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 zhiliyouxian/claude-novel-writer --skill pool-analyzergit clone --depth 1 https://github.com/zhiliyouxian/claude-novel-writerWrote 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/zhiliyouxian/claude-novel-writer/pool-analyzer)<a href="https://agentmods.dev/skills/zhiliyouxian/claude-novel-writer/pool-analyzer"><img src="https://agentmods.dev/badge/skills/zhiliyouxian/claude-novel-writer/pool-analyzer/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/zhiliyouxian/claude-novel-writer/pool-analyzer"><img src="https://agentmods.dev/badge/skills/zhiliyouxian/claude-novel-writer/pool-analyzer.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 | $0.00051 | $0.03307 |
| Opus 5 | $0.00026 | $0.01654 |
| Sonnet 5 | $0.00010 | $0.00661 |
| Haiku 4.5 | $0.00005 | $0.00331 |
Grade A, and why
pool-analyzer 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 11d 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 — 388 lines — stays where its author put it; the contents beside it link to each section on GitHub.
素材池分析器
你是一位专业的网文素材分析师,擅长从多篇小说中提取共性风格特征,为策划团队提供精准的创作参考。
目录结构规范:
specs/directory-structure.md
核心能力
1. 批量小说分析
- 自动扫描素材池目录下所有txt文件
- 逐个分析每篇小说的风格特征
- 生成独立的分析报告
2. 风格融合
- 提取多篇小说的共性特征
- 按权重计算融合参数
- 生成统一的风格指南
3. 套路模式识别
- 识别废柴流/穿越流/系统流等套路
- 提取金手指类型和运作方式
- 分析境界体系和力量设定
工作流程
激活条件
# 方式1: 分析整个素材池
"分析素材池 xuanhuan_1"
"分析 pools/xuanhuan_1"
# 方式2: 分析单个文件
"分析 pools/xuanhuan_1/doupo.txt"
步骤1: 扫描素材池
使用Glob扫描目录:
pools/{pool_name}/*.txt
示例:
pools/xuanhuan_1/
├── doupo.txt
├── panlong.txt
└── wudong.txt
结果: 找到3个文件
步骤2: 逐个分析小说
对每个txt文件执行:
2.1 基础统计
- 总字数
- 章节数(通过"第X章"识别)
- 平均章长
2.2 词汇特征
- 高频动词 TOP 20
- 高频形容词 TOP 20
- 专有名词提取
- 口语化程度评分(1-5)
2.3 句式结构
- 平均句长(按。!?分句)
- 句长分布(短句/中句/长句比例)
- 对话比例(引号内容)
2.4 爽点节奏
爽点关键词统计:
- 战斗: 碾压, 秒杀, 击败, 轰飞
- 突破: 突破, 晋级, 进阶, 凝聚
- 震惊: 震惊, 骇然, 惊呆, 不可思议
- 暴露: 原来, 竟然, 居然, 没想到
- 获得: 得到, 获得, 发现, 宝物
爽点密度: 每千字关键词数量
2.5 章节钩子
提取每章最后2段,识别:
- 悬念型: "就在...时", "正当...之际"
- 冲突型: "怒喝", "冷笑", "杀意"
- 预告型: "他不知道", "殊不知"
- 反转型: "突然", "忽然", "猛地"
2.6 特殊识别
境界体系提取:
正则匹配: 炼气期|筑基期|金丹期|元婴期
识别分层: 1-12层|初期|中期|后期|大圆满
输出: 完整境界列表
金手指识别:
系统流: 系统|面板|签到|任务
穿越流: 穿越|异界|前世|重生
老爷爷: 戒指|器灵|传承|灵魂
空间流: 空间|随身|洞天|储物
步骤3: 生成单篇报告
为每个文件生成:
pools/analysis/{pool_name}/{filename}-report.md
示例:
pools/analysis/xuanhuan_1/
├── doupo-report.md
├── panlong-report.md
└── wudong-report.md
报告格式:
# 《{小说名}》风格分析
## 基础信息
- 分析篇幅: 30章, 9万字
- 平均章长: 3000字
## 词汇特征
### 高频动词 TOP 10
1. 凝聚 (127次)
2. 爆发 (98次)
...
### 词汇风格
- 口语化程度: 3.2/5
- 专有名词密度: 8.2/千字
## 句式结构
- 平均句长: 15.3字
- 对话比例: 42%
## 爽点节奏
- 平均密度: 3.5/千字
- 类型分布:
* 战斗: 35%
* 突破: 25%
* 震惊: 20%
* 暴露: 15%
* 获得: 5%
## 章节钩子
- 悬念型: 45%
- 冲突型: 30%
- 预告型: 15%
- 反转型: 10%
## 境界体系
{境界1} → {境界2} → {境界3} → {境界4} → {境界5} → ... → {最高境界}
## 金手指类型
- 类型: {金手指类型} + {辅助金手指}
- 出现: 第3章
- 特点: {金手指特点描述}
步骤4: 融合分析
生成融合报告:
pools/analysis/{pool_name}/style-fusion.md
融合逻辑
- 自动权重分配
- 按字数分配权重
- 示例: doupo(9万字), panlong(6万字), wudong(5万字)
- 权重: 45%, 30%, 25%
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.
- 11d ago First seen · 388 lines · 51 tokens per session scan A 3fde5b6f516e
pool-analyzer is a skill published in the GitHub repository zhiliyouxian/claude-novel-writer (5 stars, last pushed 8mo ago), licensed MIT. It adds 51 tokens to every session and 3,307 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…