什么是 Redbook 小红书命令行工具？

Redbook 是一款专业级 CLI 实用程序，赋能开发者和营销人员通过编程方式与小红书交互。作为 Openclaw Skills 集合中的专项工具，它提供了对平台数据的深度访问，允许用户提取通常隐藏在移动端界面后的洞察。通过利用现有的浏览器会话，它无需复杂的 API 集成即可实现复杂的研究和自动化工作流。

该技能对于任何希望实现内容策略产业化的人来说都至关重要。从计算互动收藏比到识别高需求内容空白，Redbook 将原始社交数据转化为可操作的营销情报。对于使用 Openclaw Skills 构建自动化内容管道或竞争对手研究代理的用户而言，它尤为有效。

下载入口:https://github.com/openclaw/skills/tree/main/skills/lucasygu/redbook

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

npx clawhub@latest install redbook

2. 手动安装

将技能文件夹复制到以下位置之一

全局模式 ~/.openclaw/skills/ 工作区 /skills/

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

请帮我使用 Clawhub 安装 redbook。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。

Redbook 小红书命令行工具 应用场景

• 进行深度关键词研究，识别高上限的利基市场和尚未被满足的主题。
• 分析竞争对手主页，确定互动中位数和病毒式成功模式。
• 通过识别并回复评论区的问题来自动化社群管理。
• 从爆款帖子中提取结构化模板，为 AI 驱动的内容生成提供指导。
• 直接从 Markdown 文件生成风格化的社交媒体卡片，实现快速发布。

Redbook 小红书命令行工具 工作原理

1. 身份验证：该工具访问 macOS 上的本地浏览器 Cookie（Chrome、Safari 或 Firefox），以已登录用户身份验证请求。
2. 探索：用户进行搜索或获取动态，以检索访问内容所需的最新 xsec_token。
3. 分析：数据通过专门的模块（如关键词互动矩阵或机会评分逻辑）进行处理。
4. 处理：对于内容创作，该工具使用本地无头浏览器将 Markdown 渲染成高分辨率的 PNG 卡片。
5. 执行：发布评论或收藏笔记等操作均带有内置的安全延迟和抖动，以模拟人类行为。

Redbook 小红书命令行工具 配置指南

要将 Redbook 安装为您的 Openclaw Skills 之一，请运行以下命令：

clawhub install redbook

或者，通过 npm 安装：

npm install -g @lucasygu/redbook

要求：Node.js >= 22，macOS（用于访问钥匙串），以及 Chrome 浏览器中已登录 xiaohongshu.com 的活动会话。

Redbook 小红书命令行工具 数据架构与分类体系

Redbook 为所有命令提供结构化的 JSON 输出，以便于程序化分析。关键数据结构包括：

对象	描述	关键字段
笔记卡片	单个帖子数据	id, title, user, interact_info (点攒, 收藏, 评论)
创作者主页	用户元数据	nickname, desc, fans, vi_icon
病毒式分析	互动信号	viralScore, collectToLikeRatio, hookPatterns, themes
机会矩阵	市场研究	demand, competition, tier (S/A/B/C)
卡片渲染	视觉资产	width, height, dpr, style

description: Search, read, analyze, and automate Xiaohongshu (小红书) content via CLI
allowed-tools: Bash, Read, Write, Glob, Grep
# OpenClaw / ClawHub metadata (clawhub install redbook)
name: redbook
version: 0.5.0
metadata:
  openclaw:
    requires:
      bins:
        - redbook
    install:
      - kind: node
        package: "@lucasygu/redbook"
        bins: [redbook]
    os: [macos]
    homepage: https://github.com/lucasygu/redbook
tags:
  - xiaohongshu
  - social-media
  - analytics
  - content-ops

Redbook — Xiaohongshu CLI

Use the redbook CLI to search notes, read content, analyze creators, automate engagement, and research topics on Xiaohongshu (小红书/RED).

OpenClaw users: Install via clawhub install redbook or npm install -g @lucasygu/redbook.

Usage

/redbook search "AI编程"              # Search notes
/redbook read                    # Read a note
/redbook user                 # Creator profile
/redbook analyze              # Full creator analysis (profile + posts)

Quick Reference

Always add --json when parsing output programmatically. Without it, output is human-formatted text.

---

XHS Platform Signals

XHS is not T@witter or In@stagram. These platform-specific engagement ratios reveal content type and audience behavior.

