# Phase 1: Check database
from memory.search import search_memories
results = search_memories(
    query="your topic",
    collection="conventions",
    memory_type=["guideline", "rule"],
    limit=5
)

# Phase 4: Store findings
from memory.storage import store_best_practice
result = store_best_practice(
    content="Best practice description",
    session_id="current-session",
    source_hook="manual",
    domain="python",
    tags=["topic"],
    source="https://source-url.com",
    source_date="2026-01-29",
    auto_seeded=True
)

Research Progress:
- [ ] Phase 1: Check database (conventions collection)
- [ ] Phase 2: Web research (if needed)
- [ ] Phase 3: Save to file (BP-XXX.md)
- [ ] Phase 4: Store to database
- [ ] Phase 5: Evaluate skill-worthiness

# MANDATORY - Execute this code block
from memory.storage import store_best_practice
import os

# Get session_id from environment or use placeholder
session_id = os.environ.get("CLAUDE_SESSION_ID", "manual-research")

result = store_best_practice(
    content="YOUR_FINDING_CONTENT_HERE",  # Replace with actual finding
    session_id=session_id,
    source_hook="manual",
    domain="YOUR_DOMAIN",  # e.g., "python", "testing", "architecture"
    tags=["YOUR", "TAGS"],  # Replace with relevant tags
    source="SOURCE_URL",  # URL where you found this
    source_date="2026-02-03",  # Today's date
    auto_seeded=True,
    type="guideline"  # Stored as guideline in conventions collection
)

# Verify storage succeeded
if result.get("status") == "stored":
    print(f"SUCCESS: Stored to conventions collection: {result['memory_id']}")
else:
    print(f"WARNING: {result.get('status', 'unknown')} - {result}")