Skill: saybanet/sayba-redbook (version 0.5.0)

--- 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.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)