Collect/Like Ratio (collected_count / liked_count)

XHS's "collect" (收藏) is a save-for-later mechanic — users build personal reference libraries. This ratio is the strongest signal of content utility.

Comment/Like Ratio (comment_count / liked_count)

Measures how much a note triggers conversation.

Share/Like Ratio (share_count / liked_count)

Measures social currency — whether users share to signal identity or help others.

Search Sort Semantics

Content Form Dynamics

---

Analysis Modules

Each module is a composable building block. Combine them for different analysis depths.

Module A: Keyword Engagement Matrix

Answers: Which keywords have the highest engagement ceiling? Which are saturated vs. underserved?

Commands:

redbook search "keyword1" --sort popular --json
redbook search "keyword2" --sort popular --json
# Repeat for each keyword in your list

Fields to extract from each result's items[]:

• items[].note_card.interact_info.liked_count — likes (may use Chinese numbers: "1.5万" = 15,000)
• items[].note_card.interact_info.collected_count — collects
• items[].note_card.interact_info.comment_count — comments
• items[].note_card.user.nickname — author

How to interpret:

• Top1 ceiling = items[0] likes — the best-performing note for this keyword. This is the proven demand signal.
• Top10 average = mean likes across items[0..9] — how well an average top note does.
• A high Top1 but low Top10 avg means one outlier dominates; hard to compete.
• A high Top10 avg means consistent demand; easier to break in.

Output: Keyword × engagement table ranked by Top1 ceiling.

---

Module B: Cross-Topic Heatmap

Answers: Which topic × scene intersections have demand? Where are the content gaps?

Commands:

# Combine base topic with scene/angle keywords
redbook search "base topic + scene1" --sort popular --json
redbook search "base topic + scene2" --sort popular --json
redbook search "base topic + scene3" --sort popular --json

Fields to extract: Same as Module A — Top1 liked_count for each combination.

How to interpret:

• High Top1 = proven demand for this intersection
• Zero or very low results = content gap (opportunity or no demand — check if the combination makes sense)
• Compare across scenes to find which angles resonate most with the base topic

Output: Base × Scene heatmap.

             scene1    scene2    scene3    scene4
base topic   ████ 8K   ██ 2K     ████ 12K  ?? 200

---

Module C: Engagement Signal Analysis

Answers: What type of content is each keyword? Reference, insight, or entertainment?

Commands: Use search results from Module A, or for a single note:

redbook analyze-viral "" --json

Fields to extract:

• From search results: compute ratios from interact_info fields
• From analyze-viral: use pre-computed engagement.collectToLikeRatio, engagement.commentToLikeRatio, engagement.shareToLikeRatio

How to interpret: Apply the ratio benchmarks from XHS Platform Signals above.

Output: Per-keyword or per-note classification.

---

Module D: Creator Discovery & Profiling

Answers: Who are the key creators in this niche? What are their strategies?

Commands:

# 1. Collect unique user_ids from search results across keywords
#    Extract from items[].note_card.user.user_id

# 2. For each creator:
redbook user "" --json
redbook user-posts "" --json

Fields to extract:

• From user: interactions[] where type === "fans" → follower count
• From user-posts: notes[].interact_info.liked_count for all posts → compute avg, median, max
• From user-posts: notes[].display_title → content patterns, posting frequency

How to interpret:

• Avg vs. Median likes: Large gap means viral outliers inflate the average. Median is the "true" baseline.
• Max / Median ratio: >5× means they've had breakout hits. Study those notes specifically.
• Post frequency: Count notes to estimate posting cadence. Prolific creators (>3/week) vs. quality-focused (<1/week).

Output: Creator comparison table.

---

Module E: Content Form Breakdown

Answers: Do image-text or video notes perform better for this topic?

Commands:

redbook search "keyword" --type image --sort popular --json
redbook search "keyword" --type video --sort popular --json

Fields to extract:

• Compare Top1 and Top10 avg liked_count and collected_count between the two result sets
• Note the type field: "normal" = image-text, "video" = video

Output: Form × engagement table.

---

Module F: Opportunity Scoring

Answers: Which keywords should I target? Where is the best effort-to-reward ratio?

Input: Keyword matrix from Module A.

Scoring logic:

• Demand = Top1 likes ceiling (proven audience size)
• Competition = density of high-engagement results (how many notes in Top10 have >1K likes)
• Score = Demand × (1 / Competition density)

Tier thresholds (based on Top1 likes):

Output: Tiered keyword list.

