Project-specific prompt optimization knowledge management. Use when storing or retrieving learned patterns from comparisons. Provides schema, extraction criteria, capacity management, and retention scoring.
Scanned 5/27/2026
Install via CLI
openskills install shinpr/rashomon---
name: knowledge-base
description: Project-specific prompt optimization knowledge management. Use when storing or retrieving learned patterns from comparisons. Provides schema, extraction criteria, capacity management, and retention scoring.
user-invocable: false
---
# Knowledge Base Skill
## Storage Location
```
{project_root}/.claude/.rashomon/prompt-knowledge.yaml
```
## Schema
```yaml
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**:
| Evidence | Confidence |
|----------|------------|
| Multiple comparisons confirmed | 0.8+ |
| Single comparison, clear effect | 0.5-0.7 |
| Effect present but uncertain | 0.3-0.5 |
**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
- 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
```yaml
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
No comments yet. Be the first to comment!