redbook

分类: 内容与多媒体 | 上传者: lucasygu | 下载: 0 | 版本: v1.0（最新）
Search, read, analyze, and automate Xiaohongshu (小红书) content via CLI
更新日志: Source: GitHub https://github.com/lucasygu/redbook
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📁 .github/
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📁 scripts/
- 📄 postinstall.js 4.5 KB
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📁 src/
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  - 📄 cdp-cookies.ts 13.1 KB
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  - 📄 creator-signing.ts 1.8 KB
  - 📄 fingerprint-data.ts 2.3 KB
  - 📄 render.ts 19.5 KB
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📄 social-preview.png 261.4 KB
📄 tsconfig.json 458 B
SKILL.md

---
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 <url>                   # Read a note
/redbook user <userId>                # Creator profile
/redbook analyze <userId>             # Full creator analysis (profile + posts)
```

## Quick Reference

| Intent | Command |
|--------|---------|
| Search notes | `redbook search "keyword" --json` |
| Read a note | `redbook read <url> --json` |
| Get comments | `redbook comments <url> --json --all` |
| Creator profile | `redbook user <userId> --json` |
| Creator's posts | `redbook user-posts <userId> --json` |
| Browse feed | `redbook feed --json` |
| Search hashtags | `redbook topics "keyword" --json` |
| Analyze viral note | `redbook analyze-viral <url> --json` |
| Extract content template | `redbook viral-template <url1> <url2> --json` |
| Post a comment | `redbook comment <url> --content "text"` |
| Reply to comment | `redbook reply <url> --comment-id <id> --content "text"` |
| Batch reply (preview) | `redbook batch-reply <url> --strategy questions --dry-run` |
| List favorites | `redbook favorites --json` or `redbook favorites <userId> --json` |
| Collect a note | `redbook collect <url>` |
| Remove from collection | `redbook uncollect <url>` |
| Render markdown to cards | `redbook render content.md --style xiaohongshu` |
| Check connection | `redbook whoami` |

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

---

## 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 |

---

## 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:**
```bash
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% |

---

### Module B: Cross-Topic Heatmap

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

**Commands:**
```bash
# 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:
```bash
redbook analyze-viral "<noteUrl>" --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](#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 |

---

### Module D: Creator Discovery & Profiling

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

**Commands:**
```bash
# 1. Collect unique user_ids from search results across keywords
#    Extract from items[].note_card.user.user_id

# 2. For each creator:
redbook user "<userId>" --json
redbook user-posts "<userId>" --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 |

---

### Module E: Content Form Breakdown

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

**Commands:**
```bash
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% |

---

### 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 |

---

### 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.

| # | 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] |

---

### Module I: Comment Operations

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

**Commands:**
```bash
# 1. Fetch all comments
redbook comments "<noteUrl>" --all --json

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

# 3. Execute replies with template (5 min delay with ±30% jitter)
redbook batch-reply "<noteUrl>" --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](#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:**
```bash
# 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 "<url1>" "<url2>" "<url3>" --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 "<myNoteUrl>" --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](#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:**
```bash
# 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 "<url>" --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 "<url1>" "<url2>" "<url3>" --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

| 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 <keyword>`

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

```bash
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 <type>`: `general` (default), `popular`, `latest`
- `--type <type>`: `all` (default), `video`, `image`
- `--page <n>`: Page number (default: 1)

### `redbook read <url>`

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

```bash
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 <url>`

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

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

### `redbook user <userId>`

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

```bash
redbook user "5a1234567890abcdef012345" --json
```

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

### `redbook user-posts <userId>`

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

```bash
redbook user-posts "5a1234567890abcdef012345" --json
```

### `redbook feed`

Browse the recommendation feed.

```bash
redbook feed --json
```

### `redbook topics <keyword>`

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

```bash
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.

```bash
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 <url>`

Collect (bookmark) a note to your favorites.

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

### `redbook uncollect <url>`

Remove a note from your collection.

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

### `redbook analyze-viral <url>`

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

```bash
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 <n>`: 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 <url> [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.

```bash
redbook viral-template "<url1>" "<url2>" "<url3>" --json
redbook viral-template "<url1>" --comment-pages 5 --json
```

**Options:**
- `--comment-pages <n>`: 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 <url>`

Post a top-level comment on a note.

```bash
redbook comment "<noteUrl>" --content "Great post!" --json
```

**Options:**
- `--content <text>` (required): Comment text

### `redbook reply <url>`

Reply to a specific comment on a note.

```bash
redbook reply "<noteUrl>" --comment-id "<commentId>" --content "Thanks for asking!" --json
```

**Options:**
- `--comment-id <id>` (required): Comment ID to reply to (from `comments --json` output)
- `--content <text>` (required): Reply text

### `redbook batch-reply <url>`

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

```bash
# Preview which comments match the strategy
redbook batch-reply "<noteUrl>" --strategy questions --dry-run --json

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

**Options:**
- `--strategy <name>`: `questions` (default), `top-engaged`, `all-unanswered`
- `--template <text>`: Reply template with `{author}`, `{content}` placeholders
- `--max <n>`: Max replies (default: 10, hard cap: 30)
- `--delay <ms>`: 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 <file>`

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

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

**Options:**
- `--style <name>`: `purple`, `xiaohongshu` (default), `mint`, `sunset`, `ocean`, `elegant`, `dark`
- `--pagination <mode>`: `auto` (default), `separator` (split on `---`)
- `--output-dir <dir>`: Output directory (default: same as input file)
- `--width <n>`: Card width in px (default: 1080)
- `--height <n>`: Card height in px (default: 1440)
- `--dpr <n>`: 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.

