RedNote Research

Other

Research a topic through RedNote/Xiaohongshu discussion signals using either public-web mode (no login) or optional login-enhanced browser review when the user explicitly chooses deeper access. Use when checking RedNote community sentiment, reputation, latest policy/community updates, gossip/drama/news synthesis, local recommendations like restaurants/shops, when recovering evidence from weak public-web snippets/titles/OCR/subtitle fragments, or when analyzing posts, comments, screenshots, image posts, video/gif snippets, subtitles, or audio/transcript clues. Especially useful for prompts like "查小红书口碑", "搜 RedNote 讨论", "看看最近有什么风向/新政策", "总结八卦/争议", "找本地探店推荐", "分析评论区", "分析截图/视频/字幕", "根据截图线索继续搜", "总结某个账号最近发了什么", or "做一个 RedNote 社区情报初筛".

Install

openclaw skills install rednote-research

RedNote Community Intelligence

Research a topic with a RedNote/Xiaohongshu-first lens. Default to public-web mode, but support an optional login-enhanced path when the user explicitly wants fuller coverage. Expand queries deliberately, collect signals from multiple source types, separate evidence from vibe, and return a concise report that is honest about uncertainty.

Access modes

Read references/access-modes.md when deciding whether to stay in public-web mode or offer login-enhanced browser review. Read references/login-enhanced-workflow.md when the user explicitly chooses deeper access and you need an execution pattern for authenticated review. Read references/minimal-user-input-paths.md when public-web access is weak and the user prefers not to log in. Read references/account-summary-template.md when the task is to summarize a creator/account or recent posting behavior.

Default behavior:

start in public-web mode
do not assume login
if the user wants fuller account-level, recent-post, or comment-level coverage, offer the login-enhanced path as an explicit choice
if the user declines login, ask for the smallest useful seed input instead of giving up
state in the final answer whether findings came from public-web mode or login-enhanced mode

Core operating rules

Treat RedNote as a signal-discovery layer, not final proof.
Prefer a few inspectable sources over many shallow snippets.
Separate direct evidence, repeated anecdote, platform chatter, and rumor.
Put dates on fast-moving claims whenever possible.
Do not imply access to hidden comments, full threads, or app-only media.
If a page is inaccessible, do not overclaim from the search snippet alone.
Keep modality explicit: text-page, screenshot, image, video, gif, audio, or transcript.
Separate extraction from interpretation: OCR/ASR output is evidence, not automatic truth.
When media access is partial, say exactly what is visible and what remains uninspectable.

Default workflow

Clarify the subject, time scope, geography, output goal, and whether the user wants no-login mode or login-enhanced mode.
Start in public-web mode unless the user explicitly chooses login-enhanced mode.
Build a compact query set with mixed query families.
Search broadly across RedNote, official sources, media, and supporting review sites.
If public-web coverage is too thin for the task, explain that and offer login-enhanced browser review as the next step.
Extract recurring claims, contradictions, and missing evidence.
Score credibility separately from risk or recommendation strength.
Deliver a short report with links, caveats, next checks, and a brief note about which access mode was used.

1) Clarify the research target

Identify:

canonical name in Chinese and English
aliases, abbreviations, nicknames, hashtags, old names
category: education, policy, gossip, local, or general
geography if relevant: city, district, mall, campus, country, online/offline
time scope: latest 7 days, latest month, current season, or broader background
user intent: reputation check, update scan, controversy synthesis, shortlist, comment analysis, or post/video analysis

If the prompt is broad, infer likely aliases before searching.

For account-summary tasks, ask for the smallest useful identifier if available: profile URL, user ID/handle, screenshot, copied title list, or 3-5 recent note links. If the user wants fuller coverage and agrees to log in, switch from public-web mode to login-enhanced browser review instead of pretending public-web search is complete. If the user does not want login, read references/minimal-user-input-paths.md and ask for the least burdensome seed material that will improve coverage.

2) Build queries

Use scripts/query_builder.py when deterministic query expansion would help, especially if you need a media-focused query set or a starter claim log schema. Use scripts/recovery_query_builder.py when your starting point is weak public-web evidence: a thin search snippet, partial title, OCR fragment, subtitle line, hashtag, price, or visible date that needs recovery-oriented search pivots.

