You are an LLM agent with access to a Cognee knowledge graph for memory.

**IMPORTANT RULES:**
- Never call the `prune` command
- Always search memory before responding to user queries
- Automatically cognify new information you learn about the user

**Memory Workflow:**
1. **Before each response**: Search the knowledge graph
   - Map user request to appropriate search type:
     - Summary → SUMMARIES
     - Relationships → INSIGHTS  
     - Specific facts → CHUNKS
     - Explanations → COMPLETION
     - Complex relations → GRAPH_COMPLETION
     - Code examples → CODE
   
2. **Search command**:
   ```text
   cognee-mcp__search(\{
     search_query: "user prompt",
     search_type: "mapped type"
   \})
   ```

3. **Incorporate results** into your response

**Memory Updates:**
- When you learn new facts, preferences, or relationships about the user
- Call: `cognee-mcp__cognify(\{ data: "information" \})`
- Monitor with: `cognee-mcp__cognify_status()`

**Code Analysis:**
- When asked to analyze code repositories
- Use: `cognee-mcp__codify(\{ repo_path: "path" \})`
- Only process files returned by `rg --files`

goose run -i ~/.config/goose/cognee-instructions.md -s

COGNEE_MEMORY_SYSTEM:
You have access to a Cognee knowledge graph for persistent memory.

MEMORY_RETRIEVAL_PROTOCOL:
- Before responding, determine request type and map to search type
- Search types: SUMMARIES, INSIGHTS, CHUNKS, COMPLETION, GRAPH_COMPLETION, CODE
- Always call: cognee-mcp__search with search_query and search_type parameters
- Incorporate memory results into responses

MEMORY_STORAGE_PROTOCOL:
- Auto-cognify new user facts, preferences, relationships
- Call: cognee-mcp__cognify with data parameter
- Never use prune command

CODE_ANALYSIS_PROTOCOL:
- For repositories: cognee-mcp__codify with repo_path parameter
- Only process files from rg --files output

# Start goose with Cognee
goose session

# In goose, analyze your codebase
> goose, please codify this repository and then help me understand the architecture

# Cognify research documents
> goose, please cognify the contents of these research papers: paper1.pdf, paper2.pdf, paper3.pdf

# Later, query relationships
> What are the connections between the methodologies in these papers?

# Store preferences
> Remember that I prefer morning meetings, work best with 2-hour focused blocks, and need 15-minute breaks between calls

# Query later
> Based on my preferences, how should I structure tomorrow's schedule?

# Create optimized startup script
cat > start-cognee-optimized.sh << 'EOF'
#!/bin/bash
set -e

# Performance settings
export DEBUG=false
export LOG_LEVEL=WARNING
export RATE_LIMIT_INTERVAL=30

# Model configuration
export LLM_API_KEY=${OPENAI_API_KEY}
export LLM_MODEL=openai/gpt-4o-mini  # Faster, cheaper model
export EMBEDDING_API_KEY=${OPENAI_API_KEY}
export EMBEDDING_MODEL=openai/text-embedding-3-small  # Faster embedding

# Server settings
export HOST=0.0.0.0
export PORT=8000

cd /path/to/cognee-mcp
uv run python src/server.py --transport sse
EOF

chmod +x start-cognee-optimized.sh

# Check status
> goose, what's the status of the cognify pipeline?

# Selective pruning (if needed)
> goose, can you help me identify outdated information in the knowledge graph?

# Check Cognee logs
tail -f ~/.local/share/cognee/logs/cognee.log

# Test server connection
curl http://localhost:8000/health

# Verify knowledge graph status
# In goose session:
> goose, run cognify_status and codify_status

# Setup
> goose, codify this repository and remember that I prefer: functional programming patterns, comprehensive tests, and clear documentation

# Usage
> Review this pull request and check it against my coding preferences

# Before meeting
> goose, cognify the agenda and participant backgrounds from these documents

# During/after meeting
> Based on the knowledge graph, what are the key action items and how do they relate to our previous discussions?

# Literature review
> goose, cognify these 10 research papers and create a knowledge graph of the relationships between their methodologies

# Synthesis
> What are the emerging patterns in the research and what gaps exist?