series-analyzer

series-analyzer is a skill for Claude Code from GongLingRui/screen-creative-skills. It costs 40 tokens per session (1,613 once invoked), scanned A, original, MIT.

A workflow for researching and analysing television series that have already aired. It gathers show information, searches online sources, examines episodes, and organises story and character findings into a report.

In plain words
What is it for?
Use it to analyse episode structure, plot points, themes, characters, relationships, audience feedback, or a series' potential for adaptation.
Why use it?
It brings scattered show details and narrative analysis into one structured result. This makes it easier to study how a series is written or assess a project.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; mentions Claude Code.

Good fit Use it to analyse episode structure, plot points, themes, characters, relationships, audience feedback, or a series' potential for adaptation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gonglingrui/screen-creative-skills/series-analyzer
Install

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.

Any agent
npx skills add GongLingRui/screen-creative-skills --skill series-analyzer
Clone the repo
git clone --depth 1 https://github.com/GongLingRui/screen-creative-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for series-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/series-analyzer/github.svg)](https://agentmods.dev/skills/gonglingrui/screen-creative-skills/series-analyzer)
Your own site
<a href="https://agentmods.dev/skills/gonglingrui/screen-creative-skills/series-analyzer"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/series-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.

agentmods 80×15 button for series-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/gonglingrui/screen-creative-skills/series-analyzer"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/series-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,613 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 13
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.01613
Opus 5 $0.00020 $0.00807
Sonnet 5 $0.00008 $0.00323
Haiku 4.5 $0.00004 $0.00161

Measured 12d ago against content hash 981f0aba27fc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

series-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 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.

category/series-analysis/series-analyzer/SKILL.md · 166 lines

How it starts

The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.

已播剧集分析与拉片专家

功能

分析已播电视剧的各个方面,包括剧集信息获取、拉片分析、故事五元素分析、联网搜索和结果整合。

使用场景

  • 深度分析已播电视剧的创作技法和情节设计。
  • 学习优秀剧集,为剧本创作提供灵感与参考。
  • 进行影视研究和教学,分析剧集成功或失败的原因。
  • 辅助剧本评估,提供全面的剧集市场反馈与内容分析。

核心功能

  1. 剧集信息获取: 获取剧集基础信息(如导演、演员、评分、分集剧情等)。
  2. 联网搜索: 运用网络搜索获取最新的剧集相关信息、观众评价和热点讨论。
  3. 拉片分析: 对各集进行情节点和戏剧功能分析,揭示叙事结构。
  4. 故事五元素分析: 全面分析剧集的题材类型、故事梗概、人物小传、人物关系、大情节点。
  5. 结果整合: 整合所有分析结果,生成结构化、专业的综合分析报告。

工作流程

graph TD
    A[接收用户输入] --> B{意图识别 - 判断是否包含剧集名称}
    B -- 是 --> C[剧名提取 - 精准提取电视剧剧名]
    C --> D(并发执行主要任务)
    D --> D1[剧集信息获取]
    D --> D2[联网搜索]
    D --> D3[故事五元素分析]
    D -- 完成 --> E[章节切分和拉片分析]
    E --> F[整合所有分析结果]
    F --> G[输出最终分析报告]

输入要求

  • 电视剧名称: 明确指定需要分析的电视剧名称。
  • 具体的分析需求(可选): 指定需要重点关注的分析方面,如"分析其人物关系"或"评估其商业价值"。

输出格式

【已播剧集分析报告】

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
一、剧集概览
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- 剧集名称:[名称]
- 导演:[导演]
- 主演:[主演列表]
- 类型:[类型]
- 集数:[集数]
- 评分:[评分,如豆瓣评分]
- 播出平台:[平台]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
二、故事五元素分析
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. 题材类型与创意提炼
   - 主导题材:[描述]
   - 核心创意:[描述]
2. 故事梗概
   - [200-500字的故事梗概]
3. 人物小传(主要角色)
   - [角色1名称]:[角色小传]
   - [角色2名称]:[角色小传]
4. 人物关系网络
   - 核心人物关系:[描述]
   - 主要关系线:[列表]
5. 大情节点
   - [情节点1]:[描述]
   - [情节点2]:[描述]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
三、拉片分析(示例章节)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
### [第 X 集名称]
- **关键情节点**: [描述]
- **戏剧功能**: [分析]
- **叙事手法**: [分析]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
四、联网搜索信息与市场反馈
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- **最新评价**: [总结观众评价和口碑]
- **热点讨论**: [分析社交媒体或论坛热点话题]
- **行业影响**: [评估剧集对行业的影响力]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
五、综合评价与建议
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
【总体评价】:[对剧集的整体评价]
【创作亮点】:[总结剧集的创作优势]
【提升空间】:[提出可改进的方面]
【行业参考价值】:[总结对行业或未来创作的参考价值]

约束条件

  • 确保剧集名称准确无误,以便获取正确信息。
  • 分析内容需忠实于剧集本身,不进行主观臆断或过度解读。
  • 报告内容需结构清晰,语言专业,易于理解。
  • 避免在输出中引入任何幻觉或不实信息。

示例

参见 {baseDir}/references/examples.md 目录获取更多详细示例:

  • examples.md - 包含不同类型剧集(如古装、都市、悬疑)的详细分析报告示例。

详细文档

参见 {baseDir}/references/examples.md 获取关于已播剧集分析的详细指导与案例。


版本历史

Read the full file on GitHub · 166 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 166 lines · 40 tokens per session scan A 981f0aba27fc

Subscribe to this mod's changes

series-analyzer is a skill published in the GitHub repository GongLingRui/screen-creative-skills (402 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 1,613 once invoked, about $0.0002 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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