Omni-Rewriter: Skill for Cursor

.cursor/skills/omni-rewriter-promo-pipeline/SKILL.md

omni-rewriter-promo-pipeline is a skill for Cursor from WayneJin0918/Omni-Rewriter. It costs 66 tokens per session (1,617 once invoked), scanned A, original, Apache-2.0.

A workflow for producing promotional videos for the Omni-Rewriter project from expanded text, generated media, proof cards, a designed ending, and a final assembly. It also defines rules for audio, captions, transitions, models, and video validation.

In plain words
What is it for?
Use it to create or regenerate promo scripts, documentation media, proof cards, model showcases, and assembled promotional films.
Why use it?
It gives a repeatable production process for keeping the promotional video’s structure and claims consistent. It helps distinguish verified features from planned or unverified ones.

Skill for Cursor

Written for Cursor: installed under .cursor/.

This is WayneJin0918/Omni-Rewriter's own configuration. It tells Cursor how to work on Omni-Rewriter itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Omni-Rewriter configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is PYTHONPATH=src python scripts/promo/submit_h3_chunk.py docs/promo/omni_promo_chunk_a_messy.json \.

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/WayneJin0918/Omni-Rewriter/main/.cursor/skills/omni-rewriter-promo-pipeline/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/WayneJin0918/Omni-Rewriter

Made for: Cursor.

Wrote 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.

agentmods badge for omni-rewriter-promo-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/waynejin0918/omni-rewriter/omni-rewriter-promo-pipeline/github.svg)](https://agentmods.dev/skills/waynejin0918/omni-rewriter/omni-rewriter-promo-pipeline)
Your own site
<a href="https://agentmods.dev/skills/waynejin0918/omni-rewriter/omni-rewriter-promo-pipeline"><img src="https://agentmods.dev/badge/skills/waynejin0918/omni-rewriter/omni-rewriter-promo-pipeline/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.

agentmods 80×15 button for omni-rewriter-promo-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/skills/waynejin0918/omni-rewriter/omni-rewriter-promo-pipeline"><img src="https://agentmods.dev/badge/skills/waynejin0918/omni-rewriter/omni-rewriter-promo-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,617 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Agent Snooping · line 69
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
  • medium Agent Snooping · line 122
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
  • medium Agent Snooping · line 123
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00066 $0.01617
Opus 5 $0.00033 $0.00809
Sonnet 5 $0.00013 $0.00323
Haiku 4.5 $0.00007 $0.00162

Measured 13d ago against content hash f7b1249b228d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

omni-rewriter-promo-pipeline 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 13d 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.

.cursor/skills/omni-rewriter-promo-pipeline/SKILL.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Omni-Rewriter promo pipeline

Mission

Reproduce the maintainer promo workflow: expand (PE) → generate (H3) → proof cards → designed finale → assemble. Expand is not generate. Do not commit full .mp4 files.

Hard rules

  1. Video tasks require duration_seconds; validate with BaseRewrite before submit.
  2. Promo model matrix excludes Seedance (docs/promo/model_matrix.yaml).
  3. Include roadmap models (wanted / unverified / adapter) honestly—never claim PE for them.
  4. No chapter bumpers (01/02/03). Soft crossfades only.
  5. Never burn VO/dialogue as corner subtitles; quote on-screen product text exactly.
  6. Proof audio comes from the PE arm only.
  7. Models act = short designed finale (build_models_finale.py): bridge → brand hero → staggered T2V|T2I lanes, one continuous audio bed. No H3 hanging-board B-roll; no slat-glued names. Target full film about 30–35s. Keep still-plate motion tiny (no sway).
  8. After H3 intro, run fix_intro_pe_card.py so the PE close-up uses a clean designed card (H3 often garbles PE body text).
  9. Prefer intro freeze as atmosphere; keep both T2V and T2I named in the lanes beat.
  10. Assemble regenerates each outgoing tail as a designed hold from the last frame (blur/vignette/accent — not raw tpad clone) so dialogue survives acrossfade and the gap still looks intentional.
  11. Outro CTA voice: prefer H3 15s take with the same warm narrator (S1) + pluck BGM as the intro (omni_promo_outro_vo.json), then clip the usable window (e.g. 0.35–~10s) into build_outro_audio.py --vo-audio … --bgm-vol 0outro_full.mp4 → assemble --no-endcard. Edge-TTS is fallback only when H3 is unavailable.

Layout

Path Role
docs/promo/*.json PE envelopes (request + output)
docs/promo/model_matrix.yaml Canonical T2V/T2I names + status
scripts/promo/submit_h3_chunk.py Submit one envelope to H3
scripts/promo/submit_h3_batch.py Multi-seed H3 regen on the 8-GPU node
scripts/promo/build_proof_cards.py RAW|PE cards + PE audio
scripts/promo/fix_intro_pe_card.py Replace garbled H3 PE close-up with clean card
scripts/promo/build_models_finale.py Designed models finale (bridge/brand/lanes)
scripts/promo/assemble_promo.py Soft-xfade final film
scripts/promo/build_models_brand_card.py Legacy brand card (not default cut)
scripts/promo/burn_names_on_slats.py Legacy slat name burn (not default cut)
docs/promo/out/ Local outputs (gitignored *.mp4)
docs/day2-h3-pe/ Site: promo entry → home.html

Read the full file on GitHub · 124 lines

Changes

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.

  1. 13d ago First seen · 124 lines · 66 tokens per session scan A f7b1249b228d

Subscribe to this mod's changes

omni-rewriter-promo-pipeline is a skill published in the GitHub repository WayneJin0918/Omni-Rewriter (85 stars, last pushed 23d ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,617 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.

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