Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill prompt-versioninggit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/prompt-versioning)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/prompt-versioning"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/prompt-versioning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/prompt-versioning"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/prompt-versioning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00566 |
| Opus 5 | $0.00008 | $0.00283 |
| Sonnet 5 | $0.00003 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
prompt-versioning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Versioning
Prompts are code. They should be versioned, tested, reviewed, and deployed with the same rigor as software. Treating prompts as casual text that anyone can edit leads to quality regressions, inconsistent behavior, and debugging nightmares.
Why Version Prompts
- Accountability: Know who changed what and when
- Rollback: Revert to a previous version when a change causes problems
- Testing: Compare performance of different versions
- Collaboration: Multiple people can work on prompts without overwriting each other
- Audit trail: Understand how the prompt evolved and why
Versioning Practices
- Source control: Store prompts in version control (Git, etc.), not in application configuration
- Meaningful commits: Each change should explain what was changed and why
- Change categories: Classify changes as bug fixes, improvements, new features, or experiments
- Review process: Prompt changes should be reviewed before deployment, like code reviews
- Semantic versioning: Major changes (behavioral shift), minor changes (new capability), patches (bug fixes)
Testing Prompt Changes
Before deploying a prompt change:
- Regression testing: Run the golden test set against the new version. Did anything get worse?
- Targeted testing: Test the specific scenario the change was designed to improve
- Edge case testing: Test edge cases related to the change
- A/B testing: For significant changes, run both versions in production and compare
- User testing: For major persona or behavioral changes, test with real users
Prompt Change Management
- Staging environment: Test prompt changes in a non-production environment first
- Gradual rollout: Deploy to a percentage of users, monitor, then expand
- Feature flags: Toggle prompt features on and off without deployment
- Monitoring: Watch quality metrics closely after any prompt change
- Rollback plan: Always know how to revert if the change causes problems
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 43 lines · 16 tokens per session scan A 4a4edb56ab13
prompt-versioning is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 566 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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