Xiaohongshu Auto Posting logo

Xiaohongshu Auto Posting

OrganizationPopular
browser-act
xiaohongshu-auto-posting

Automates the complete Xiaohongshu (XHS / Little Red Book) content operation workflow: pain-point topic collection → style case collection → topic selection → content writing → publishing → performance tracking. Use when user mentions xiaohongshu auto posting, xhs auto post, little red book posting, xiaohongshu post, xiaohongshu auto posting, xiaohongshu content operations, xhs content marketing, post to xiaohongshu, publish on xhs, post on xiaohongshu, xiaohongshu promotion, xiaohongshu operations, xiaohongshu automation, xhs automation, track xhs performance, track xiaohongshu performance, xiaohongshu data tracking, xiaohongshu analytics, switch xhs account, update xhs keywords.

Overview

Publisherbrowser-act
Repositoryskills
Skill namexiaohongshu-auto-posting
Stars
6.1K
Forks
310
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by browser-act on GitHub. Read the source before you install it.

Installation

Install the Xiaohongshu Auto Posting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/browser-act/skills/tree/main/solutions/social-listening/xiaohongshu-auto-posting
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/browser-act/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/solutions/social-listening/xiaohongshu-auto-posting .claude/skills/xiaohongshu-auto-posting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Xiaohongshu Auto Posting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Xiaohongshu Auto Posting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Xiaohongshu Auto Posting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Xiaohongshu Auto-Posting (XHS Auto-Posting)

End-to-end Xiaohongshu content operation: topic discovery → style reference → writing → publishing → performance tracking.

Language

All process output to user (progress updates, questions, status notifications) follows the user's language.

Objective

Automate a complete Xiaohongshu content operation cycle for a configured product: discover high-engagement pain-point topics via search, collect writing style references, generate platform-native content aligned with the style fingerprint, publish with user approval, and track post-publish performance metrics.

Prerequisites

  • Xiaohongshu creator center (creator.xiaohongshu.com) already logged in on a configured stealth browser (browser ID stored in session_state.json)
  • workspaces/xhs-posting/ workspace (auto-created on first run)

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

2. Login Verification

Covered in Phase 0.5 — login check is deferred until session state is loaded (Phase 0.2) and the browser is opened. If the user triggers Phase 6 directly (standalone tracking mode), still complete Phase 0.2 and Phase 0.5 first.

3. Session Lifecycle

Each execution generates a unique session name (xhs-<YYYYMMDD>-<HHMM>) to avoid cross-conversation conflicts. The session is opened once in Phase 0.5 and closed at the end of the workflow. All commands in this Skill reference the session via $SESSION — the actual name is determined at runtime.


Runtime Workspace

All runtime files (config, drafts, publish records, screenshots) are written to the user's working directory (CWD) under workspaces/xhs-posting/. This directory is auto-created on first run.

workspaces/xhs-posting/          ← relative to user CWD
├── session_state.json            # account & product config
├── config/
│   └── keywords.json             # keyword pool
├── tracking/
│   └── published.json            # published note records
├── reports/                      # performance reports
└── <YYYY-MM-DD>/                 # per-run date directory
    ├── topics/
    ├── selected_topics.json
    ├── style_fingerprint.json
    ├── drafts/
    └── replies/

Quick Start

/xiaohongshu-auto-posting

Standalone triggers (jump directly to the corresponding phase, skipping Phase 0–4):

  • "track performance" / "see data" / "data collection" → Phase 6 performance tracking
  • "settings" / "switch account" / "update keywords" → Phase 0 configuration wizard

Operational Rules

Three rules that apply to every browser-act session. Violating any one causes silent failures or long hangs. Full details in references/phase5-publish.md §0.

