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Knowledge base

Skill shinpr/rashomon/skills/knowledge-base

Measure prompt and skill improvements with blind A/B comparison.

Install
npx -y skills add shinpr/rashomon --skill knowledge-base

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Project-specific prompt optimization knowledge management. Use when storing or retrieving learned patterns from comparisons. Provides schema, extraction criteria, capacity management, and retention scoring.

SKILL.md

5.3 KB, as published. Nobody here has run it

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"
    source: "comparison ID, report path, or user feedback"
    source_fingerprint: "stable hash or revision identifying the evidence"
    validity_scope: "project area, version range, or conditions where this applies"
    last_verified: "ISO-8601 timestamp"
    invalidated_when: "observable condition that requires revalidation"
    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"
    source: "comparison ID, report path, or user feedback"
    source_fingerprint: "stable hash or revision identifying the evidence"
    validity_scope: "project area, version range, or conditions where this applies"
    last_verified: "ISO-8601 timestamp"
    invalidated_when: "observable condition that requires revalidation"
    confidence: 0.0-1.0
    times_applied: 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:

EvidenceConfidence
Multiple comparisons confirmed0.8+
Single comparison, clear effect0.5-0.7
Effect present but uncertain0.3-0.5

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
  • Applies no direct age penalty; validity is evaluated separately

Age alone does not reduce retention. Before scoring, revalidate an entry when its invalidated_when condition is observed or its source fingerprint no longer matches. An entry with unresolved validity is excluded from retrieval until verified.

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. Exclude entries whose validity condition is triggered or whose source fingerprint is stale
  3. For each valid entry, check what_to_look_for against current prompt
  4. Return relevant entries with relevance scores and provenance

Retrieval is read-only. It records proposed entry IDs in the prompt-analysis result; it does not increment counters or write the knowledge file.

Storage

After a comparison and user feedback confirm how an entry affected execution:

  1. Evaluate against extraction criteria
  2. Generate candidate entries
  3. Check for duplicates
  4. Increment times_applied for each valid entry whose use is confirmed by the report
  5. Revalidate source fingerprints and validity conditions
  6. Apply capacity management
  7. Write updated knowledge base
  8. 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"
    source: "comparison: cmp-20260114-types"
    source_fingerprint: "git:abc123:src/types"
    validity_scope: "TypeScript generation under src/"
    last_verified: "2026-01-14T12:00:00Z"
    invalidated_when: "src/types is removed or its public type policy changes"
    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

Keep looking

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