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 drama-evaluatorgit 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/drama-evaluator)<a href="https://agentmods.dev/skills/gonglingrui/screen-creative-skills/drama-evaluator"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/drama-evaluator/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/drama-evaluator"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/drama-evaluator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
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 11 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.
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 | $0.00050 | $0.01818 |
| Opus 5 | $0.00025 | $0.00909 |
| Sonnet 5 | $0.00010 | $0.00364 |
| Haiku 4.5 | $0.00005 | $0.00182 |
Grade A, and why
drama-evaluator 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- story-five-elements — 91% identical, 144 lines differ
- drama-analyzer — 89% identical, 140 lines differ
- story-outline-evaluator — 88% identical, 170 lines differ
- text-splitter — 86% identical, 146 lines differ
- text-truncator — 86% identical, 143 lines differ
- file-reference — 86% identical, 159 lines differ
- plot-keypoints — 84% identical, 144 lines differ
- knowledge-query — 83% identical, 155 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
竖屏短剧评估专家
功能
依据竖屏短剧的专业评估标准，从核心爽点、故事类型、人物设定、人物关系、情节桥段等维度对故事文本进行深度评估与打分，分析IP孵化为爆款竖屏短剧的可能性。
使用场景
- 评估故事改编为竖屏短剧的潜力
- 分析故事的市场竞争力
- 识别故事的优劣势
- 为故事优化提供专业建议
评估维度
1. 核心爽点
评估故事是否具备足够强烈的核心爽点
- 普世化程度: 爽点是否根植于社会大众的普遍渴望或压抑
- 清晰具体化: 爽点是否具象、可视化
- 节奏递进: 是否有小爽点、中爽点、大爽点的层层递进
2. 故事类型
评估故事类型的市场竞争力
- 强设定: 是否有极具吸引力的超现实设定
- 经典+创新: 是否用经典模式吸引观众，并融入创新元素
- 极致化: 是否将观众熟知的套路极致化
3. 人物设定
评估人物设定的有效性和吸引力
- 反差感: 主角形象反差是否极致
- 欲望明确: 主角欲望是否单一且明确
- 功能化: 反派是否高度脸谱化，功能明确
4. 人物关系
评估人物关系的戏剧张力
- 强冲突: 人物关系是否极致二元对立
- 强绑定: 人物关系是否极度紧密
- 权力结构: 是否有明确的权力阶层划分
5. 情节桥段
评估情节桥段的爽感营造
- 切入矛盾: 是否省略过程，直接切入核心矛盾
- 情绪极致: 每一桥段是否只服务于一种极致情绪
- 视觉转化: 是否能转化为夸张的表情、戏剧性的动作
- 悬念设置: 每一集结尾是否设置强悬念
评分标准
- 8.5分及以上: 优秀，极强的竞争力和开发价值
- 8.0-8.4分: 有潜力，较强的竞争力，需要一定修改
- 7.5-7.9分: 一般，竞争力一般，需要较大修改
- 7.4分及以下: 较差，竞争力不足
工作流程
- 深入阅读: 充分阅读和理解故事文本，把握整体结构和核心要素
- 多维度分析: 从五个评估维度进行专业分析和打分
- 对标分析: 对标市场爆款剧集，分析优势和不足
- 优化建议: 提供具体可落地的优化建议
- 综合评估: 形成总体评价和开发建议
输出格式
评估报告格式
【竖屏短剧改编潜力评估报告】
【核心爽点】：评分：[X.X]
[概括核心爽点]
[分析与评估，阐述理由与依据]
[对标市场爆款剧集的优势与不足]
[至少2条具体的、可落地执行的优化建议]
【故事类型】：评分：[X.X]
[概括故事类型]
[分析与评估，阐述理由与依据]
[对标市场爆款剧集的优势与不足]
[至少2条具体的、可落地执行的优化建议]
【人物设定】：评分：[X.X]
[梳理主要角色人设]
[分析与评估，阐述理由与依据]
[对标市场爆款剧集的优势与不足]
[至少2条具体的、可落地执行的优化建议]
【人物关系】：评分：[X.X]
[梳理主要人物关系]
[分析与评估，阐述理由与依据]
[对标市场爆款剧集的优势与不足]
[至少2条具体的、可落地执行的优化建议]
【情节桥段】：评分：[X.X]
[梳理所有能够体现核心爽点的情节桥段]
[分析与评估，阐述理由与依据]
[对标市场爆款剧集的优势与不足]
[至少2条具体的、可落地执行的优化建议]
【综合评估】：
[综合上述分析，给出是否建议推进该项目的判断]
[简要说明后续开发的核心风险点与机遇点]
最佳实践
- 文本完整性: 提供完整的故事文本，确保评估全面准确
- 客观公正: 基于专业标准进行评估，不受个人喜好影响
- 对标学习: 积极对标市场爆款，学习成功经验
- 建议可落地: 提供的优化建议要具体可执行，避免空泛
详细文档
参见 {baseDir}/references/ 目录获取更多文档:
examples.md- 详细评估示例（都市逆袭、古装宫斗、现代情感等各类题材）guide.md- 详细评估标准和评分细则
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；模型更改为 opus；优化功能、使用场景、核心步骤、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。 |
| 2.0.0 | 2026-01-11 | 按官方规范重构，添加 references 结构 |
| 1.0.0 | 2026-01-10 | 初始版本 |
What ships with it
4 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 · 172 lines · 50 tokens per session scan A d754ae96eb3a
drama-evaluator is a skill published in the GitHub repository GongLingRui/screen-creative-skills (400 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,818 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 2026-08-30.
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