---

Module G: Audience Inference

Answers: Who is the audience for this niche? What do they want?

Input: Engagement ratios from Module C + comment themes from analyze-viral + content patterns.

Fields to extract from analyze-viral JSON:

• comments.themes[] — recurring phrases and keywords from comment section
• comments.questionRate — % of comments that are questions (learning intent)
• engagement.collectToLikeRatio — save behavior signals intent
• hook.hookPatterns[] — what title patterns attract this audience

Inference rules:

• High collect rate + high question rate → learning-oriented audience (students, professionals)
• High comment rate + emotional themes → community-oriented audience (sharing experiences)
• High share rate → aspiration-oriented audience (lifestyle, identity signaling)
• Comment language patterns → age/education signals (formal = older, slang = younger)

Output: Audience persona summary — demographics, intent, content preferences.

---

Module H: Content Brainstorm

Answers: What specific content should I create, backed by data?

Input: Opportunity scores (Module F) + audience persona (Module G) + heatmap gaps (Module B).

For each content idea, specify:

• Target keyword — from opportunity scoring
• Hook angle — based on hookPatterns that work for this niche
• Content type — 工具型/认知型/娱乐型 based on what the audience wants
• Form — 图文 or 视频 based on Module E
• Engagement target — realistic based on Top10 avg for this keyword
• Competitive reference — specific note URL that proves this angle works

Output: Ranked content ideas with data backing.

---

Module I: Comment Operations

Answers: Which comments deserve a reply? What is the comment quality distribution?

Commands:

# 1. Fetch all comments
redbook comments "" --all --json

# 2. Preview reply candidates (dry run)
redbook batch-reply "" --strategy questions --dry-run --json

# 3. Execute replies with template (5 min delay with ±30% jitter)
redbook batch-reply "" --strategy questions r
  --template "感谢提问！关于{content}，..." r
  --max 10

Fields to extract from --dry-run JSON:

• candidates[].commentId — target comments
• candidates[].isQuestion — boolean, detected question
• candidates[].likes — engagement signal
• candidates[].hasSubReplies — whether already answered
• skipped — how many comments were filtered out
• totalComments — total fetched

Strategies:

• questions — replies to comments ending with ？ or ? (learning-oriented audience)
• top-engaged — replies to highest-liked comments (maximum visibility)
• all-unanswered — replies to comments with no existing sub-replies (fill gaps)

How to interpret:

• High question rate (>15%) = audience is learning-oriented → reply to build authority
• High top-engaged comments (>100 likes) = reply to visible comments for maximum reach
• Many unanswered comments = engagement gap, opportunity to increase reply rate

Safety: Hard cap 30 replies per batch, minimum 3-minute delay with ±30% jitter (default 5 min), --dry-run by default (no template = preview only), immediate stop on captcha. See Rate Limits & Safety for details.

Output: Reply plan table with candidate comments, strategy match reason, and status.

---

Module J: Viral Replication

Answers: What structural template can I extract from successful notes to guide new content creation?

Commands:

# 1. Find top notes for a keyword
redbook search "keyword" --sort popular --json

# 2. Extract structural template from 2-3 top performers
redbook viral-template "" "" "" --json

Fields to extract from viral-template JSON:

• dominantHookPatterns[] — hook types appearing in majority of notes
• titleStructure.commonPatterns[] — specific title formula
• titleStructure.avgLength — target title length
• bodyStructure.lengthRange — target word count [min, max]
• bodyStructure.paragraphRange — target paragraph count
• engagementProfile.type — reference/insight/entertainment
• audienceSignals.commonThemes[] — what the audience talks about

How to interpret:

• Consistent hook patterns across notes = proven formula for this niche
• Narrow body length range = audience has clear content length preference
• High collect/like in profile = audience saves content → create reference material
• Common comment themes = topics to address in new content

Composition with other modules:

• Uses Module A results to identify top URLs for template extraction
• Feeds into Module H (Content Brainstorm) as structural constraint
• Uses Module C classification to validate engagement profile

Output: Content template spec — the structural skeleton for content creation. An LLM (via the composed workflow) uses this template to generate actual title, body, hashtags, and cover image prompt.

---

Module K: Engagement Automation

Answers: How should I manage ongoing engagement with my audience?

This module is a workflow that composes Modules I and J with human oversight.

