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-summarizergit 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-summarizer)<a href="https://agentmods.dev/skills/gonglingrui/screen-creative-skills/novel-summarizer"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/novel-summarizer/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-summarizer"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/novel-summarizer.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 | $0.00035 | $0.00910 |
| Opus 5 | $0.00017 | $0.00455 |
| Sonnet 5 | $0.00007 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
novel-summarizer 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.
This is a copy
78% identical to drama-evaluator — 146 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
故事大纲生成专家(小说版)
功能
阅读理解故事文本,总结成流畅的故事大纲,字数控制在500-800字之间。
使用场景
- 对小说文本进行初筛选,快速了解故事梗概。
- 生成规范化的故事大纲,为后续创作提供基础。
- 快速掌握故事的核心人物、关系、事件和情节。
核心能力
- 准确总结: 准确总结故事文本中的人物、人物关系、人物行动与事件情节。
- 叙事转换: 以第三人称视角进行准确总结。
- 复杂关系处理: 理解并准确总结复杂的人物身份与人物关系。
- 语言表达: 运用优美准确的语言总结故事梗概。
工作流程
- 深入阅读: 深入阅读故事文本,准确理解人物、人物关系与事件情节。
- 总结生成: 根据阅读内容,将故事文本总结为一篇行文流畅的故事大纲。
约束条件
- 字数控制: 严格控制总结字数在500-800字之间。
- 内容准确性: 严格按照文本原文总结,不进行自行创作或改编。
- 格式要求: 直接输出总结文本内容,不带任何标题。
输出格式
直接输出流畅的文字总结故事大纲。
示例
参见 {baseDir}/references/examples.md 目录获取更多详细示例:
examples.md- 详细总结示例(都市爱情、古言、重生复仇等不同类型)
详细文档
参见 {baseDir}/references/examples.md 获取关于小说总结的详细指导与案例。
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;添加 allowed-tools (Read) 和 model (opus) 字段;优化功能、使用场景、核心能力、工作流程、约束条件、输出格式的描述,使其更符合命令式语言规范;添加示例和详细文档部分。 |
| 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
1 file 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.
- 13d ago First seen · 94 lines · 35 tokens per session scan A 44a64f678580
novel-summarizer is a skill published in the GitHub repository GongLingRui/screen-creative-skills (402 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 910 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to drama-evaluator, differing in 146 lines, and is treated as a copy.
Other skills, from other repositories
workers-best-practices
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
find-journalists
Build, refine, dedupe, and enrich small fit-checked journalist lists for newsjack campaigns. Uses the newsjack CLI (preferred) or the medialyst MCP for news search and journalist enrichment, and falls back to a best-effort local mode with no verified contacts; the agent owns how returned data is organized.
story-origin-check
Recover the first public timestamp and canonical major coverage for a newsjacking signal, then decide whether newer coverage is the same story, a different story, or a materially new development.
annotating-task-lineage
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
managing-astro-local-env
Manage local Airflow environment with Astro CLI (Docker and standalone modes). Use when the user wants to start, stop, or restart Airflow, view logs, query the Airflow API, troubleshoot, or fix environment issues. For project setup, see setting-up-astro-project.