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 ChenShuo2004/cs-skills --skill cs-narration-phrase-timelinegit clone --depth 1 https://github.com/ChenShuo2004/cs-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/chenshuo2004/cs-skills/cs-narration-phrase-timeline)<a href="https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-narration-phrase-timeline"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-narration-phrase-timeline/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/chenshuo2004/cs-skills/cs-narration-phrase-timeline"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-narration-phrase-timeline.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 | $0.00084 | $0.01062 |
| Opus 5.5 | $0.00034 | $0.00425 |
| Sonnet 5.5 | $0.00017 | $0.00212 |
| Haiku 4.5 | $0.00008 | $0.00106 |
Grade A, and why
cs-narration-phrase-timeline 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 18d 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
口播短语时间表
目标与边界
将真实口播音频与实际朗读的中文稿对齐，交付按短语排序的 phrase-timeline.json。它为字幕、重点词、动态图文和 Motion Graphics 提供真实的毫秒与帧号边界。
这个 Skill 不生成配音、不按字数估算时间、不编辑时间线，也不导出 MP4。没有最终旁白音频时，不能把计划时长伪装成对齐结果。
输入与交付
需要：
- 最终旁白音频；若是音色克隆工作流，使用克隆完成后的音频，不使用参考音频。
- 与音频实际朗读内容一致的完整口播稿；不能用分镜提纲、摘要或事后改写稿替代。
- 可选：现有任务目录、目标帧率（默认 30 fps）和输出位置。
交付：
alignment.tokens.json：真实语音识别的词级边界。phrase-timeline.json：短语到真实毫秒/帧的映射。- 对齐结果摘要：音频总时长、全文覆盖率、失败原因或可用文件位置。
字段与验收要求见 时间表结构。
工作流
- 先找已有产物。 如果项目任务目录已经有
phrase-timeline.json，先检查它是否来自最终音频且符合结构；核对音频与稿件的哈希或等效来源记录以及目标帧率；一致且有效时直接复用，避免重新识别。仅文件名相同不足以证明仍有效。 - 选择正确入口。 目标项目已有“动态信息图”或同类工作台时，优先让它在配音完成后自动生成时间表。只有用户已提供最终音频、需要独立产物时才走手动对齐。
- 验证实现，不猜命令。 手动执行前，确认目标项目实际存在音频对齐器、短语构建器、Node、FFmpeg/FFprobe 与项目 Python 环境。常见实现会有
align.mjs与semantic_timeline.py；不要假定某个旧的 CLI 文件仍存在。 选择 WhisperX / faster-whisper 后，先核对所需 ASR 与中文对齐模型的版本、缓存路径、完整文件与哈希，复用有效缓存；只下载缺失文件，不为每个 Skill 新建模型副本。让实际加载器使用同一缓存或检查结果的本地模型目录。其他对齐后端沿用其配置，不强制下载 Whisper 模型；VAD 与 Python/GPU 依赖另行检查。 - 先对齐音频，再构建短语。 音频对齐器先写词级时间戳；短语构建器再使用原文和这些时间戳生成结果。不要按字符数量、页面时长或预设语速补全缺失时间。
- 严格验收。 覆盖率过低、短语顺序不能匹配、输出缺少真实时间边界，或结果来自估算回退时，应明确失败并保留诊断信息，而不是交付不可靠的时间表。
结果使用
- 字幕与重点词：直接读取每个短语的起止毫秒或帧号。
- 信息图与 Motion Graphics：元素不得早于对应口播短语开始出现。
- 需要重新配音或改稿时：重新生成时间表；旧表不能复用于音频节奏已变化的版本。
完成检查
交付前确认：
timing_source是基于真实音频的对齐来源，且estimated_fallback_used为false。phrases按口播顺序排列，每条都有合法的起止时间与帧号。- 全文覆盖率、单短语覆盖率和置信度已保留；低覆盖率不会被静默忽略。
- 口播稿、音频和生成文件中可能含有私密语音或文案；默认留在用户指定的本地任务目录，不上传或公开。
What ships with it
2 files 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.
- 18d ago First seen · 55 lines · 84 tokens per session scan A 1b8157f7d7cf
cs-narration-phrase-timeline is a skill published in the GitHub repository ChenShuo2004/cs-skills (204 stars, last pushed 18d ago), licensed MIT. It adds 84 tokens to every session and 1,062 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-07.
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