Pwrl Learnings Dedup

pwrl-learnings-dedup — Learning Deduplication

Purpose: Phase 4 of learnings workflow. Detects and merges duplicate or semantically similar learnings while preserving complete audit trails and source history. Maintains knowledge base quality and discoverability.

Interaction Method

• Automated duplicate detection with fingerprinting.
• Flagged similarities (85%+) shown for user decision.
• Ask: "Merge these similar learnings? Yes/No/Keep separate"
• Show comparison before merging.
• No approval gate; proceed to save.

Input: Structure Artifact

Expects artifact from pwrl-learnings-structure with:

structure_id: YYYY-MM-DD-NNN-structure
learnings: [array of structured learnings with metadata]

Output: Dedup Artifact

Emit dedup artifact (YAML + markdown):

---
format: pwrl-learnings-dedup-artifact
version: "1.0"
dedup_id: YYYY-MM-DD-NNN-dedup
created: ISO-8601-timestamp
---

# Learning Deduplication Results

## Summary
- **Before Dedup:** [count] learnings
- **After Dedup:** [count] unique learnings
- **Duplicates Merged:** [count]
- **Archived:** [count]
- **Dedup Confidence:** [0-100%]

## Merge Actions

### Exact Merges: [count]
- [Learning A] merged into [Learning B]
- Preserved: sources, examples, tags from both

### High-Similarity Flagged: [count]
- [Learning C] similar to [Learning D] (94% match)
- User decision: merged | kept-separate

### Related Linked: [count]
- [Learning E] complements [Learning F]
- Cross-reference created

## Archived Mapping
- `OLD_ID_1` → `NEW_ID_1`
- `OLD_ID_2` → `NEW_ID_2`

## Ready for Save
- **Status:** ready
- **Next Skill:** pwrl-learnings-save
- **Artifacts Passed:** This dedup artifact + updated learnings

Detailed Workflow

For complete step-by-step instructions, see dedup-learnings-detailed-workflow.md.

This SKILL.md provides an overview. The detailed workflow document contains:

• Fingerprint calculation (exact, semantic, text similarity)
• Duplicate finding algorithm
• Merge strategy selection
• Merge algorithm with winner selection
• High-similarity flagging process
• Related learning linking
• Archive mapping with audit trail

Duplicate Detection: Late Resolution

This phase performs final duplicate resolution using three fingerprinting strategies:

1. Exact Fingerprint: MD5(type + domain + title) → 95%+ confidence
2. Semantic Fingerprint: MD5(type + domain + problem + sorted_tags) → 85-95% confidence
3. Text Similarity: Cosine similarity of tokenized content → 70-95% confidence

See duplicate-handling-consolidated.md for complete fingerprinting details.

Quality Gate Validation

After completing this phase, run quality gate validation:

/pwrl-phase-checkpoint learnings 4 [artifact-path]

See pwrl-phase-checkpoint for validation rules.

Check input has valid structure_id and learnings array with complete metadata.

Step 2: Calculate Fingerprints

For each learning, generate three fingerprints:

1. Exact Fingerprint: MD5(type + domain + title.lowercase.strip())

   • Detects identical learnings (copy-paste duplicates)

2. Semantic Fingerprint: MD5(type + domain + problem_hash + tags_sorted)

   • Detects same concept expressed differently

3. Text Similarity: Cosine similarity of (title + problem + tags)

   • Detects partially matching learnings
   • Threshold: 85% for "high similarity"

Step 3: Find Duplicates

Compare all fingerprints:

For each pair (L1, L2):
  if exact_fingerprint(L1) == exact_fingerprint(L2):
    → Exact duplicate (auto-merge)

  elif semantic_fingerprint(L1) == semantic_fingerprint(L2):
    → Semantic duplicate (auto-merge)

  elif text_similarity(L1, L2) > 0.85:
    → High similarity (flag for review)

  elif related_tags(L1, L2) and type(L1) != type(L2):
    → Complementary learning (cross-reference)

Step 4: Apply Merge Strategy

Three strategies available:

Default: hybrid (recommended)

Step 5: Merge Exact Duplicates

For exact/semantic matches:

1. Choose winner:

   • Most recent (newer created_at)
   • Most complete (longest problem + application)
   • Highest applicability score

2. Preserve loser data:

   • Append loser's tags to winner's tags (deduplicate)
   • Merge examples: winner.examples.push(...loser.examples)
   • Extend sources: winner.sources.push(...loser.sources)
   • Note: merged_from: [loser.id]

3. Archive loser:

   • Mark as archived: true
   • Set merged_into: winner.id
   • Keep for audit trail

Example:

Winner: learning-id-1
  title: "Race Condition in Shared Cache"
  problem: "Multiple threads accessing cache without locking"
  tags: [concurrency, cache, threading]
  sources:
    - src/cache.ts:42
  examples: ["Reproducer code 1"]

Loser: learning-id-2
  title: "Race Condition - Cache Access"  # Similar but different
  problem: "Concurrent cache access causes data corruption"
  tags: [concurrency, cache]  # Subset of winner's
  sources:
    - src/old-cache.ts:20
  examples: ["Reproducer code 2"]

Result: learning-id-1 (winner)
  title: "Race Condition in Shared Cache"
  problem: "Multiple threads accessing cache without locking, causing data corruption"
  tags: [concurrency, cache, threading]  # Merged + deduped
  sources:
    - src/cache.ts:42
    - src/old-cache.ts:20  # Both preserved
  examples:
    - "Reproducer code 1"
    - "Reproducer code 2"  # Both preserved
  merged_from: [learning-id-2]
  merged_at: 2026-06-12T10:30:00Z

Step 6: Flag High-Similarity Matches

For 85%+ similarity (non-exact):

1. Display to user:

   High Similarity Match (91%)
   
   Learning A: "React Hook Dependency Gotcha"
   Learning B: "React Hooks - Dependency Array Gotcha"
   
   Should these be merged? Yes / No / Keep separate
   

2. User decides:

   • Yes: Merge like exact duplicate
   • No: Keep separate; no cross-reference
   • Keep separate: Mark with cross-reference

Step 7: Link Related Learnings

For complementary learnings:

Learning: "Race Condition Gotcha"
Related:
  - [prevents]: "Mutex Usage Pattern"
  - [complements]: "Immutable Data Structures"

Step 8: Generate Dedup Artifact

Emit artifact with:

• Exact merges count
• Flagged similarities
• Related links
• Archived mapping (old_id → new_id)
• Updated learning list
• Quality metrics

Error Handling

Testing Coverage

Test file: tests/pwrl-learnings/dedup-learnings.test.ts

Happy Path Tests:

• ✅ Exact duplicate (auto-merged)
• ✅ Semantic duplicate (auto-merged)
• ✅ High similarity (flagged, user chooses)
• ✅ Related learnings (cross-referenced)
• ✅ No duplicates (all kept)

Edge Cases:

• ✅ Conflicting metadata (merged correctly)
• ✅ Multiple duplicates of same learning (all merged to one)
• ✅ Circular relationships (handled)
• ✅ User decision flip (corrected)

Output Validation Tests:

• ✅ Archived mapping complete
• ✅ Winner selection correct
• ✅ Data preserved (no loss)
• ✅ Confidence score accurate