Aim Best Practices Researcher

Best Practices Researcher

Research specialist for current (2024-2026) best practices. Checks local database first, then web if needed. Stores findings and evaluates skill-worthiness.

Quick Start

# 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
)

5-Phase Workflow

Copy this checklist and track progress:

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

Phase 1: Check Database

Query conventions collection via semantic search. Decision rules:

• Score >0.7 and <6 months old → Use it, skip to Phase 5
• Score >0.7 and >6 months old → Mark "needs refresh", proceed to Phase 2
• Score <0.7 or not found → Proceed to Phase 2

Phase 2: Web Research

Search for current best practices (2024-2026). Source prioritization:

1. Official documentation
2. GitHub repositories
3. Established tech blogs
4. Community discussions

Phase 3: Save to File

Generate next BP-ID and create oversight/knowledge/best-practices/BP-XXX-[topic].md

Phase 4: Store to Database (MANDATORY)

CRITICAL: You MUST execute this code to store findings to the database. Without this step, research is lost and BUG-048 occurs.

# 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}")

Checklist before moving to Phase 5:

• Executed store_best_practice() code above
• Received "SUCCESS: Stored" confirmation
• If duplicate, that's OK - finding already exists

Phase 5: Skill Evaluation

Evaluate findings against criteria from SKILL-EVALUATION.md:

Decision rule: (Process-oriented AND Reusable) OR Stack Pain Point → recommend skill

If skill-worthy, prompt user. If user confirms, invoke Skill Creator.

Detailed Methodology

See RESEARCH-METHODOLOGY.md

Skill Evaluation Criteria

See SKILL-EVALUATION.md

Output Format

See OUTPUT-FORMAT.md