Use AskUserQuestion tool with:
- question: "What type of research entry is this?"
- header: "Entry Type"
- options:
  1. label: "Decision"
     description: "Architectural or design choice made"
  2. label: "Finding"
     description: "Discovery or insight during development"
  3. label: "Learning"
     description: "Knowledge or best practice learned"
  4. label: "Spike"
     description: "Investigation or evaluation results"
- multiSelect: false

## Research Entry

**Title:** [Clear, searchable title]

**Context:**
What prompted this research? What problem were you solving?

**Content:**
What was discovered/decided? Include specifics.

**Rationale:**
Why this conclusion? What factors influenced the decision?

**Alternatives Considered:** (if applicable)
What else was evaluated? Why were they rejected?

**References:** (optional)

- Issue/PR numbers
- Documentation links
- Related entries

# Current git context
branch = get_current_branch()
recent_commits = get_recent_commits(limit=3)

# Related issues
related_issues = extract_issue_refs(content)

# Current project
project = get_project_name()

# Tags from content
suggested_tags = extract_keywords(title + content)

Use AskUserQuestion tool with:
- question: "Confirm tags for this entry (suggested based on content):"
- header: "Tags"
- options:
  1. label: "{suggested_tag_1}"
     description: "Auto-detected from content"
  2. label: "{suggested_tag_2}"
     description: "Auto-detected from content"
  3. label: "{suggested_tag_3}"
     description: "Auto-detected from content"
  4. label: "Add custom tags"
     description: "Specify your own tags"
- multiSelect: true

import json
import os
from datetime import datetime
from uuid import uuid4

def create_research_entry(entry_type, title, content, context, rationale, alternatives, tags, project, references):
    # Generate ID
    index = load_index()
    next_num = len(index.get('entries', [])) + 1
    entry_id = f"r{next_num:03d}"

    # Create entry
    entry = {
        "id": entry_id,
        "type": entry_type,
        "title": title,
        "content": content,
        "context": context,
        "rationale": rationale,
        "alternatives": alternatives or [],
        "tags": tags,
        "project": project,
        "createdAt": datetime.utcnow().isoformat() + "Z",
        "updatedAt": datetime.utcnow().isoformat() + "Z",
        "references": references or [],
        "relatedEntries": []
    }

    # Ensure directory exists
    os.makedirs(".claude/research/entries", exist_ok=True)

    # Save entry
    with open(f".claude/research/entries/{entry_id}.json", "w") as f:
        json.dump(entry, f, indent=2)

    # Update index
    update_index(entry)

    return entry_id

def embed_entry(entry):
    """Generate and store embedding for semantic search."""
    # Combine searchable content
    text = f"{entry['title']}\n{entry['content']}\n{entry.get('rationale', '')}"

    # Check for cloud API
    api_key = os.environ.get('POPKIT_API_KEY')
    if not api_key:
        return None

    # Generate embedding via cloud
    try:
        response = requests.post(
            "https://api.thehouseofdeals.com/v1/embeddings",
            headers={"Authorization": f"Bearer {api_key}"},
            json={
                "text": text,
                "id": entry['id'],
                "type": "research",
                "metadata": {
                    "title": entry['title'],
                    "type": entry['type'],
                    "tags": entry['tags'],
                    "project": entry['project']
                }
            }
        )
        if response.ok:
            return response.json().get('embeddingId')
    except Exception as e:
        print(f"Embedding failed (offline mode): {e}")

    return None

.claude/
  research/
    index.json              # Master index
    entries/
      r001.json             # Individual entries
      r002.json
      ...

{
  "version": "1.0.0",
  "lastUpdated": "2024-12-09T10:30:00Z",
  "entries": [
    {
      "id": "r001",
      "type": "decision",
      "title": "Use Redis for session storage",
      "tags": ["auth", "infrastructure"],
      "project": "popkit-cloud",
      "createdAt": "2024-12-09T10:30:00Z",
      "embeddingId": "vec_r001"
    }
  ],
  "tagIndex": {
    "auth": ["r001", "r015"],
    "infrastructure": ["r001"]
  },
  "projectIndex": {
    "popkit-cloud": ["r001", "r002"]
  }
}

Use AskUserQuestion tool with:
- question: "Any research insights to capture from this session?"
- header: "Research"
- options:
  1. label: "Yes, capture insights"
     description: "Record decisions, findings, or learnings"
  2. label: "No, nothing to capture"
     description: "Skip research capture"
- multiSelect: false

def surface_related_research(issue_keywords):
    """Search for related research entries."""
    # Local search
    index = load_index()
    matches = []

    for entry in index['entries']:
        if any(kw.lower() in entry['title'].lower() for kw in issue_keywords):
            matches.append(entry)

    # Semantic search (if cloud available)
    if os.environ.get('POPKIT_API_KEY'):
        semantic_matches = semantic_search(issue_keywords)
        matches.extend(semantic_matches)

    return dedupe_by_id(matches)[:5]

Found related research:
- [decision] Use Redis for session storage (r001)
- [finding] JWT refresh token race condition (r015)

View with /popkit:research show <id>

def check_decision_conflicts(changed_files, changes_summary):
    """Flag potential conflicts with documented decisions."""
    decisions = [e for e in load_index()['entries'] if e['type'] == 'decision']

    conflicts = []
    for decision in decisions:
        # Simple keyword matching (enhanced by embeddings in cloud)
        if overlaps(decision['tags'], changed_files):
            conflicts.append({
                'decision': decision,
                'reason': f"Changes to {changed_files} may affect '{decision['title']}'"
            })

    return conflicts

{
  "id": "r001",
  "type": "decision",
  "title": "Use Redis for session storage",
  "content": "We chose Redis (via Upstash) for storing session tokens...",
  "context": "Evaluating session storage for auth system",
  "rationale": "Redis provides native TTL, sub-ms latency, serverless-compatible",
  "alternatives": ["PostgreSQL sessions", "JWT-only", "Memcached"],
  "tags": ["auth", "infrastructure", "redis"],
  "project": "popkit-cloud",
  "references": ["#68", "https://upstash.com/docs/redis/"]
}

{
  "id": "r015",
  "type": "finding",
  "title": "JWT refresh token race condition",
  "content": "Discovered that concurrent refresh requests can invalidate each other...",
  "context": "Debugging intermittent auth failures",
  "rationale": "First refresh succeeds, second uses stale token",
  "tags": ["auth", "security", "race-condition"],
  "project": "popkit-cloud"
}

{
  "id": "r004",
  "type": "spike",
  "title": "Evaluate email providers for transactional email",
  "content": "Compared Resend, SendGrid, Postmark, and AWS SES...",
  "context": "Need transactional email for auth and billing",
  "rationale": "Resend: best DX, fair pricing, good deliverability",
  "alternatives": [
    { "name": "SendGrid", "reason": "Complex API, overkill for our needs" },
    { "name": "AWS SES", "reason": "Requires more setup, region restrictions" },
    { "name": "Postmark", "reason": "Great but more expensive" }
  ],
  "tags": ["email", "infrastructure", "comparison"],
  "project": "popkit-cloud"
}

Research entry captured:

ID: r001
Type: decision
Title: Use Redis for session storage
Tags: auth, infrastructure, redis
Project: popkit-cloud

Embedding: Generated (cloud sync enabled)

Use /popkit:research show r001 to view
Use /popkit:research search "..." to find later