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 ip-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/ip-evaluator)<a href="https://agentmods.dev/skills/gonglingrui/screen-creative-skills/ip-evaluator"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/ip-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/ip-evaluator"><img src="https://agentmods.dev/badge/skills/gonglingrui/screen-creative-skills/ip-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 | $0.00042 | $0.01371 |
| Opus 5 | $0.00021 | $0.00685 |
| Sonnet 5 | $0.00008 | $0.00274 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
ip-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 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.
What it actually says
IP评估专家
功能
梳理IP网络信息，从市场潜力、创新属性、内容亮点等多个维度进行分析和打分，为IP改编价值提供参考意见。
使用场景
- 评估IP改编价值
- 网络小说版权购买前的尽职调查
- IP市场前景分析
- 内容投资决策参考
评估维度
1. 市场潜力
- 受众适合度: 判断IP是否贴合目标受众
- 讨论热度: 判断IP能否引起大众共鸣和讨论
- 稀缺性: 分析IP内容的独特性与稀缺性
- 播放数据: 分析IP在市场上的表现前景
2. 创新属性
- 核心选点: 判断IP最核心的故事选点是否新鲜独特
- 故事概念: 判断IP的故事概念是否突出鲜明
- 故事设计: 判断IP在故事设计上是否特点鲜明
3. 内容亮点
- 主题立意: 分析IP的主题立意是否清晰明确
- 故事情境: 判断IP的故事情境是否有张力和戏剧性
- 人物设定: 判断IP的主要人物设定是否新颖有特点
- 人物关系: 判断IP的主要人物关系是否出彩鲜明
- 情节桥段: 判断IP的情节桥段是否有戏剧张力和可看性
评分标准
- 8.5分及以上: 优秀，影视改编价值很大，值得推荐
- 8.0-8.4分: 有潜力，需要一定程度的修改才能达到优秀
- 7.5-7.9分: 一般，中规中矩，竞争力一般
- 7.4分及以下: 较差，竞争力不足，不建议推进
工作流程
- 梳理IP相关信息，总结基本内容
- 根据评估框架对IP的各个维度进行分析
- 对每个维度进行严谨、细致的打分
- 形成总体评价与总评分
- 给出是否推进IP开发的建议
输出格式
【IP评估报告】
一、基本信息
- 故事梗概：[IP的故事梗概]
- 故事主题：[IP的故事主题]
- 人物关系：[主要人物及人物关系]
- 市场表现：[口碑、评分、粉丝基础等]
- 作者信息：[作者过往作品及口碑]
二、市场潜力分析
- 受众适合度：[分析+评分]
- 讨论热度：[分析+评分]
- 稀缺性：[分析+评分]
- 播放数据：[分析+评分]
三、创新属性分析
- 核心选点：[分析+评分]
- 故事概念：[分析+评分]
- 故事设计：[分析+评分]
四、内容亮点分析
- 主题立意：[分析+评分]
- 故事情境：[分析+评分]
- 人物设定：[分析+评分]
- 人物关系：[分析+评分]
- 情节桥段：[分析+评分]
五、总体评价
- [总体分析与评价]
- 总评分：[X.X分]
六、建议
- [是否建议推进IP开发的具体建议]
详细文档
参见 {baseDir}/references/ 目录获取更多文档:
guide.md- IP评估完整指南，包括评估框架、评分标准、信息获取技巧和注意事项examples.md- 详细评估示例
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；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
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.
- 13d ago First seen · 143 lines · 42 tokens per session scan A ab10a2142a4a
ip-evaluator is a skill published in the GitHub repository GongLingRui/screen-creative-skills (402 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 1,371 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.
Other skills, from other repositories
workers-best-practices
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
create-custom-grader
Use when converting an existing benchmark, rubric, verifier, task YAML/JSON, or domain check into SkillEvaluator BYOG/BYOT custom evaluation.
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.