redbook

---

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

| Intent | Command | |--------|---------| | Search notes | redbook search "keyword" --json | | Read a note | redbook read --json | | Get comments | redbook comments --json --all | | Creator profile | redbook user --json | | Creator's posts | redbook user-posts --json | | Browse feed | redbook feed --json | | Search hashtags | redbook topics "keyword" --json | | Analyze viral note | redbook analyze-viral --json | | Extract content template | redbook viral-template --json | | Post a comment | redbook comment --content "text" | | Reply to comment | redbook reply --comment-id --content "text" | | Batch reply (preview) | redbook batch-reply --strategy questions --dry-run | | Like a note | redbook like | | Unlike a note | redbook like --undo | | List favorites | redbook favorites --json or redbook favorites --json | | Collect a note | redbook collect | | Remove from collection | redbook uncollect | | List followers | redbook followers --json | | List following | redbook following --json | | Delete own note | redbook delete | | Check note health | redbook health --json or redbook health --all --json | | List user boards | redbook boards or redbook boards --json | | List album notes | redbook board or redbook board --json | | Render markdown to cards | redbook render content.md --style xiaohongshu | | Publish image note | redbook post --title "..." --body "..." --images img.jpg | | Check connection | redbook whoami |

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

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XHS Platform Signals

XHS is not Twitter or Instagram. 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.

| Ratio | Classification | Meaning | |-------|---------------|---------| | >40% | 工具型 (Reference) | Tutorial, checklist, template — users bookmark for reuse | | 20–40% | 认知型 (Insight) | Thought-provoking but not saved for later | | <20% | 娱乐型 (Entertainment) | Consumed and forgotten — engagement is passive |

Comment/Like Ratio (comment_count / liked_count)

Measures how much a note triggers conversation.

| Ratio | Classification | Meaning | |-------|---------------|---------| | >15% | 讨论型 (Discussion) | Debate, sharing experiences, asking questions | | 5–15% | 正常互动 (Normal) | Typical engagement pattern | | <5% | 围观型 (Passive) | Users like but don't engage further |

Share/Like Ratio (share_count / liked_count)

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

| Ratio | Meaning | |-------|---------| | >10% | 社交货币 — people share to signal taste, identity, or help friends | | <10% | Content consumed individually, not forwarded |

Search Sort Semantics

| Sort | What It Reveals | |------|----------------| | --sort popular | Proven ceiling — the best a keyword can do | | --sort latest | Content velocity — how much is being posted now | | --sort general | Algorithm-weighted blend (default) |

Content Form Dynamics

| Form | Tendency | |------|----------| | 图文 (image-text, type: "normal") | Higher collect rate — users save reference content | | 视频 (video, type: "video") | Higher like rate — easier to consume passively |

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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.

| Keyword | Top1 Likes | Top10 Avg | Top1 Collects | Collect/Like | |---------|-----------|-----------|---------------|-------------| | keyword1 | 12,000 | 3,200 | 5,400 | 45% | | keyword2 | 8,500 | 4,100 | 1,200 | 14% |

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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

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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.

| Keyword | Collect/Like | Comment/Like | Type | |---------|-------------|-------------|------| | keyword1 | 45% | 8% | 工具型 + 正常互动 | | keyword2 | 12% | 22% | 娱乐型 + 讨论型 |

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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.

| Creator | Followers | Avg Likes | Median | Max | Posts | Style | |---------|----------|-----------|--------|-----|-------|-------| | @creator1 | 12万 | 3,200 | 1,800 | 45,000 | 89 | Tutorial | | @creator2 | 5.4万 | 8,100 | 6,500 | 22,000 | 34 | Story |

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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.

| Form | Top1 Likes | Top10 Avg | Collect/Like | |------|-----------|-----------|-------------| | 图文 | 8,000 | 2,400 | 42% | | 视频 | 15,000 | 5,100 | 18% |

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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):

| Tier | Top1 Likes | Meaning | |------|-----------|---------| | S | >100,000 (10万+) | Massive demand — hard to compete but huge upside | | A | 20,000–100,000 | Strong demand — competitive but winnable | | B | 5,000–20,000 | Moderate demand — good for growing accounts | | C | <5,000 | Niche — low competition, low ceiling |

Output: Tiered keyword list.

| Tier | Keyword | Top1 | Competition | Opportunity | |------|---------|------|-------------|------------| | A | keyword1 | 45K | Medium (6/10 >1K) | High | | B | keyword3 | 12K | Low (2/10 >1K) | Very High | | S | keyword2 | 120K | High (10/10 >1K) | Medium |

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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.

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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.

| # | Keyword | Hook Angle | Type | Target Likes | Reference | |---|---------|-----------|------|-------------|-----------| | 1 | keyword3 | "N个方法..." (List) | 工具型 图文 | 5K+ | [top note URL] | | 2 | keyword1 | "为什么..." (Question) | 认知型 视频 | 10K+ | [top note URL] |

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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 \
  --template "感谢提问！关于{content}，..." \
  --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.

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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.

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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

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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:

---
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

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Module M: Note Health Check (限流检测)

Answers: Are any of my notes being secretly rate-limited by XHS?

XHS assigns a hidden level field to each note in the creator backend API. This level controls recommendation distribution but is never shown in the UI. Your note may look "normal" while secretly receiving zero recommendations.

Commands:

# Check all notes (first page)
redbook health
Check all pages
redbook health --all
JSON output for programmatic use
redbook health --all --json

Level definitions:

| Level | Status | Meaning | |-------|--------|---------| | 4 | 🟢 Normal | Full recommendation distribution | | 2-3 | 🟡 Baseline | Basically normal, minor constraints | | 1 | ⚪ New | Under review (new post) | | -1 | 🔴 Soft limit | Mild throttling, decreased recommendations | | -5 to -101 | 🔴 Moderate | Moderate throttling, minimal promotion | | -102 | ⛔ Severe | Irreversible — must delete and repost |

Additional checks:

Sensitive word detection — flags titles containing automation/AI keywords (自动化, AI生成, 批量, etc.)

Tag count warning — flags notes with >5 hashtags (risk factor)

How to interpret:

Level -1 or below = your note is being throttled. Consider editing or deleting + reposting.

Level -102 = irreversible. Delete the note and create fresh content.

Sensitive word hits = risky title keywords that may trigger throttling. Rephrase.

Excessive tags = potential spam signal. Use 3-5 targeted tags.

Output: Terminal dashboard with color-coded distribution summary, limited notes list, and risk factor warnings.

Discovery credit: @xxx111god — xhs-note-health-checker

---

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

# 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 ...

Account Health Monitoring

Modules: M

Run redbook health --all periodically to catch throttled notes early. If level drops below 1, investigate the note's content for policy violations. Combine with Module I to check if throttled notes still have unanswered comments worth replying to.

Full Operations

Modules: A → C → I → J → K → M

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

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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

| Action | Safe Interval | CLI Default | Hard Cap | |--------|--------------|-------------|----------| | Post a note | 3-4 hours (2-3 notes/day max) | N/A (manual) | — | | Comment | ≥3 minutes | N/A (manual) | — | | Reply | ≥3 minutes | N/A (manual) | — | | Batch reply delay | ≥3 minutes | 5 min ±30% jitter | — | | Batch reply count | — | 10 | 30 |

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 \
  --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