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 renalpelvisdeinocheirus70/ozor-skills --skill ozor-prompt-optimizergit clone --depth 1 https://github.com/renalpelvisdeinocheirus70/ozor-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/renalpelvisdeinocheirus70/ozor-skills/ozor-prompt-optimizer)<a href="https://agentmods.dev/skills/renalpelvisdeinocheirus70/ozor-skills/ozor-prompt-optimizer"><img src="https://agentmods.dev/badge/skills/renalpelvisdeinocheirus70/ozor-skills/ozor-prompt-optimizer/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/renalpelvisdeinocheirus70/ozor-skills/ozor-prompt-optimizer"><img src="https://agentmods.dev/badge/skills/renalpelvisdeinocheirus70/ozor-skills/ozor-prompt-optimizer.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 | $0.00146 | $0.03085 |
| Opus 5 | $0.00073 | $0.01543 |
| Sonnet 5 | $0.00029 | $0.00617 |
| Haiku 4.5 | $0.00015 | $0.00309 |
Grade A, and why
ozor-prompt-optimizer 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.
This is a copy
100% identical to ozor-prompt-optimizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ozor Prompt Optimizer
Analyze existing Ozor.ai video prompts and transform them from vague or underperforming into specific, high-quality prompts that produce great results on the first try. This skill acts as a prompt quality reviewer — it identifies what's missing, what's vague, and what can be improved, then rewrites the prompt.
When to Use This Skill
- User has a prompt that produced a mediocre video
- User wants a prompt reviewed before running it
- User is iterating on a video and can't get the result they want
- User asks "why does my video look generic/bad/wrong?"
- User pastes an existing prompt and asks for improvements
The Prompt Quality Framework
Every Ozor prompt is scored on six dimensions. A great prompt scores high on all six.
1. Specificity (most impactful)
Bad: "Show the product" Good: "Show the TaskFlow dashboard with 3 task columns (To Do, In Progress, Done), each containing 2–3 task cards. Zoom in on a card being dragged from 'To Do' to 'In Progress'."
Ozor generates better output when it knows exactly what to render. Vague prompts force the AI to guess, and its guesses will be generic.
Check for:
- Are visual descriptions concrete? (colors, layouts, animations)
- Is text on screen explicitly written out, not just described?
- Are scene transitions specified?
- Is the overall visual style defined beyond just "clean" or "modern"?
2. Structure
Bad: A long paragraph describing the whole video. Good: Numbered scenes, each with a clear title and single purpose.
Check for:
- Is the prompt broken into numbered scenes?
- Does each scene have exactly one job?
- Is there a logical flow (hook → content → CTA)?
- Are scenes ordered for maximum impact?
3. Constraints
Bad: "Make a video about my product" Good: "Create a 45-second landscape (16:9) video with 12 scenes"
Check for:
- Duration specified?
- Aspect ratio specified (16:9 or 9:16)?
- Scene count specified?
- Maximum text per scene implied or stated?
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 · 285 lines · 146 tokens per session scan A 00bb10072793
ozor-prompt-optimizer is a skill published in the GitHub repository renalpelvisdeinocheirus70/ozor-skills (2 stars, last pushed 2d ago), licensed MIT. It adds 146 tokens to every session and 3,085 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ozor-prompt-optimizer, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
ozor-prompt-optimizer
Analyze and improve existing Ozor.ai video prompts for better output quality. Use this skill whenever the user has an Ozor prompt that produced mediocre results, wants to improve a video prompt, needs help debugging why a video didn't turn out well, or wants expert feedback on their prompt before running it. Trigger…
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ozor-document-video
Turn a real document (PDF, PPTX, DOCX, Keynote export, slide deck, proposal, whitepaper, report) into a finished AI-generated video using the Ozor MCP. Use this skill whenever the user attaches, uploads, references, or points to a document file and wants a video from it — e.g. 'make a video from this pitch deck'…