A Git-inspired version control system designed to track edits, run A/B tests, measure quality metrics, and manage rollbacks for AI prompts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-version-control
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install prompt-version-control using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Prompt Version Control?
Prompt Version Control brings software engineering best practices to prompt engineering by providing a Git-like version control system for AI prompts. It allows developers to treat prompts like code, ensuring that every modification is automatically tracked, versioned, and documented with semantic-aware history. This tool eliminates the guesswork of prompt refinement by introducing automated semantic versioning, structural change tracking, and direct remote synchronization with GitHub or GitLab for seamless team collaboration.
Beyond basic version history, the system empowers developers to maintain high prompt quality through empirical evaluation. By implementing comprehensive diffing engines, LLM-as-judge quality metrics, and structured rollback features, it bridges the gap between manual experimentation and rigorous, production-grade prompt optimization. It guarantees a structured environment where prompt variants can be managed, merged, and deployed with absolute confidence.
Prompt Version Control Use Cases
- Solo Developer Iterating on Prompts: A developer building a customer-facing chatbot can systematically edit, track, and test system prompts daily, safely rolling back to high-performing versions when regressions occur.
- Team Prompt Collaboration: Engineering teams managing large repositories of over 50 prompts can use remote sync, push/pull mechanics, and interactive conflict resolution tools to collaborate without overwriting edits.
- Production Rollback Emergency: Teams can mitigate real-time quality drops or regressions in production by identifying the problematic deployment version, executing a rapid rollback in under a minute, and verifying baseline recovery.
How Prompt Version Control Works
- Initialize Prompt Repository: Execute the init command to set up the specialized directory structure including configuration, prompt tracking, history, and metrics modules.
- Register a New Prompt: Create a structured prompt file preconfigured with default templates, metadata tracking, variables, test cases, and performance baselines.
- Edit and Auto-Version: Modify the prompt configuration to trigger an automated semantic version bump (patch, minor, or major) determined by an LLM-assisted semantic diff engine.
- Run A/B Test: Execute automated test cases across multiple prompt versions to compile side-by-side performance tables measuring quality, latency, and token consumption.
- Diff Two Versions: Generate a semantic-aware diff highlighting precise text modifications, structural changes, variable alterations, and LLM-summarized behavioral changes.
- View Version History: Review a comprehensive, Git-log-style history complete with metrics trends, author attribution, and quality score timelines.
- Rollback Overwrites: Revert instantly to any previous historical baseline while preserving full branch history through automated tracking commits.
- Remote Sync: Securely push or pull local repository changes to upstream git hosts, utilizing specialized interactive conflict resolution tools for simultaneous team edits.
- Generate Iteration Reports: Compile comprehensive visual summaries including performance timelines, token cost trends, regression indicators, and high-impact changes.
Prompt Version Control Setup
System Dependencies
Ensure your development environment meets the following requirements:
- Bash 4+
- Unix standard
diffutility - Python 3 (for YAML parsing and testing validation)
- Git (for repository integration)
Installation Steps
Initialize the repository configuration and set up your initial prompt environment using the following commands:
# Initialize the prompt repository structure
./scripts/prompt-vc.sh init ./my-prompts
# Add a new prompt using a specific template type
./scripts/prompt-vc.sh add email-generator --template generator
Practical CLI Workflow
# Open the prompt editor
./scripts/prompt-vc.sh edit email-generator
# Compare differences between two recorded versions
./scripts/prompt-vc.sh diff email-generator v0.1.0 v0.2.0
# Inspect the mutation history logs
./scripts/prompt-vc.sh log email-generator
# Run a structured A/B evaluation test
./scripts/prompt-vc.sh test email-generator
# Execute an instant version rollback
./scripts/prompt-vc.sh rollback email-generator --to v0.1.0
Prompt Version Control Data Schema & Taxonomy
The repository structures all parameters, historical backups, and metrics metadata inside a local workspace folder. This structure integrates natively with existing project source trees.
Repository File Architecture
| Path | Description |
|---|---|
.prompt/config.yaml |
Core repository settings, LLM provider settings, and test dataset pathways. |
.prompt/prompts/ |
Directory containing individual prompt source files managed as YAML. |
.prompt/history/ |
Git-compatible version records storing state mutations. |
.prompt/metrics/ |
Aggregated datasets tracking empirical evaluation metrics from testing cycles. |
.prompt/branches/ |
Head and branch pointers representing parallel iteration lines. |
Prompt Schema Specification
Individual prompt configurations are maintained as validated YAML documents containing the following metadata blocks:
- Core Fields:
name,version,description,model,temperature - Templates:
system,user_templatetracking the underlying instructions - Variables: An explicit list specifying
name, datatype, and validation constraints - Test Cases: An array of explicit inputs matched against expected evaluation outputs
- Metrics Baselines: Analytical slots capturing quality ratings, latency, and token footprints
Prompt Version Control Advanced Features
- LLM-As-Judge Metrics: Utilizes automated evaluation rubrics powered by language models to score response quality objectively across testing data.
- Semantic-Aware Diff Engine: Moves beyond character-level diffs to highlight changes in prompt intent, variables, and structural hyper-parameters.
- Automatic Version Incrementing: Analyzes edits via local tools to intelligently categorize and bump version steps by patch, minor, or major significance.
- Interactive Merge UI: Simplifies repository conflicts during simultaneous multi-user updates through an interactive layout via standard Openclaw Skills interfaces.
- Deterministic Offline Simulation: Executes complete test runs and structural reporting safely without requiring active internet connectivity or API token overhead.
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