Workflow:

1. Monitor — redbook comments "" --all --json to fetch recent comments
2. Filter — redbook batch-reply --strategy questions --dry-run to identify reply candidates
3. Review — Human reviews dry-run output (or LLM reviews with persona guidelines)
4. Execute — redbook batch-reply --strategy questions --template "..." --max 10
5. Report — Summary of replies sent, errors encountered, rate limit status

Safety rules:

• Always --dry-run first, human approval before execution
• Maximum 30 replies per session (hard cap)
• Minimum 3-minute delay between replies, default 5 minutes, with ±30% random jitter
• Never reply to the same comment twice (check hasSubReplies)
• Stop immediately on captcha — do not retry
• See Rate Limits & Safety for XHS risk control thresholds

Anti-spam guidelines:

• Vary reply templates across batches
• Limit to 1-2 batch runs per note per day
• Prioritize quality (targeted strategy) over quantity
• Uniform timing patterns trigger bot detection — jitter is applied automatically

---

Module L: Card Rendering

Answers: How do I turn markdown content into Xiaohongshu-ready image cards?

Commands:

# Render markdown to styled PNG cards
redbook render content.md --style xiaohongshu

# Custom style and output directory
redbook render content.md --style dark --output-dir ./cards

# JSON output (for programmatic use)
redbook render content.md --json

Input: Markdown file with YAML frontmatter: ```markdown

emoji: "??" title: "5个AI效率技巧" subtitle: "Claude Code 实战"

技巧一：...

Content here...

---

技巧二：...

More content...

**Output:** `cover.png` + `card_1.png`, `card_2.png`, ... in the same directory.

**Card specs:**
- **Size:** 1080×1440 (3:4 ratio, standard XHS image)
- **DPR:** 2 (retina quality, actual output 2160×2880)
- **Styles:** purple, xiaohongshu, mint, sunset, ocean, elegant, dark

**Pagination modes:**
- `auto` (default) — smart split on heading/paragraph boundaries using character-count heuristic
- `separator` — manual split on `---` in markdown

**How to interpret:**
- Uses the user's existing Chrome for rendering (via `puppeteer-core`) — no browser download needed
- Purely offline — no XHS API or cookies required
- Output images are ready for `redbook post --images cover.png card_1.png ...`

**Dependencies:** Requires `puppeteer-core` and `marked` (optional, install with `npm install -g puppeteer-core marked`).

**Composition with other modules:**
- Pairs with Module H (Content Brainstorm) — generate content ideas, write markdown, render to cards
- Pairs with Module J (Viral Replication) — extract template, write content matching the template, render
- Output feeds into `redbook post --images` for publishing

---

## Composed Workflows

Combine modules for different analysis depths.

### Quick Topic Scan (~5 min)
**Modules:** A → C → F

Search 3–5 keywords, classify engagement type, rank opportunities. Good for quickly validating whether a niche is worth deeper research.

### Content Planning
**Modules:** A → B → E → F → H

Build keyword matrix, map topic × scene intersections, check content form performance, score opportunities, brainstorm specific content ideas.

### Creator Competitive Analysis
**Modules:** A → D

Find who dominates a niche and study their content strategy, posting frequency, and engagement patterns.

### Full Niche Analysis
**Modules:** A → B → C → D → E → F → G → H

The comprehensive playbook — keyword landscape, cross-topic heatmap, engagement signals, creator profiles, content form analysis, opportunity scoring, audience personas, and data-backed content ideas.

### Single Note Deep-Dive
**Command:** `redbook analyze-viral "" --json`

No module composition needed — `analyze-viral` returns hook analysis, engagement ratios, comment themes, author baseline comparison, and a 0-100 viral score in one call.

