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
import os
import sys
sys.path.insert(0, os.path.join(os.path.expanduser("~/.ai-memory"), "src"))
from memory.search import search_memories
from memory.secrets_env import pin_qdrant_api_key, is_auth_error

# Pin QDRANT_API_KEY from .env.secrets so a stale exported key can't silently
# fail auth and degrade this search to file-only (run-with-env.sh parity).
pin_qdrant_api_key()

# The 'conventions' collection is project-scoped (PLAN-028 P1, DEC-PM298-D4).
# Resolve the project from AI_MEMORY_PROJECT_ID — never from os.getcwd(), which
# is unreliable for this forked skill subprocess. Fail loud if it is not set.
project_id = os.environ.get("AI_MEMORY_PROJECT_ID")
if not project_id:
    raise RuntimeError(
        "AI_MEMORY_PROJECT_ID is not set — cannot search the project-scoped "
        "'conventions' collection. Set AI_MEMORY_PROJECT_ID and retry."
    )
try:
    results = search_memories(
        query="your topic",
        collection="conventions",
        group_id=project_id,
        memory_type=["guideline", "rule"],
        limit=5
    )
except Exception as e:
    # Auth failure: the knowledge base was NOT consulted. Do not present this
    # as "no results found" — results are file-only.
    if is_auth_error(str(e)):
        print("❌ Memory search auth FAILED (401) — knowledge base NOT "
              "consulted; results are file-only")
    raise

# Phase 4: Store findings
scripts/memory/run-with-env.sh store_best_practice.py \
    --content "Best practice description" \
    --session-id "current-session" \
    --domain "python" \
    --tags topic \
    --source "https://source-url.com" \
    --source-date "2026-01-29" \
    --group-id "$AI_MEMORY_PROJECT_ID"

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 run this command to store findings to the database. Without this step, research is lost and BUG-048 occurs.

# MANDATORY - Run this command to store findings
scripts/memory/run-with-env.sh store_best_practice.py \
    --content "YOUR_FINDING_CONTENT_HERE" \
    --session-id "YOUR_SESSION_ID" \
    --domain "YOUR_DOMAIN" \
    --tags YOUR TAGS \
    --source "SOURCE_URL" \
    --source-date "2026-05-30" \
    --group-id "$AI_MEMORY_PROJECT_ID"

Checklist before moving to Phase 5:

• Ran store_best_practice.py via run-with-env.sh
• Received "Stored: " or "Duplicate skipped" 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