Prefer a mixed query set instead of one giant keyword dump:

overview: baseline discovery
latest: newest updates and recent turns in sentiment
trending: hot discussion and rumor-tracking discovery
comment: comment-area reactions and repeated talking points
review: reputation, quality, warning signs, user experience
recommendation: worth-it, shortlist, comparison, local picks
verification: official notices, registration records, named responses, implementation details

Typical source patterns:

site:xiaohongshu.com <entity> <modifier>
site:www.xiaohongshu.com <entity> <modifier>
<entity> 小红书 <modifier>
<entity> <modifier>

Category hints:

education: 口碑, 避雷, 退费, 课程质量, 就业, offer, 合同, 维权
policy: 政策, 新规, 通知, 官方回应, 执行, 解读, 影响
gossip: 爆料, 八卦, 翻车, 塌房, 争议, 后续, 聊天记录, 回应
local: 推荐, 探店, 菜品, 排队, 价格, 服务, 环境, 值不值, 避雷
general: 评价, 口碑, 体验, 真实反馈, 怎么样, 值不值

Query-building heuristics:

Start with 8-16 queries, not 40+.
Mix discovery queries with 2-4 verification queries.
Add geography for local or policy tasks.
Use a narrow time scope for fast-moving topics.
Search aliases and nicknames when drama or local slang is involved.
For cross-market topics, run both Chinese and English variants.

3) Search public-web sources

Prefer breadth before depth. Search first, then fetch only the strongest pages.

Target source mix:

RedNote/Xiaohongshu indexed pages and snippets
official statements, brands, schools, stores, regulators, or platform notices
reputable media reports for disputes or policy coverage
maps/review/listing sites for local businesses
forums and other community sites only as supplementary anecdotal signals

Search heuristics:

favor recency for policy, gossip, and local recommendations
keep a short source list with one-line relevance notes
search exact names, aliases, hashtags, and comparison targets
cross-check surprising claims with at least one non-RedNote source when possible
if the task is about a specific account and public-web search returns thin results, say so explicitly instead of overclaiming; then offer the login-enhanced path or ask for a few seed links/screenshots

4) Extract claims and discussion patterns

Normalize findings into compact bullets with fields like:

claim type: complaint / praise / neutral fact / official claim / media report / rumor / recommendation
theme: pricing, quality, service, fraud risk, policy impact, taste, queue, environment, controversy, support, etc.
evidence snippet
source URL
source class
visible date
repetition count if multiple sources echo the same point

Read references/output-patterns.md when you need output templates or comment clustering patterns. Read references/claim-log-schema.md when the task is evidence-heavy, rumor-sensitive, or needs claim-by-claim tracking. Read references/multimodal-capture.md when screenshots, images, videos, gifs, subtitles, or audio cues materially affect the answer. Read references/public-web-recovery.md when the first page is partial, blocked, snippet-only, or clearly weaker than the underlying media/discussion. Use scripts/claim_log_tools.py to initialize, normalize, or summarize a structured claim log when you have enough evidence items that manual tracking will become noisy.

Post / comment / screenshot / image / video / gif / audio analysis

Stay explicit about what is and is not directly observable from public-web access.

Break analysis into layers:

Surface metadata — visible title, caption, date, platform text, source URL.
Observed media evidence — visible text, OCR-able text, subtitles, scene details, sequence, speaker labels, or audio/transcript clues.
Content summary — what is clearly shown, spoken, or claimed.
Reaction summary — visible comment themes, sentiment split, repeated jokes, skepticism, support.
Credibility check — firsthand evidence vs repost vs edit-heavy clip vs rumor relay.
Open questions — what would require login, in-app rendering, browser automation, direct file access, frame extraction, OCR cleanup, or ASR.

If the user provides screenshots, transcripts, fetched page text, or media files, analyze those directly and keep extraction separate from interpretation.

Claim-first working pattern

When the topic is messy, do not jump straight from search results to a vibe summary.

Use this loop instead:

list the 2-6 decision-relevant claims
attach evidence items with explicit modality and access level
downgrade anything that remains snippet-only or relay-only
summarize only after the strongest claim/evidence pairs are visible

Good trigger conditions for a claim log:

rumor-heavy controversies
screenshot-led accusations
policy interpretation disputes
local recommendation tasks with sharply conflicting chatter
any answer where you need to explain why one repeated claim is still weak

5) Verify before concluding

Read references/verification-patterns.md when the task involves rumors, policy changes, business legitimacy, or claims that could materially affect a decision.

Default verification moves:

find the earliest visible source, not just the loudest repost
separate claim, response, and confirmed consequence
check whether the page is firsthand, quoted, scraped, or relayed
look for official names, dates, location details, and implementation language
for local businesses, compare RedNote chatter with maps/review data or official menu/hours pages
for policy topics, prioritize primary notices over interpretation posts
for gossip, keep anonymous screenshots and clipped media at rumor level unless independently corroborated
for screenshots, note whether key text is fully visible, cropped, or OCR-uncertain
for videos, distinguish caption-level evidence from frame-level evidence
for audio claims, distinguish direct transcript, ASR-derived wording, and second-hand paraphrase

6) Score credibility and decision risk

Read references/scoring-rubric.md when you need the full rubric.

