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 guyulong/cn-agent-skills --skill xiaohongshu-analyzegit clone --depth 1 https://github.com/guyulong/cn-agent-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/guyulong/cn-agent-skills/xiaohongshu-analyze)<a href="https://agentmods.dev/skills/guyulong/cn-agent-skills/xiaohongshu-analyze"><img src="https://agentmods.dev/badge/skills/guyulong/cn-agent-skills/xiaohongshu-analyze/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/guyulong/cn-agent-skills/xiaohongshu-analyze"><img src="https://agentmods.dev/badge/skills/guyulong/cn-agent-skills/xiaohongshu-analyze.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 | $0.00013 | $0.00549 |
| Opus 5 | $0.00006 | $0.00275 |
| Sonnet 5 | $0.00003 | $0.00110 |
| Haiku 4.5 | $0.00001 | $0.00055 |
Grade A, and why
xiaohongshu-analyze 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.
What it actually says
小红书数据分析
使用场景
分析小红书内容数据，优化发布策略。
数据维度
- 笔记类型（图文/视频）
- 互动数据（点赞、收藏、评论）
- 发布时间
- 标签使用
- 封面设计风格
分析脚本模板
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS']
# 加载数据
df = pd.read_csv('xiaohongshu_data.csv')
# 1. 最佳发布时间分析
df['hour'] = pd.to_datetime(df['发布时间']).dt.hour
hourly_engagement = df.groupby('hour')['互动量'].mean()
plt.figure(figsize=(12, 6))
hourly_engagement.plot(kind='bar')
plt.title('各时段平均互动量')
plt.xlabel('发布时段')
plt.ylabel('平均互动量')
plt.show()
# 2. 热门标签分析
from collections import Counter
all_tags = []
for tags in df['标签'].dropna():
all_tags.extend([t.strip() for t in tags.split(',')])
tag_counts = Counter(all_tags).most_common(20)
# 3. 内容类型对比
type_stats = df.groupby('类型').agg({
'点赞': 'mean',
'收藏': 'mean',
'评论': 'mean'
}).round(0)
print(type_stats)
# 4. 爆款特征分析
viral = df[df['点赞'] > df['点赞'].quantile(0.9)]
print(f"爆款笔记特征：")
print(f" 平均标题长度: {viral['标题'].str.len().mean():.0f}字")
print(f" 平均图片数: {viral['图片数'].mean():.0f}张")
print(f" 最常用标签: {Counter([t for tags in viral['标签'] for t in tags.split(',')]).most_common(5)}")
运营建议
- 最佳发布时间：工作日晚7-10点，周末全天
- 标签数量：5-10个为宜
- 标题长度：15-25字
- 图片数量：6-9张最佳
- 首图要有吸引力
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 · 72 lines · 13 tokens per session scan A f795d54d5f7b
xiaohongshu-analyze is a skill published in the GitHub repository guyulong/cn-agent-skills (3 stars, last pushed 3mo ago), licensed MIT. It adds 13 tokens to every session and 549 once invoked, about $0.0001 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.
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card-twitter
Twitter quote or data card designed to pair with a post.
card-xiaohongshu
Xiaohongshu-style knowledge cards, arranged as a swipeable multi-card carousel.
poster-hero
Vertical poster or Moments-style share image with strong visual impact.
deck-ljg-present
A presentation template that converts an outline or Markdown document into large, poster-like slides while keeping the original wording, order, and content. It uses black, red, or yellow colour themes and assigns document elements to slide layouts.