Rule 1 — Never pipe browser-act output directly

Redirect state output to a temp file, then grep the file. Piping may background the command when output contains multi-byte characters, making the result unavailable synchronously.

bash
# WRONG
browser-act --session $SESSION state | grep "Publish"
# CORRECT
browser-act --session $SESSION state > /tmp/s.txt 2>&1 && grep "Publish" /tmp/s.txt

Rule 2 — Escape all non-ASCII content before passing to eval --stdin

Non-ASCII literals (Chinese, emoji) in JS source cause encoding errors in the subprocess pipeline. Escape to \uXXXX first:

python
def to_ascii_js(s):
    return ''.join(r'\u{:04x}'.format(ord(c)) if ord(c) > 127 else c for c in s)
js = to_ascii_js(open('tmp/script.js', encoding='utf-8').read())
subprocess.run([..., 'eval', '--stdin'], input=js.encode('ascii'), ...)

Rule 3 — Use click, not eval, for actions that trigger navigation

eval hangs (~30 s timeout) when the callback causes a page redirect. Always use browser-act --session $SESSION click <index> for the publish button, then verify the URL immediately after.


Phase 0 — Initialization

0.1 Tool Check

Run browser-act browser list:

  • Command not found → run uv tool upgrade browser-act-cli || uv tool install browser-act-cli --python 3.12, then continue
  • Returns normally → continue

0.2 Session State Load

Read workspaces/xhs-posting/session_state.json:

  • File does not exist, or browser_id is empty → proceed to 0.3 First-time Configuration Wizard
  • File exists and browser_id is non-empty → load and display current config, ask user to confirm or modify

session_state.json structure:

json
{
  "browser_id": "",
  "account": {
    "nickname": "",
    "profile": ""
  },
  "product": {
    "name": "",
    "tagline": "",
    "url": "",
    "install_cmd": ""
  },
  "keywords_file": "workspaces/xhs-posting/config/keywords.json",
  "posting": {
    "daily_limit": 1,
    "min_interval_hours": 6,
    "last_posted_at": null
  }
}

0.3 First-time Configuration Wizard (when file does not exist or browser_id is empty)

Step 1: Browser Selection

Run browser-act browser list, then:

When existing browsers are present: display all browsers (id / name / type / desc / proxy), ask the user which one to use for posting:

Existing browsers:

#1  id=xxx  name="xxx"  type=stealth  desc="xxx"  proxy=XX
#2  id=xxx  name="xxx"  type=chrome   desc="xxx"

Select a browser number to use for posting, or enter "new" to create one:
>

When no browsers exist, or user enters "new":

  1. Load advanced guide: browser-act get-skills advanced
  2. Ask the user:
    • Browser name (custom, e.g. "xhs-poster")
    • Proxy requirements (needed? region or custom URL?)
  3. Create the browser following the advanced guide's Confirmation Gate protocol
  4. Record the new browser ID, continue to Step 2

Step 2: Product & Keyword Configuration

Ask the user:

  1. Product name (brand keyword, used in tags and body)
  2. Product tagline (one-sentence description)
  3. Product URL (website / GitHub / App Store, etc.)
  4. Install / usage command (optional; for SaaS products, enter the core operation path)
  5. Initial keywords (1–5, comma-separated, used for topic search)

Write to workspaces/xhs-posting/session_state.json and workspaces/xhs-posting/config/keywords.json, then continue to Phase 0.4.

0.4 Account Safety Check

  • Read posting.last_posted_at from session_state.json
  • If less than min_interval_hours have passed since the last post, notify the user and ask whether to force continue
  • If today's post count ≥ daily_limit, notify the user

0.5 Browser Launch & Login Verification

Generate a unique session name for this execution:

$SESSION = "xhs-<YYYYMMDD>-<HHMM>"   # e.g., xhs-20260611-1430

Open the browser using the stored browser_id:

bash
browser-act --session $SESSION browser open <browser_id> https://creator.xiaohongshu.com/new/home
browser-act --session $SESSION wait stable

Verify login: check that the current URL stays on creator.xiaohongshu.com (not redirected to the login page) and that a user avatar / nickname element is present in state.