Use at least two separate judgments:

Credibility score (0-5)

5: official documents, regulator notices, court/government records, direct statements, reputable reporting
4: detailed firsthand post or review with dates, screenshots, prices, names, or concrete specifics
3: specific but weakly corroborated anecdote or snippet
2: vague anecdote, repost, engagement bait, or SEO page
1: obvious rumor or unsourced assertion
0: cannot inspect or verify

Risk / caution / recommendation score (0-5)

Interpret the second score according to task type:

education / reputation / policy / gossip: higher means more caution or downside risk
local recommendation: higher can mean stronger recommendation confidence only if you label it explicitly; otherwise keep it as caution risk to avoid ambiguity

Weight repeated, independent, recent, and specific evidence more heavily than loud but vague posts.

7) Deliver the report

Keep the report concise and decision-oriented.

Choose the smallest fitting format:

A) Quick snapshot

Subject:
Category:
Time scope:
Overall signal: positive / mixed / caution / high risk / inconclusive
Confidence: low / medium / high

B) Findings

3-6 bullets, strongest evidence first

C) Evidence list

Use compact bullets when tables are awkward:

[credibility 4 | score 4 | first-hand | 2025-09] refund complaints repeat across multiple posts — <url>

D) Discussion clusters

cluster name
representative wording pattern
approximate repetition count
confidence note

E) What remains unverified

missing items that public-web access cannot confirm

F) Suggested next checks

official notice or registration lookup
a more recent search window
map/review cross-check for local places
direct in-app or browser review if the user wants deeper comment or media extraction

Fast paths

Quick reputation check

Build a mixed overview + review + verification query set.
Search 6-12 strong queries.
Capture 5-10 sources.
Score each source.
Return a short summary plus caveats.

Latest update or policy scan

Use latest + trending + verification.
Bias toward the last 7-30 days.
Separate official update from community interpretation.
State whether the trend is confirmed, contested, or still rumor-level.

Local recommendation scan

Use category local.
Mix review, recommendation, complaint, and verification queries.
Cluster themes: taste, price, queue, service, environment, location convenience.
Return a shortlist plus tradeoffs, not just one winner.

Comment or post analysis

Collect visible text, screenshots, snippets, or transcript first.
Cluster reactions into 3-5 themes.
Mark what is directly seen vs inferred.
State clearly when deeper extraction would require login, browser automation, or direct media processing.
If the user wants deeper comment-level coverage, offer login-enhanced mode as an explicit escalation path.
If the user declines login, ask for screenshots or copied comment text instead of pretending the full thread was inspected.

Account summary or recent-post scan

Start with public-web mode and gather any inspectable profile URL, note URLs, snippets, mirrors, or search-engine traces.
Read references/account-summary-template.md for output structure.
If the goal is a broad impression only, summarize from public-web evidence with caveats.
If the goal is recent-post completeness, tell the user public-web coverage may be partial and offer login-enhanced browser review.
If the user chooses login-enhanced mode, read references/login-enhanced-workflow.md and follow the controlled authenticated-review path.
If the user does not want login, read references/minimal-user-input-paths.md and ask for a few seed links, screenshots, or copied note titles to improve coverage.
Distinguish clearly between account-level observations, note-level evidence, and anything missing because of access limits.

Screenshot / image-led analysis

Capture the page context plus image-visible text, prices, dates, names, and watermarks.
Note image legibility and likely OCR uncertainty.
Separate image-contained claims from caption-contained claims.
If the page itself is weak, pivot on the strongest visible fragment with scripts/recovery_query_builder.py.
Log the strongest inspectable claim(s) before summarizing.

Video / subtitle / gif-led analysis

Capture caption, visible duration, upload date, and any subtitle/on-screen text.
Distinguish clip content from commentary about the clip.
If you only have snippet-level access, keep conclusions provisional and pivot on distinctive subtitle fragments or overlays with scripts/recovery_query_builder.py.
Say whether frames or the original file would materially improve confidence.

Audio / transcript-led analysis

Identify whether you have direct audio, subtitles, ASR text, or only quoted paraphrases.
Treat transcript quality as part of the evidence rating.
Avoid overreading tone, sarcasm, or exact wording without direct audio access.
If the only foothold is a quoted line, subtitle fragment, or repost caption, use scripts/recovery_query_builder.py to search for the earliest visible source or mirrors.
Log the spoken claim separately from reactions to it.

Reliability caveats

Search indexing can lag behind live app discussion.
Viral repetition does not equal verification.
Snippets can omit qualifiers or updates visible only on the landing page.
Local quality and policy enforcement can change quickly; recency matters.
Platform anti-bot controls can make no-login account research much thinner than in-app browsing.
If evidence stays thin after cross-checking, say inconclusive rather than stretching.