Knowledge Base Skill

Storage Location

{project_root}/.claude/.rashomon/prompt-knowledge.yaml

Schema

patterns:
  - name: "Pattern name"
    what_to_look_for: |
      When this pattern applies
    improvement: |
      How to improve when detected
    learned_from: "Date and context"
    confidence: 0.0-1.0
    times_applied: 0

anti_patterns:
  - name: "Anti-pattern name"
    what_to_look_for: |
      What to avoid
    why_bad: |
      Why problematic in this project
    learned_from: "Date and context"
    confidence: 0.0-1.0

metadata:
  last_updated: "ISO-8601 timestamp"
  total_comparisons: 0
  patterns_count: 0
  anti_patterns_count: 0
  max_entries: 20

Extraction Criteria

Save as Improvement Pattern

ALL conditions must be true:

• Optimized prompt showed structural improvement (not variance)
• Improvement is project-specific (not explained by BP-001~008)
• Pattern is likely to recur in this project

Confidence Assignment:

Minimum threshold: 0.3 (entries below this are skipped)

Save as Anti-Pattern

ALL conditions must be true:

• Original had problem specific to this project
• Problem is project-specific (beyond standard patterns BP-001~008)
• Problem likely to recur

Extraction Scope

Save only entries that are:

• Project-specific (beyond standard best practices BP-001~008)
• Likely to recur in this project
• Showing clear effect (structural improvement, confidence ≥ 0.3)

Capacity Management

Maximum: 20 entries (patterns + anti_patterns combined)

Retention Score: confidence * (1 + log(times_applied + 1))

This formula:

• Prioritizes high-confidence entries
• Rewards frequently-used patterns
• Treats all entries equally regardless of age

Key Principle: Old entries are valuable. Retention depends on confidence and usage frequency.

Eviction Process:

1. Calculate retention scores for all entries
2. Calculate score for new candidate
3. If new > lowest existing: remove lowest, add new
4. Otherwise: skip new entry

Operations

Retrieval

At start of prompt analysis:

1. Read .claude/.rashomon/prompt-knowledge.yaml (if exists)
2. For each entry, check what_to_look_for against current prompt
3. Return relevant entries with relevance scores
4. Increment times_applied for patterns used

Storage

After comparison (if structural improvement found):

1. Evaluate against extraction criteria
2. Generate candidate entries
3. Check for duplicates
4. Apply capacity management
5. Write updated knowledge base
6. Update metadata

Example Entry

patterns:
  - name: "TypeScript interface reference"
    what_to_look_for: |
      Code generation prompts creating TypeScript types without
      referencing existing type definitions in src/types/
    improvement: |
      Add: "Reference existing types in src/types/ to maintain
      consistency and avoid duplicate type definitions"
    learned_from: "2026-01-14: Comparison showed better type reuse"
    confidence: 0.7
    times_applied: 3

Feedback-Based Adjustments

When comparison results require knowledge base updates:

Confidence Adjustments:

• User confirms improvement: +0.1 (cap at 0.95)
• Pattern led to worse result: -0.2
• Remove entry if confidence < 0.2 after decrease

Entry Management:

• Add new entries from user insight (initial confidence: 0.5)
• Remove entries that fall below confidence threshold