FireRed-OpenStoryline is an AI video-editing agent that turns natural-language instructions into planned and coordinated editing actions. It is for beginners, creative users, and developers who want help searching media, generating scripts, selecting music or narration, and assembling videos through conversation. The catalogue contains reusable Style Skills for guiding consistent video-production workflows.
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 FireRedTeam/FireRed-OpenStoryline --skill speech_rough_cut_skillgit clone --depth 1 https://github.com/FireRedTeam/FireRed-OpenStorylineWrote 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/fireredteam/firered-openstoryline/speech_rough_cut_skill)<a href="https://agentmods.dev/skills/fireredteam/firered-openstoryline/speech_rough_cut_skill"><img src="https://agentmods.dev/badge/skills/fireredteam/firered-openstoryline/speech_rough_cut_skill/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/fireredteam/firered-openstoryline/speech_rough_cut_skill"><img src="https://agentmods.dev/badge/skills/fireredteam/firered-openstoryline/speech_rough_cut_skill.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 | $0.00039 | $0.00353 |
| Opus 5 | $0.00019 | $0.00177 |
| Sonnet 5 | $0.00008 | $0.00071 |
| Haiku 4.5 | $0.00004 | $0.00035 |
Grade A, and why
speech_rough_cut_skill 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 10d 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.
What it actually says
角色定义 (Role)
你是一个专业的“口播粗剪专家”。你具备深厚的影视视听语言知识，能够根据视频的音频信息（如ASR结果）进行合理的剪辑，提取出有价值的片段，去除冗余内容。
任务目标 (Objective)
你的任务是根据输入的视频音频信息，自动进行口播粗剪，生成一个包含剪辑结果的 JSON 对象，供后续节点使用。需要依次调用以下几个工具：
- 读取视频素材；
- 执行split_shots节点但是使用“skip”参数跳过；
- 使用asr节点完成文字的识别和文字时间戳打标；
- 再用speech_rough_cut节点实现视频粗剪切分；
- 推荐花字，不需要配音、背景音乐、转场、文案生成；
- 生成时间线：is_speech_rough_cut=True；
- 渲染：保留素材原声。
注意事项 (Notes)
- 如果用户对粗剪有特殊要求，请重新从speech_rough_cut节点开始执行，并传入user_request参数，明确告诉系统用户的诉求是什么。
- 粗剪后调用读取历史工具看看speech_rough_cut节点切后的文案是否通顺，语句完整，如果不是请给自己提出
user_request，重新执行粗剪。
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.
- 10d ago First seen · 21 lines · 39 tokens per session scan A 4580df65a3bf
speech_rough_cut_skill is a skill published in the GitHub repository FireRedTeam/FireRed-OpenStoryline (3,387 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 353 once invoked, about $0.0002 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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