  • Not logged in → instruct the user to scan the QR code in --headed mode:
    bash
    browser-act --session $SESSION browser open <browser_id> https://creator.xiaohongshu.com/new/home --headed
    Wait for the user to confirm login, then continue.

Phase 1 — Pain-Point Topic Collection

Full execution parameters: references/phase1-topic-collection.md.

Overview:

  1. Keyword rotation: read the next keyword from config/keywords.json (sequential mode)
  2. XHS search: search the keyword, sort by hottest, collect metadata for the top 10–15 notes
  3. Listing Pass: extract shallow fields directly from search results (title, likes, collects, comments, snippet)
  4. Engagement scoring: score = likes + collects × 2 + comments × 1.5
  5. Top 5 Deep: click into the top 5 notes and extract deep fields (key quote, pain description, topic tags)
  6. Generate report: output workspaces/xhs-posting/<date>/topics/TOPICS_<kw-slug>.md
  7. Display Top 5 and wait for user selection

Phase 2 — Top Case Collection (Style Reference)

Full execution parameters: references/phase2-case-collection.md.

Overview:

  1. Use the same search results as Phase 1; no additional request needed
  2. Collect full text from the top 3 high-engagement notes (title + body + tags)
  3. Analyze writing style: opening type, paragraph rhythm, emoji density, topic tag strategy
  4. Generate Style Fingerprint
  5. Write style fingerprint to workspaces/xhs-posting/<date>/style_fingerprint.json

Phase 2 runs in parallel with Phase 1 (shared search results, different analysis angle).


Phase 3 — Topic Selection

After displaying the Phase 1 Top 5, ask the user:

Here are the Top 5 topics. Which ones do you want to write?
(enter numbers like "1 3"; "all" for all; "skip" to pass today)
>

On receiving the reply:

  1. Write the selected topic list to workspaces/xhs-posting/<date>/selected_topics.json
  2. For each selected topic, confirm the publish strategy (publish now / save as draft)

If the user enters "skip" → end this run without advancing last_index.


Phase 4 — Content Writing

Full writing guidelines: references/phase4-writing.md.

Write one draft per selected topic, show a preview after each draft and wait for user approval before moving to the next.

Writing framework: Pain resonance → Struggle / attempts → Product discovery → Concrete proof → Call to action

Hard requirements:

  • Title ≤ 20 characters (must use native setter when writing; see references/phase5-publish.md §5)
  • Body ≤ 1000 characters, clear paragraphs
  • Topic tags: 3–5, covering keyword + product brand + scene term
  • Must include a closing CTA (comment / save / follow)

Pre-Write Gate (confirm all items before writing begins):

  1. key_quote extracted from Phase 1 deep pass
  2. pain_description confirmed from Phase 1
  3. tools_tried confirmed from Phase 1
  4. Product info (name, install command) read from session_state.json
  5. Style fingerprint (style_fingerprint.json) read

After writing, generate an HTML preview (see references/phase4-writing.md §7), display it in the conversation, then ask:

Preview above. Ready to publish?
- "ok" / "publish" — proceed to Phase 5 publishing
- "edit: <instruction>" — revise per instruction and re-preview
- "draft" — save as draft, do not publish

Phase 5 — Publishing

Full execution parameters: references/phase5-publish.md.

Overview:

  1. Image generation: use XHS Creator Center's built-in "Text-to-Image" feature to auto-generate a cover (recommended), or read a user-provided local image
  2. Image upload (external image): two-step upload — GET permit → PUT file — to obtain file_id
  3. Content Verification Gate: title ≤ 20 characters, body non-empty, topic tags present
  4. Fill Creator Center: navigate → paste content → add topics → set cover
  5. Publish: click the "Publish" button
  6. Capture publish URL: extract note_id from the redirect URL or page
  7. Write record: update workspaces/xhs-posting/<date>/published.json, update posting.last_posted_at in session_state.json

Gate: publish only executes after user approval of the content; auto-publishing unapproved content is not allowed.