### Viral Pattern Research → Content Template
```bash
# 1. Find top notes
redbook search "keyword" --sort popular --json

# 2. Extract template from top 3 notes (replaces manual synthesis)
redbook viral-template "" "" "" --json

viral-template automates what previously required manual synthesis across analyze-viral results. It outputs a ContentTemplate JSON that captures dominant hooks, body structure ranges, engagement profile, and audience signals.

Reply Management

Modules: I

Single-module workflow for managing comment engagement on your notes. Use batch-reply --dry-run to audit, then execute with a template.

Content Replication

Modules: A → J → H → L

Keyword research → viral template extraction → data-backed content brainstorm → render to image cards. The template provides structural constraints that guide Module H's content ideas. Module L renders the final markdown to XHS-ready PNGs.

Content Creation End-to-End

Modules: A → J → H → L → post

The full pipeline: research keywords → extract viral template → brainstorm content → write markdown → render to styled image cards → publish via redbook post --images cover.png card_1.png ...

Full Operations

Modules: A → C → I → J → K

Comprehensive automation playbook — keyword analysis, engagement classification, comment operations, viral replication templates, and engagement automation workflow.

---

Rate Limits & Safety

XHS enforces aggressive anti-spam (风控) that detects automated behavior through device fingerprinting, activity ratio monitoring, and timing pattern analysis. The CLI applies safe defaults based on platform research.

Safe Thresholds

Anti-Detection Measures

• Timing jitter: ±30% random variation on all batch delays. Uniform intervals are a bot signature.
• Hard caps: Maximum 30 replies per batch (down from 50). No override.
• Rate limit warnings: post, comment, and reply commands display safe interval reminders after each action.
• Captcha circuit breaker: Batch operations stop immediately on captcha (NeedVerify).

What Triggers Risk Control

• Uniform timing — replying at exact 3-second intervals flags bot detection
• High frequency — >50 interactions/minute across any action type
• Activity ratio anomaly — more comments than post views signals inauthentic behavior
• Device fingerprint mismatch — XHS fingerprints 21 hardware parameters

Best Practices for Agents

1. Always --dry-run first, review candidates, then execute
2. Use the default 5-minute delay — do not override --delay below 180000 (3 min)
3. Limit batch runs to 1-2 per note per day
4. Vary reply templates between batches
5. Space post commands 3-4 hours apart (2-3 notes/day maximum)

---

API vs Browser Limitations

The following operations work reliably via API:

• Reading: search, notes, comments, user profiles, feed, favorites
• Writing: top-level comments, comment replies, collect/uncollect notes
• Analysis: viral scoring, template extraction, batch reply planning

The following operations are unreliable via API (frequently trigger captcha):

• Publishing notes (use --private for higher success rate)
• Bulk operations at very high frequency

The following operations require browser automation (not supported by this CLI):

• Captcha solving, real-time notifications
• Like/follow (heavy anti-automation enforcement)
• DM/private messaging
• Cover image generation (use external tools like Gemini/DALL-E)

---

Command Details

redbook search

Search for notes by keyword. Returns note titles, URLs, likes, author info.

redbook search "Claude Code教程" --json
redbook search "AI编程" --sort popular --json        # Sort: general, popular, latest
redbook search "Cursor" --type image --json           # Type: all, video, image
redbook search "MCP Server" --page 2 --json           # Pagination

Options:

• --sort : general (default), popular, latest
• --type : all (default), video, image
• --page : Page number (default: 1)

redbook read

Read a note's full content — title, body text, images, likes, comments count.

redbook read "https://www.xiaohongshu.com/explore/abc123" --json

Accepts full URLs or short note IDs. Falls back to HTML scraping if API returns captcha.

redbook comments

Get comments on a note. Use --all to fetch all pages.

redbook comments "https://www.xiaohongshu.com/explore/abc123" --json
redbook comments "https://www.xiaohongshu.com/explore/abc123" --all --json

redbook user

Get a creator's profile — nickname, bio, follower count, note count, likes received.

redbook user "5a1234567890abcdef012345" --json

The userId is the hex string from the creator's profile URL.

redbook user-posts

List all notes posted by a creator. Returns titles, URLs, likes, timestamps.

redbook user-posts "5a1234567890abcdef012345" --json

redbook feed

Browse the recommendation feed.

redbook feed --json

redbook topics

Search for topic hashtags. Useful for finding trending topics to attach to posts.

redbook topics "Claude Code" --json

redbook favorites [userId]

List a user's collected (bookmarked) notes. Defaults to the current logged-in user when no userId is provided.

redbook favorites --json                        # Your own favorites
