd ~/.claude/skills/universal-ideation-v3
python3 scripts/llm_runner.py "your domain" -i 30 -m 30 -v
```
This mode:
- Uses Claude API for idea generation and scoring
- Stores ideas in SQLite database
- Stores embeddings in Qdrant for semantic search
- Exports full v3.2 statistics (DARLING learnings, atomic novelty, etc.)
### With Web Search (Market Intelligence)
Enable real-time market context from Perplexity:
```bash
python3 scripts/llm_runner.py "protein beverages" -i 10 -m 5 -w -v
```
This fetches:
- **Domain trends** - Current market developments
- **Market gaps** - Underserved opportunities
- **Emerging tech** - Relevant technologies
- **Consumer insights** - Behavior patterns
The market context is injected into idea generation prompts for more grounded, trend-aware ideas.
### With Interview Context (Deep Domain Understanding)
Enrich ideation with structured interview context from the universal-interview skill:
```bash
# First, run an interview to build context
cd ~/.claude/skills/universal-interview
python3 scripts/interview_runner.py "sustainable protein beverages"
# Then use the context in ideation
cd ~/.claude/skills/universal-ideation-v3
python3 scripts/llm_runner.py "protein beverages" --context-id [ID] -b -n 10 -v
# Or use interactive context selection
python3 scripts/llm_runner.py "protein beverages" -c -b -n 10 -v
```
The interview context provides:
- **Problem Space** - Pain points and user needs
- **Constraints** - Budget, timeline, regulations
- **Assumptions** - Hidden beliefs to challenge
- **Intent** - Strategic goals
- **Preferences** - What excites vs. bores
- **Existing Solutions** - Competitors and gaps
- **Resources** - Assets and capabilities
Both skills share the database at `~/.claude/data/ideation.db`.
### Batch Mode (5x Faster)
Generate multiple ideas per API call:
```bash
# Generate 100 ideas in batch mode
python3 scripts/llm_runner.py "your domain" -b -n 100 -s 10 -v
# With web search
python3 scripts/llm_runner.py "your domain" -b -n 100 -w -v
# With interview context
python3 scripts/llm_runner.py "your domain" -b -n 100 --context-id [ID] -v
```
| Mode | Ideas/min | 100 ideas |
|------|-----------|-----------|
| Standard | ~1 | ~100 min |
| Batch | ~5 | ~20 min |
## Options
### llm_runner.py (Full Mode)
| Flag | Default | Description |
|------|---------|-------------|
| `-i, --iterations` | 15 | Max iterations (standard mode) |
| `-m, --minutes` | 15 | Max duration (standard mode) |
| `-t, --threshold` | 60.0 | Acceptance score |
| `-v, --verbose` | false | Show progress |
| `-w, --web-search` | false | Enable Perplexity web search |
| `-b, --batch` | false | Enable batch mode |
| `-n, --target` | 100 | Target ideas (batch mode) |
| `-s, --batch-size` | 10 | Ideas per API call (batch mode) |
| `-c, --context` | false | Interactive interview context selection |
| `--context-id` | - | Specific interview context ID |
### run_v3.py (v3.3 - LLM Mode)
| Flag | Default | Description |
|------|---------|-------------|
| `-i, --iterations` | 30 | Max iterations |
| `-m, --minutes` | 30 | Max duration |
| `-t, --threshold` | 65.0 | Acceptance score |
| `-v, --verbose` | false | Show progress |
| `--use-llm` | false | Enable Anthropic API for real idea generation |
| `--llm-model` | claude-sonnet-4-20250514 | Model to use for generation |
| `--initiative` | - | Load enriched_domain from interview database by ID |
| `--test` | false | Stub mode for testing |
#### With Interview Integration (v3.3)
Load context directly from interview database:
```bash
# Run ideation with interview context
python3 scripts/run_v3.py --initiative e87f16de-61d0-427c-aa21-2cc7b6d3274d --use-llm -v
# Or with manual domain
python3 scripts/run_v3.py "your domain" --use-llm -v
```
The `--initiative` flag queries `~/.claude/data/ideation.db` for the enriched_domain generated by universal-interview.
## Storage
### SQLite Database
- Location: `~/.claude/data/ideation.db` (shared with universal-interview)
- Tables: ideas, sessions, learnings, initiatives, interview_responses
- Persists all accepted ideas with scores
### Qdrant Vector Database (Optional)
- Location: `localhost:6333`
- Collection: `universal_ideas` (384-dim embeddings)
- Enables semantic similarity search
Start Qdrant:
```bash
docker run -d -p 6333:6333 qdrant/qdrant
```
## Output
Results saved to:
- `output/ideation_YYYYMMDD_HHMMSS.json` - Full session export
- `data/ideation.db` - SQLite persistence
- Qdrant vectors - Semantic embeddings (if enabled)
## Backup
Backup your ideas database (SQLite + Qdrant vectors):
```bash
cd ~/.claude/skills/universal-ideation-v3
# Full backup (SQLite + Qdrant + JSON)
python3 scripts/backup.py backup
# Named backup
python3 scripts/backup.py backup -n "my_backup"
# SQLite only (skip Qdrant)
python3 scripts/backup.py backup --no-qdrant
# View statistics
python3 scripts/backup.py stats
# List all backups
python3 scripts/backup.py list
# Export all ideas to JSON
python3 scripts/backup.py export -o my_ideas.json
# Restore full backup (SQLite + Qdrant)
python3 scripts/backup.py restore backup_file.db
# Restore SQLite only
python3 scripts/backup.py restore backup_file.db --no-qdrant
```
### Backup Files
| File | Contents |
|------|----------|
| `*_backup.db` | SQLite database (ideas, sessions, learnings) |
| `*_qdrant.snapshot` | Qdrant vector embeddings |
| `*_backup.json` | Full JSON export |
Backups saved to `backups/` folder.
## Testing
```bash
python -m pytest tests/ -v
```
## Structure
```
universal-ideation-v3/
├── SKILL.md # Skill definition
├── README.md # This file
├── requirements.txt # Dependencies
├── scripts/
│ ├── run_v3.py # Main orchestrator (stub mode)
│ ├── llm_runner.py # LLM-integrated runner (full mode)
│ ├── backup.py # Database backup tool
│ ├── generators/ # Triple Generator
│ ├── gates/ # Quality gates
│ ├── evaluators/ # Cognitive diversity
│ ├── learning/ # DARLING + reflection
│ ├── escape/ # Plateau escape
│ ├── novelty/ # Atomic novelty
│ ├── search/ # Web search (Perplexity)
│ └── storage/ # Persistence (SQLite + Qdrant)
├── tests/ # 74 unit tests
├── data/ # Runtime SQLite
├── backups/ # Database backups
└── output/ # Generated results
```
## License
MIT
## Version
v3.3 (2026-01-01) - LLM integration for run_v3.py, --initiative flag for interview database integration