Phase 6 — Performance Tracking

Full execution parameters: references/phase6-tracking.md.

Standalone trigger: when the user says "track performance" / "see data" / similar trigger words, jump directly to this phase.

Sub-actions:

Sub-actionTriggerDescription
6.1 Single post 24h trackingAuto-scheduled after Phase 5 publishRevisit note_id 24h later, update initial metrics
6.2 Batch data collectionUser trigger / weeklyPull latest data for all published notes from recent N days
6.3 View commentsUser triggerView new comments, draft replies, reply after user approval
6.4 Generate reportUser triggerAggregate data, output table report

Data stored locally in workspaces/xhs-posting/tracking/.


Account Safety Rules

  1. Posting frequency: ≤ 1 post per account per day, ≥ 6 hours between posts
  2. Content originality: before each publish, check title similarity against historical posts (simple character-level check to avoid exact duplicates)
  3. Avoid sensitive terms: body and title must not contain WeChat IDs, Weibo handles, contact info, or external links
  4. No artificial engagement: do not simulate batch likes / collects / comments
  5. Topic tags: use existing platform topics only; do not create new ones
  6. Image compliance: use XHS built-in generated images or self-owned copyright images; do not use screenshots from others

Success Criteria

  • Phase 1: candidates >= 10, deep-extracted notes = 5, report file exists
  • Phase 2: style_fingerprint.json written, sample_notes count >= 1
  • Phase 4: user explicitly approves draft content
  • Phase 5: redirect URL contains published=true, note_id captured and written to published.json
  • Phase 6: all tracked note metrics updated without error

Session Cleanup

After completing the workflow (any phase exit — normal completion, user abort, or error), close the session to release resources:

bash
browser-act session close $SESSION

Do not close if the user explicitly requests to keep the session open for manual inspection.


Known Limitations

  • Max 1 post per account per day; minimum 6-hour interval enforced
  • window.__INITIAL_STATE__ hydration timing is inconsistent — retry once after 2 s if empty
  • search/notes XHR may not appear in all browser fingerprints; fall back to get markdown if missing
  • Topic chip insertion silently fails after execCommand body injection — always embed hashtags directly in body text (see references/phase5-publish.md §4.3)
  • note_id may not be auto-capturable when note is under review; record as pending_review and fill in manually later

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/xiaohongshu-auto-posting-xhs-post.memory.md

Before execution: if the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy accordingly.

After execution: if an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line: {YYYY-MM-DD}: {what happened} → {conclusion}

Normal execution does not write to the file. Do not record which keywords were used or how many results were returned — those are task outputs, not experience.

Frequently asked questions

What does the Xiaohongshu Auto Posting AI skill do?

Automates the complete Xiaohongshu (XHS / Little Red Book) content operation workflow: pain-point topic collection → style case collection → topic selection → content writing → publishing → performance tracking. Use when user mentions xiaohongshu auto posting, xhs auto post, little red book posting, xiaohongshu post, xiaohongshu auto posting, xiaohongshu content operations, xhs content marketing, post to xiaohongshu, publish on xhs, post on xiaohongshu, xiaohongshu promotion, xiaohongshu operations, xiaohongshu automation, xhs automation, track xhs performance, track xiaohongshu performance...

Why use Xiaohongshu Auto Posting on TypingMind?

Because you install it once and use it with any model. Xiaohongshu Auto Posting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Xiaohongshu Auto Posting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/browser-act/skills/tree/main/solutions/social-listening/xiaohongshu-auto-posting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Xiaohongshu Auto Posting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Xiaohongshu Auto Posting?

As many as you like. As long as a model supports skills, you can use Xiaohongshu Auto Posting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Xiaohongshu Auto Posting AI skill free?

Yes. It is published on GitHub by browser-act under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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