Knowledge Curator

For each learning file:

1. Read the file content
2. Extract:
   • Title/slug from filename
   • Tags from frontmatter
   • Key concepts mentioned
   • Technology/tools mentioned

Step 3: Group by Topic

Analyze and group learnings by:

• Common tags
• Similar titles
• Related technologies
• Problem domain

Step 4: Identify Distill Candidates

A topic is ready for distill when:

• 3+ learnings on same topic
• Clear pattern emerges
• Reusable across projects
• Not already in knowledge-base

Step 5: Check Existing Knowledge Base

ls -la docs/knowledge-base/

Compare with candidates to avoid duplicates.

Step 6: Prioritize Topics

Output Format

## Knowledge Curation Report

**Learnings Scanned:** N files
**Date Range:** YYYY-MM-DD to YYYY-MM-DD
**Distill Candidates:** X topics

---

### Ready to Distill (3+ learnings)

#### 1. [Topic Name] - High Priority
**Learnings:** 5 files
**Pattern:** Brief description of the pattern

| File | Date | Key Point |
|------|------|-----------|
| `HH.MM_slug.md` | YYYY-MM-DD | Key insight |

**Suggested Distill Title:** `topic-name.md`
**Suggested Tags:** tag1, tag2, tag3

---

### Growing Topics (2 learnings)

| Topic | Count | Latest |
|-------|-------|--------|
| [topic] | 2 | YYYY-MM-DD |

---

### Knowledge Gaps

Areas with learnings but no knowledge base entry:
- [gap 1]
- [gap 2]

---

### Recommendations

1. **Distill Now:** [topic] - has 5+ learnings
2. **Watch:** [topic] - needs 1 more learning
3. **Review:** [existing entry] - might need update