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 gabrielmoreira/agent-skills-mirror --skill news-rough-cutgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/news-rough-cut)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/news-rough-cut"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/news-rough-cut/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/gabrielmoreira/agent-skills-mirror/news-rough-cut"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/news-rough-cut.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 | $0.00108 | $0.01870 |
| Opus 5 | $0.00054 | $0.00935 |
| Sonnet 5 | $0.00022 | $0.00374 |
| Haiku 4.5 | $0.00011 | $0.00187 |
Grade A, and why
news-rough-cut 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
News Rough Cut(新闻智能粗剪)
把新闻素材粗剪为一条内容完整、逻辑清晰、节奏紧凑的新闻短视频。忠实于原始素材,不加任何外部声音,保持客观、正式、紧凑、清晰的信息型新闻风格。
This is an OpenChatCut-native workflow. Use the current project's assets, transcript, word-level editing, timeline, and editing tools. Do not depend on external download or transcode pipelines.
工作流总览
- 完整分析素材(剪辑前必做):识别素材中的新闻事件、核心话题、关键人物、重要结论及有效画面,再确定剪辑主线和成片时长。使用
read_project、transcribe_track、view_timeline_frames逐段核对素材内容。 - 话题分析与时长确定:判断素材包含多少个话题,区分核心话题与次要内容。
- 内容组织:开头直接呈现最重要的新闻结果/核心结论/最新进展/关键现场画面,不铺垫。
- 剪辑执行:按保留/删除规则筛选,讲话按语义完整切割。
- 音频只保留目标新闻素材原声:开始编辑前只把用户明确指定/选中的新闻素材及其原始现场声列入允许来源;未明确指定时只采用活动时间线上已存在的新闻画面与现场声。媒体池里的 BGM、音效、配音、旁白和其他未选素材即使早已存在,也不得进入允许来源。
- 终检:逐段回放核对事实忠实、讲话语义完整、剪切点衔接自然。
话题分析与时长确定
- 原则上一条成片只围绕一条核心新闻主线展开。
- 若素材中存在多个相互独立的话题,优先选择新闻价值最高、信息最完整、画面最充分的话题进行剪辑;不要将无关话题强行拼接在同一条视频中。
- 成片时长不做固定限制,根据以下因素自动确定:
- 核心新闻的信息量;
- 有效人物讲话的长度;
- 事件发展阶段和最新进展;
- 关键现场画面的数量;
- 保证新闻语义完整所需要的时长。
- 信息较少时应缩短成片,避免为了延长时长加入无关内容;信息较多时可以适当延长,不能为了压缩时长而剪断人物讲话、遗漏关键事实或破坏新闻逻辑。
内容组织逻辑
成片开头直接呈现最重要的新闻结果、核心结论、最新进展或关键现场画面,不使用冗长铺垫。整体按照以下逻辑组织:
- 发生了什么;
- 目前有哪些最新进展;
- 最终结果、后续影响或相关回应。
若新闻事件尚未结束,应以当前已经确认的最新进展收尾,不得自行推测结果。
内容保留规则
优先保留以下内容:
- 新闻事件的核心事实;
- 时间、地点、人物和事件结果;
- 最新进展及权威回应;
- 重要人物具有实际信息量的讲话;
- 新闻现场、采访、发布会、监控画面及相关有效素材;
- 能够直接说明事件经过、结果或影响的关键画面。
所有保留内容必须服务于核心新闻主线。
内容删除规则
删除以下内容:
- 广告和商业推广;
- 节目宣传、频道包装及片头片尾;
- 主持人寒暄和无信息量的串场;
- 重复表述和重复画面;
- 无效停顿、口头语及明显空白;
- 与核心事件无关的冗长背景;
- 不影响新闻理解的次要内容;
- 无法验证、表意模糊或容易造成误解的片段。
人物讲话剪辑规则
- 人物讲话必须保持语义完整。
- 优先在以下位置切割:
- 一个完整句子结束后;
- 人物自然停顿处;
- 讲话内容发生明显转折处;
- 镜头自然转场处。
- 不得从一句话中间强行切断,不得只保留部分表述导致原意改变,不得将不同时间、不同语境下的讲话错误拼接。
- 若一段讲话较长,可删除其中重复、空泛或无关的句子,但保留下来的内容必须能够独立表达完整意思。
- 使用词级编辑工具(文字稿)按词删改,保证落点落在完整句子边界。
事实与逻辑要求
剪辑必须忠于原始新闻素材,不得:
- 改变人物讲话原意;
- 夸大或弱化事实;
- 将不同事件错误关联;
- 通过镜头拼接制造虚假因果关系;
- 将推测性内容表达为确定事实;
- 使用与新闻事件不对应的画面误导观众;
- 为追求节奏而删除必要的前因后果。
音频要求
- 不得新增任何背景音乐、配音、旁白、音效、转场音效或其他外部声音。
- 只保留原始素材中与新闻内容直接相关的人声和必要的现场声音。
- 删除广告音乐、节目包装音乐以及与核心新闻无关的声音。
- 处理剪切点时应保证原始人声衔接自然,避免突然截断、重叠、爆音或明显音量跳变(用
edit_item的 fadeInSeconds/fadeOutSeconds 微调剪切点即可,不用加音乐)。
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 · 120 lines · 108 tokens per session scan A 5e2310e111ff
news-rough-cut is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 108 tokens to every session and 1,870 once invoked, about $0.0005 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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