TuYv
TuYv/ccpm
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/TuYv/ccpm --skill golden-jupyter-dir命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when auditing a paid ad account for incremental contribution, wasted spend, or measurement integrity before scaling; runs a typed 20-item ROAS profile with verified vetoes and a SHIP/FIX/BLOCK/UNDECIDED gate on own exported data. Not for campaign structure design — use campaign-architect; not for creative production — use ad-creative-builder. 付费广告账户审计/ROAS评分
Use when the user asks to "write ad copy", "generate RSA headlines", or "build ad creative at volume"; produces ad units — RSA headlines/descriptions, hooks, and an angle matrix — message-matched to the destination landing page. Not for scoring an ad account — use ad-account-auditor; not for the post-click page — use landing-optimizer; not for organic articles — use content-writer. 广告创意/广告文案/RSA标题
Use when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"; produces a hypothesis, variant matrix, sample-size/duration/power plan, and a documented effect/uncertainty read from own exported results. It applies only a precommitted owner-approved action rule; the statistical helper never chooses a business action. Not for producing variants — use ad-creative-builder; not for reading back one shipped change — use paid-measurement-loop. 广告AB测试设计/实验设计/显著性判定/增效测试
基于 SOC 职业分类
正在显示 SKILL.md
| name | golden-jupyter-dir |
| description | Use when testing the golden_jupyter_dir golden build |
Use when testing the golden_jupyter_dir golden build
Kernel: Python 3
Language: python 3.11.4
Use this skill when you need to:
Total Sections: 5
Content Breakdown:
Main topics covered in this notebook
Major Topics:
Subtopics:
3 package(s) imported
numpypandassklearnCommon documentation patterns found:
Getting Started (1 sections):
Modeling (1 sections):
High-quality code cells from notebook
Example 1 (Quality: 5.0/10):
pip install pandas
Example 1 (Quality: 9.5/10):
def long_example():
x0 = 0
x1 = 1
x2 = 2
x3 = 3
x4 = 4
x5 = 5
x6 = 6
x7 = 7
x8 = 8
x9 = 9
x10 = 10
x11 = 11
x12 = 12
x13 = 13
x14 = 14
x15 = 15
x16 = 16
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x24 = 24
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x33 = 33
x34 = 34
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x36 = 36
x37 = 37
x3
...
In [2] (Quality: 7.5/10):
import pandas as pd
df = pd.read_csv('data.csv')
df.head()
Example 3 (Quality: 2.0/10):
%timeit broken()
Language Breakdown:
Reference Files:
references/section_s2-s2.md - Data Loadingreferences/section_s5-s5.md - Evaluationreferences/section_s1-s1.md - Setupreferences/section_s3-s4.md - OtherSee references/index.md for complete notebook structure.
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