redbook favorites "5a1234567890abcdef" --json   # Another user's favorites
redbook favorites --all --json                  # Fetch all pages

Options:

• --all: Fetch all pages of favorites (default: first page only)

Note: Other users' favorites are only visible if they haven't set their collection to private.

redbook collect

Collect (bookmark) a note to your favorites.

redbook collect "https://www.xiaohongshu.com/explore/abc123"

redbook uncollect

Remove a note from your collection.

redbook uncollect "https://www.xiaohongshu.com/explore/abc123"

redbook analyze-viral

Analyze why a viral note works. Returns a deterministic viral score (0–100).

redbook analyze-viral "https://www.xiaohongshu.com/explore/abc123" --json
redbook analyze-viral "https://www.xiaohongshu.com/explore/abc123" --comment-pages 5

Options:

• --comment-pages : Comment pages to fetch (default: 3, max: 10)

JSON output structure: Returns { note, score, hook, content, visual, engagement, comments, relative, fetchedAt }.

• score.overall (0–100) — composite of hook (20) + engagement (20) + relative (20) + content (20) + comments (20)
• hook.hookPatterns[] — detected title patterns (Identity Hook, Emotion Word, Number Hook, Question, etc.)
• engagement — likes, comments, collects, shares + ratios (collectToLikeRatio, commentToLikeRatio, shareToLikeRatio)
• relative.viralMultiplier — this note's likes / author's median likes
• relative.isOutlier — true if viralMultiplier > 3
• comments.themes[] — top recurring keyword phrases from comments

redbook viral-template [url2] [url3]

Extract a reusable content template from 1-3 viral notes. Analyzes each note (same pipeline as analyze-viral) and synthesizes common structural patterns.

redbook viral-template "" "" "" --json
redbook viral-template "" --comment-pages 5 --json

Options:

• --comment-pages : Comment pages to fetch per note (default: 3, max: 10)

JSON output structure: Returns { dominantHookPatterns, titleStructure, bodyStructure, engagementProfile, audienceSignals, sourceNotes, generatedAt }.

• dominantHookPatterns[] — hook types appearing in majority of input notes
• titleStructure.avgLength — average title length across notes
• bodyStructure.lengthRange — [min, max] body length
• engagementProfile.type — "reference" / "insight" / "entertainment"
• audienceSignals.commonThemes[] — merged comment themes across notes

redbook comment

Post a top-level comment on a note.

redbook comment "" --content "Great post!" --json

Options:

• --content (required): Comment text

redbook reply

Reply to a specific comment on a note.

redbook reply "" --comment-id "" --content "Thanks for asking!" --json

Options:

• --comment-id (required): Comment ID to reply to (from comments --json output)
• --content (required): Reply text

redbook batch-reply

Reply to multiple comments using a filtering strategy. Always preview with --dry-run first.

# Preview which comments match the strategy
redbook batch-reply "" --strategy questions --dry-run --json

# Execute replies with a template (default 5 min delay with jitter)
redbook batch-reply "" --strategy questions r
  --template "感谢提问！{content}" --max 10

Options:

• --strategy : questions (default), top-engaged, all-unanswered
• --template : Reply template with {author}, {content} placeholders
• --max : Max replies (default: 10, hard cap: 30)
• --delay : Delay between replies in ms (default: 300000 / 5 min, min: 180000 / 3 min). ±30% random jitter applied automatically.
• --dry-run: Preview candidates without posting (default when no template)

Safety: Stops immediately on captcha. No template = dry-run only. Delays include random jitter to avoid uniform timing patterns that trigger XHS bot detection.

redbook render

Render a markdown file with YAML frontmatter into styled PNG image cards. Uses the user's existing Chrome installation — no browser download needed.

redbook render content.md --style xiaohongshu
redbook render content.md --style dark --output-dir ./cards
redbook render content.md --pagination separator --json

Options:

• --style : purple, xiaohongshu (default), mint, sunset, ocean, elegant, dark
• --pagination : auto (default), separator (split on ---)
• --output-dir : Output directory (default: same as input file)
• --width : Card width in px (default: 1080)
• --height : Card height in px (default: 1440)
• --dpr : Device pixel ratio (default: 2)

Requires: puppeteer-core and marked (npm install -g puppeteer-core marked). Does NOT require XHS cookies — purely offline rendering.

Override Chrome path: Set CHROME_PATH environment variable if Chrome is not in the standard location.

redbook whoami

Check connection status. Verifies cookies are valid and shows the logged-in user.

redbook whoami

redbook post (Limited)

Publish an image note. Frequently triggers captcha (type=124) on the creator API. Image upload works, but the publish step is unreliable. For posting, consider using browser automation instead.

redbook post --title "标题" --body "正文" --images cover.png --json
redbook post --title "测试" --body "..." --images img.png --private --json

Options:

• --title