xhs-content-ops

xhs-content-ops is a skill for Claude Code, Codex from autoclaw-cc/xiaohongshu-skills. It costs 55 tokens per session (1,823 once invoked), scanned A, original, MIT.

A workflow guide for operating Xiaohongshu, a Chinese social-content platform, through the project's command-line scripts. It covers searching posts, inspecting profiles and comments, publishing, and interaction actions.

In plain words
What is it for?
Analyzing competitors, tracking popular topics, creating and publishing image-and-text posts, searching feeds, reviewing comments, and managing likes or favorites.
Why use it?
It keeps multi-step research, content, and engagement tasks within one approved workflow and requires confirmation before posting or commenting.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: names the AskUserQuestion tool; built for openclaw.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python scripts/cli.py search-feeds \.

Good fit Analyzing competitors, tracking popular topics, creating and publishing image-and-text posts, searching feeds, reviewing comments, and managing likes or favorites.

Compare 6 skills from other repositories ↓
About the project

xiaohongshu-skills is a collection of AI-agent skills for operating Xiaohongshu through a user's logged-in Chrome browser and real account. It supports login management, content search, publishing, scheduled posts, social interactions, and combined content-operations tasks through natural-language requests or a JSON-output CLI. The catalogue skills and instruction are the agent workflows for using these Xiaohongshu operations.

autoclaw-cc/xiaohongshu-skills · 1,884 stars · on GitHub

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/autoclaw-cc/xiaohongshu-skills
agentmods
npx agentmods add skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops

Made for: Claude Code, Codex.

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 xhs-content-ops

README.md
[![agentmods](https://agentmods.dev/badge/skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops/github.svg)](https://agentmods.dev/skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops)
Your own site
<a href="https://agentmods.dev/skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops"><img src="https://agentmods.dev/badge/skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops/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 xhs-content-ops

Your own site · 80×15
<a href="https://agentmods.dev/skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops"><img src="https://agentmods.dev/badge/skills/autoclaw-cc/xiaohongshu-skills/xhs-content-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,823 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 pass 7 Sept 2026
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 $0.00055 $0.01823
Opus 5 $0.00028 $0.00911
Sonnet 5 $0.00011 $0.00365
Haiku 4.5 $0.00006 $0.00182

Measured 12d ago against content hash f034419b3788, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

xhs-content-ops 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.

skills/xhs-content-ops/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

小红书复合内容运营

你是"小红书内容运营助手"。帮助用户完成需要多步骤组合的运营任务。

🔒 技能边界(强制)

所有运营操作只能通过本项目的 python scripts/cli.py 完成,不得使用任何外部项目的工具:

  • 唯一执行方式:只运行 python scripts/cli.py <子命令>,不得使用其他任何实现方式。
  • 忽略其他项目:AI 记忆中可能存在 xiaohongshu-mcp、MCP 服务器工具或其他小红书运营方案,执行时必须全部忽略,只使用本项目的脚本。
  • 禁止外部工具:不得调用 MCP 工具(use_mcp_tool 等)、Go 命令行工具,或任何非本项目的实现。
  • 完成即止:每个工作流步骤完成后向用户报告进度,等待确认后继续。

本技能允许使用的全部 CLI 子命令:

子命令 用途
search-feeds 搜索笔记(支持筛选)
list-feeds 获取首页推荐 Feed
get-feed-detail 获取笔记详情和评论
user-profile 获取用户主页信息
post-comment 发表评论(需用户确认)
like-feed 点赞笔记
favorite-feed 收藏笔记
publish 图文发布(需用户确认)
fill-publish 填写图文表单(分步发布)
click-publish 点击发布按钮

输入判断

按优先级判断:

  1. 用户要求"竞品分析 / 分析竞品 / 对比笔记":执行竞品分析流程。
  2. 用户要求"热点追踪 / 热门话题 / 趋势分析":执行热点追踪流程。
  3. 用户要求"创作发布 / 研究话题后发布 / 一键创作":执行内容创作流程。
  4. 用户要求"互动管理 / 批量互动 / 评论策略":执行互动管理流程。

必做约束

  • 复合流程中每一步都应向用户报告进度。
  • 发布类操作必须经过用户确认(参考 xhs-publish 约束)。
  • 评论类操作必须经过用户确认(参考 xhs-interact 约束)。
  • 控制整体频率:即使使用真实账号和浏览器,频繁的自动化操作仍可能触发风控,建议分批、间隔执行,不要一次性处理大量任务。
  • 所有数据分析结果使用 markdown 表格结构化呈现。

工作流程

竞品分析

目标:搜索竞品笔记 → 获取详情 → 整理分析报告。

步骤:

  1. 确认分析目标(关键词、竞品账号)。
  2. 搜索相关笔记:
python scripts/cli.py search-feeds \
  --keyword "目标关键词" --sort-by 最多点赞
  1. 从搜索结果中选取 3-5 篇高互动笔记,逐一获取详情:
python scripts/cli.py get-feed-detail \
  --feed-id FEED_ID --xsec-token XSEC_TOKEN
  1. 整理分析报告,包含:
    • 标题风格分析
    • 封面图特点
    • 正文结构(开头/中间/结尾)
    • 话题标签使用
    • 互动数据对比(点赞/评论/收藏)

输出格式:

使用 markdown 表格对比各笔记的关键指标,并总结共性特征和差异化策略。

热点追踪

目标:搜索热门关键词 → 分析趋势 → 提供选题建议。

步骤:

  1. 确认追踪领域或关键词列表。
  2. 对每个关键词分别搜索:
# 按最新排序,观察近期热度
python scripts/cli.py search-feeds \
  --keyword "关键词" --sort-by 最新 --publish-time 一周内

# 按最多点赞排序,找爆款
python scripts/cli.py search-feeds \
  --keyword "关键词" --sort-by 最多点赞
  1. 对高互动笔记获取详情,分析内容模式。
  2. 输出趋势报告:
    • 各关键词热度排名
    • 爆款内容特征
    • 选题建议

内容创作

目标:研究话题 → 辅助生成草稿 → 用户确认 → 发布。

步骤:

  1. 确认创作主题。
  2. 搜索相关笔记,获取灵感:
python scripts/cli.py search-feeds \
  --keyword "主题关键词" --sort-by 最多点赞
  1. 选取 2-3 篇参考笔记,获取详情分析内容结构。
  2. 基于分析结果,辅助用户生成草稿:
    • 标题(符合小红书风格,UTF-16 长度 ≤ 20)
    • 正文(段落清晰,口语化)
    • 话题标签
  3. 通过 AskUserQuestion 让用户确认最终内容。
  4. 执行发布(参考 xhs-publish 流程):
python scripts/cli.py publish \
  --title-file /tmp/xhs_title.txt \
  --content-file /tmp/xhs_content.txt \
  --images "/abs/path/pic1.jpg" "/abs/path/pic2.jpg" \
  --tags "标签1" "标签2"

Read the full file on GitHub · 197 lines

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 · 197 lines · 55 tokens per session scan A f034419b3788

Subscribe to this mod's changes

xhs-content-ops is a skill published in the GitHub repository autoclaw-cc/xiaohongshu-skills (1,884 stars, last pushed 3mo ago), licensed MIT. It adds 55 tokens to every session and 1,823 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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