Borrowing it
Nothing to install: this file belongs to WayneJin0918/Omni-Rewriter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/WayneJin0918/Omni-Rewriter/main/.cursor/skills/omni-rewriter-model-contribution/SKILL.mdgit clone --depth 1 https://github.com/WayneJin0918/Omni-RewriterWrote 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/waynejin0918/omni-rewriter/omni-rewriter-model-contribution)<a href="https://agentmods.dev/skills/waynejin0918/omni-rewriter/omni-rewriter-model-contribution"><img src="https://agentmods.dev/badge/skills/waynejin0918/omni-rewriter/omni-rewriter-model-contribution/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/waynejin0918/omni-rewriter/omni-rewriter-model-contribution"><img src="https://agentmods.dev/badge/skills/waynejin0918/omni-rewriter/omni-rewriter-model-contribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00746 |
| Opus 5 | $0.00027 | $0.00373 |
| Sonnet 5 | $0.00011 | $0.00149 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
omni-rewriter-model-contribution 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 11d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Omni-Rewriter model contribution
Start here
- Read
docs/architecture.md,docs/dialects/community-models.md, and the relevant profile guide. - Classify the contribution as exactly one of
video,image, orunified. - Copy the structure in CONTRIBUTION_TEMPLATE.md.
- Keep prompt expansion and media generation as separate deliverables.
Required implementation order
- Evidence — link the public upstream prompt/API/runtime contract. Do not infer private behavior or equate stock vLLM, custom vLLM forks, and vLLM-Omni.
- Routing — map public user intent to an existing task, or justify a new transport-neutral
task without breaking current
RewriteRequestusers. - Schema — add or reuse a strict typed output. Unknown fields must remain rejected.
- Validation — encode deterministic constraints before adding repair instructions.
- Renderer — serialize the validated output into the target public dialect.
- Repair — pass only the invalid candidate, errors, and required invariants; keep retries bounded.
- Fixtures and tests — cover valid, invalid, boundary, and RAW-vs-expanded cases.
- Adapter, optional — add generation only when a public contract exists. Pin the tested
runtime and label every untested path
unverified. - Docs — update the supported matrix only to the exact demonstrated level: PE, adapter, or live runtime.
Category checklist
Video
- Require
duration_seconds. - Define timeline, camera, motion, cut, continuity, dialogue, and audio behavior where applicable.
- Test timestamp bounds and task/media-role combinations.
- Optional dialect selector:
metadata.video_pe_profile(h3default,seedancefor Seedance PE,ltxfor LTX-2.5 PE). Seedance is PE-only. LTX has an optional official-CLI runner; live runtime is unverified until a local generate is recorded.
Image
- Omit
duration_seconds. - Define ratio/resolution, visible-text quoting, reference retention, and edit locality.
- Test T2I, I2I, and edit routes only when the model contract supports them.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 82 lines · 54 tokens per session scan A d8e15e38f9b7
omni-rewriter-model-contribution is a skill published in the GitHub repository WayneJin0918/Omni-Rewriter (85 stars, last pushed 21d ago), licensed Apache-2.0. It adds 54 tokens to every session and 746 once invoked, about $0.0003 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.
Other skills, from other repositories
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This skill should be used when the user asks to write, improve, translate, compress, or debug a Seedance 2.0 video prompt; mentions T2V, I2V, V2V, R2V, camera direction, prompt quality, or provides reference assets for a production-ready prompt.
seedance-2-5-prompt-director
A skill for writing and improving prompts for Seedance 2.5, a video-generation system. It covers text-to-video, image-to-video, editing, video extension, references, camera movement, green-screen work, sound, and lip sync.
model-aware-image-prompt-engineer
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nano-banana-image-skill
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awesome-gpt-image-2
A structured library of reusable prompts for GPT-Image2, an image-generation model. It breaks prompts into parts such as subject, lighting, materials, composition, and visual details, with more than 20 templates.
mj-prompt
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