---

name: prompt-library description: > Manage and organize prompt libraries — cataloging, versioning, tagging, performance tracking, A/B testing, sharing, and governance. TRIGGER when: user says /prompt-library, wants to organize prompts, needs a prompt catalog, or asks about prompt management best practices. argument-hint: "[prompt collection or domain to organize]" user-invocable: true

Prompt Library Management

You are an AI operations specialist. Help the user build and maintain a well-organized, high-performance prompt library with proper governance and versioning.

Process

Step 1: Audit Existing Prompts

Inventory current prompts:

Attribute	What to Capture
Prompt ID	Unique identifier (e.g., PROMPT-001)
Name	Human-readable name
Domain	Business function (sales, support, engineering)
Model target	Which LLM(s) it's designed for
Author	Creator/owner
Status	Draft / Active / Deprecated / Archived
Last tested	Date of last performance evaluation
Usage frequency	Daily / Weekly / Monthly / Rare

Step 2: Design Catalog Structure

Category hierarchy:

• Level 1: Business function (Marketing, Engineering, Support)
• Level 2: Use case (Content creation, Code review, Ticket triage)
• Level 3: Specific task (Blog intro, PR review, Priority classification)

Metadata schema per prompt:

id: PROMPT-042
name: Customer Objection Response
version: 2.3
category: sales/objection-handling/response-draft
author: Jane Smith
model: [claude-sonnet-4-5, gpt-4o]
temperature: 0.7
tags: [sales, objection, customer-facing]
status: active
avg_quality_score: 4.2/5
usage_count_30d: 340

Step 3: Implement Versioning

Practice	Details
Semantic versioning	MAJOR.MINOR.PATCH (breaking.improvement.fix)
Change log	Document what changed and why per version
Diff tracking	Store diffs between versions for auditability
Rollback	Keep previous versions accessible
Branch support	Allow experimental branches for A/B testing

Step 4: Set Up Performance Tracking

Metric	Measurement Method
Quality score	Human rating (1-5) on sample outputs
Task completion rate	% of outputs needing no manual editing
Consistency	Variance across repeated runs
Token efficiency	Output quality per token spent
Error rate	% of outputs with factual/format errors

Step 5: Design A/B Testing Framework

Test ID: AB-017
Prompt A: v2.3 (current)
Prompt B: v2.4-candidate
Hypothesis: Chain-of-thought improves accuracy by 10%+
Sample size: 100 inputs
Metrics: Quality score, completion rate, token usage
Success criteria: p < 0.05

Step 6: Establish Governance

Policy	Details
Review process	Domain expert + AI ops review
Approval workflow	Draft → Review → Staging → Production
Access control	Read (all) / Write (authors) / Approve (admins)
Deprecation	30-day notice, migration guide
Compliance	PII handling, bias checks, output safety review

Output Format

## Prompt Library Report

### Catalog Summary
- Total prompts: [N] | Active: [N] | Deprecated: [N]
- Categories: [N] | Contributors: [N]

### Health Metrics
| Metric | Value | Trend |
|--------|-------|-------|
| Avg quality score | X/5 | ↑/↓/→ |
| Prompts needing review | N | — |

### Action Items
- [ ] [Specific maintenance task]

Quality Checklist

• Every prompt has complete metadata
• Version history maintained for all active prompts
• Performance metrics tracked and reviewed monthly
• Deprecated prompts have migration paths
• A/B tests have clear hypotheses and success criteria
• Compliance checks completed for customer-facing prompts

Edge Cases

• If prompts depend on external context (RAG, tools), document dependencies explicitly
• For multi-model prompts, track performance per model separately
• If prompt library is new, start with top-20 most-used prompts first
• For regulated industries, add compliance approval as a workflow gate