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 GongLingRui/screen-creative-skills --skill novel-truncatorgit clone --depth 1 https://github.com/GongLingRui/screen-creative-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/gonglingrui/screen-creative-skills/novel-truncator)<a href="https://agentmods.dev/skills/gonglingrui/screen-creative-skills/novel-truncator"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/novel-truncator/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/gonglingrui/screen-creative-skills/novel-truncator"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/novel-truncator.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 | $0.00030 | $0.00911 |
| Opus 5 | $0.00015 | $0.00456 |
| Sonnet 5 | $0.00006 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
novel-truncator 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.
This is a copy
81% identical to drama-evaluator — 143 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
文本截断工具（小说版）
功能
接收文本内容与最大长度限制，智能截断文本，保持内容完整性。
使用场景
- 对长篇小说文本进行预处理，使其符合长度限制。
- 在不破坏语义完整性的前提下，截取文本片段。
- 确保输入到其他智能体的文本不超过其处理能力。
截断原则
- 优先句子结束: 在句号、问号、感叹号处断开。
- 其次段落结束: 在空格、换行符处断开。
- 最后指定长度: 确保不超过设定的最大长度限制。
输入要求
- 文本内容: 待截断的原始文本（字符串）。
- 最大长度限制: 目标文本的最大字符数（整数）。
- 截断标记（可选）: 用于标识文本截断位置的字符串，如 "[...]"。
输出格式
【文本截断报告】
原始长度：[字符数]
截断后长度：[字符数]
截断位置：[位置描述，如：在第 X 句句号处]
截断后的文本：
[文本内容]
约束条件
- 截断过程应最大限度地保留原文的语义和语境完整性。
- 严格遵守最大长度限制。
- 避免在词语中间进行截断。
示例
参见 {baseDir}/references/examples.md 目录获取更多详细示例:
examples.md- 包含不同截断场景（如按句子、按段落、强制截断）的详细示例。
详细文档
参见 {baseDir}/references/examples.md 获取关于文本截断工具的详细指导与案例。
版本历史
| 版本 | 日期 | 变更 |
|---|---|---|
| 2.1.0 | 2026-01-11 | 优化 description 字段，使其更精简并符合命令式语言规范；优化功能、使用场景、截断原则、输入要求、输出格式的描述，使其更符合命令式语言规范；添加约束条件、示例和详细文档部分；模型更改为 opus。 |
| 2.0.0 | 2026-01-11 | 按官方规范重构 |
| 1.0.0 | 2026-01-10 | 初始版本 |
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.
- 12d ago First seen · 101 lines · 30 tokens per session scan A 5ce553d9a5ed
novel-truncator is a skill published in the GitHub repository GongLingRui/screen-creative-skills (402 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 911 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to drama-evaluator, differing in 143 lines, and is treated as a copy.
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