```bash
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.

```bash
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 <title>`: Note title (required)
- `--body <body>`: Note body text (required)
- `--images <paths...>`: Image file paths (required, at least one)
- `--topic <keyword>`: Search and attach a topic hashtag
- `--private`: Publish as private note

### Global Options

All commands accept:
- `--cookie-source <browser>`: `chrome` (default), `safari`, `firefox`
- `--chrome-profile <name>`: Chrome profile directory name (e.g., "Profile 1"). Auto-discovered if omitted.
- `--json`: Output as JSON

---

## Technical Reference

### xsec_token — Required for Reading Notes

The XHS API requires a valid `xsec_token` to fetch note content. Without it, `read`, `comments`, and `analyze-viral` return `{}`.

**Key rules:**

1. **Tokens expire.** A URL with `?xsec_token=...` from a previous session will return `{}`. Never cache or reuse old URLs.
2. **`search` always returns fresh tokens.** Every item in search results includes a valid `xsec_token` for that note.
3. **noteId alone returns `{}`.** Running `redbook read <noteId>` without a token almost always fails.

**The correct workflow — always search first:**

```bash
# WRONG — stale URL or bare noteId, will likely return {}
redbook read "689da7b0000000001b0372c6" --json
redbook read "https://www.xiaohongshu.com/explore/689da7b0?xsec_token=OLD_TOKEN" --json

# RIGHT — search first, then use the fresh URL with token
redbook search "AI编程" --sort popular --json
# Extract the noteId + xsec_token from search results, then:
redbook read "https://www.xiaohongshu.com/explore/<noteId>?xsec_token=<freshToken>" --json
```

**For agents:** When the user gives a bare XHS note URL (no `xsec_token` param), extract the noteId from the URL path, search for the note title or noteId to get a fresh token, then use the full URL with the fresh token.

**How to extract fresh URLs from search results (JSON):**

```bash
# Each search result item has: { id: "noteId", xsec_token: "...", note_card: { ... } }
# Build the URL: https://www.xiaohongshu.com/explore/{id}?xsec_token={xsec_token}
```

**Commands that need xsec_token:** `read`, `comments`, `analyze-viral`
**Commands that do NOT need xsec_token:** `search`, `user`, `user-posts`, `feed`, `whoami`, `topics`

### Chinese Number Formats in API Responses

The XHS API returns abbreviated numbers with Chinese unit suffixes:

| API value | Actual number |
|-----------|---------------|
| `"1.5万"` | 15,000 |
| `"2.4万"` | 24,000 |
| `"1.2亿"` | 120,000,000 |
| `"115"` | 115 |

`万` = ×10,000. `亿` = ×100,000,000. Numbers under 10,000 are plain integers as strings.

The `analyze-viral` command handles this automatically. When parsing `--json` output manually, watch for these suffixes in `interact_info` fields (`liked_count`, `collected_count`, etc.).

### Error Handling

| Error | Meaning | Fix |
|-------|---------|-----|
| `{}` empty response | Missing or expired xsec_token | Search first to get a fresh token |
| "No 'a1' cookie" | Not logged into XHS in browser | Log into xiaohongshu.com in Chrome |
| "Session expired" | Cookie too old | Re-login in Chrome |
| "NeedVerify" / captcha | Anti-bot triggered | Wait and retry, or reduce request frequency |
| "IP blocked" (300012) | Rate limited | Wait or switch network |

---

## Output Format Guidance

When producing analysis reports, use these formats:

**Data tables:** Markdown tables with exact field mappings. Always include the metric unit.

**Heatmaps:** ASCII bar charts for cross-topic comparison:
```
             职场    生活    教育    创业
AI编程       ████ 8K  ██ 2K   ████ 12K ░░ 200
Claude Code  ██ 3K    ░░ 100  ██ 4K    █ 1K
```

**Creator comparison:** Structured table with both quantitative metrics and qualitative style assessment.

**Final reports:** Use this section order:
1. Market Overview (demand signals, content velocity)
2. Keyword Landscape (engagement matrix, opportunity tiers)
3. Cross-Topic Heatmap (topic × scene intersections)
4. Audience Persona (demographics, intent, preferences)
5. Competitive Landscape (creator profiles, strategy patterns)
6. Content Opportunities (tiered recommendations with data backing)
7. Content Ideas (specific hooks, angles, targets)

## Programmatic API

```typescript
import { XhsClient } from "@lucasygu/redbook";
import { loadCookies } from "@lucasygu/redbook/cookies";

const cookies = await loadCookies("chrome");
const client = new XhsClient(cookies);

const results = await client.searchNotes("AI编程", 1, 20, "popular");
const topics = await client.searchTopics("Claude Code");
```

## Requirements

- Node.js >= 22
- Logged into xiaohongshu.com in Chrome (or Safari/Firefox with `--cookie-source`)
- macOS (cookie extraction uses native keychain access)
- **For card rendering only:** `puppeteer-core` and `marked` (`npm install -g puppeteer-core marked`). Uses your existing Chrome — no additional browser download.
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