Install
openclaw skills install @browseract-cli/threads-keyword-searchSearches Threads posts by keyword or hashtag and returns matching posts with engagement metrics, extracted from SSR-embedded JSON. Use when user asks to search Threads posts, find Threads content by topic, scrape Threads search results, collect Threads posts about a keyword, monitor Threads hashtag activity, pull Threads posts mentioning a term, gather Threads content by hashtag, search for posts on Threads, extract Threads search feed, get trending posts on Threads, find Threads discussions about a subject, or fetch recent or top Threads posts by keyword.
openclaw skills install @browseract-cli/threads-keyword-searchkeyword + sort filter → list of matching posts with engagement metrics
All process output to user (progress updates, process notifications) follows the user's language.
Search Threads for posts matching a keyword or hashtag and extract the results from SSR-embedded JSON, with support for top/recent sort ordering.
If browser-act has been confirmed available in the current session → skip this step.
Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.
This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page. JS code is encapsulated in Python files under the
scripts/directory, invoked viaeval "$(python scripts/xxx.py {params})".$(...)is bash syntax; it is recommended to use the bash tool for execution.
Navigate to the search page with the keyword and sort filter, then extract all results embedded in the initial HTML:
navigate https://www.threads.com/search/?q={keyword}&serp_type={filter}
{keyword}: URL-encoded search term or hashtag (e.g., AI or %23AI for #AI){filter}: top (default, most relevant) or recent (chronologically newest)wait stableeval "$(python scripts/extract-search-results.py '{keyword}' --filter {filter})"Output example:
{
"keyword": "AI",
"filter": "top",
"posts": [
{
"id": "3936653356768062022", // post internal ID (pk)
"code": "DZyvcdEl-W1", // post short code for URL
"url": "https://www.threads.com/@flower_popy/post/DZyvcdEl-W1",
"text": "AI is changing everything...", // post text content, null if no caption
"taken_at": 1781926571, // Unix timestamp of post creation
"like_count": 34, // number of likes
"reply_count": 6, // number of direct replies
"repost_count": 0, // number of reposts
"quote_count": 0, // number of quote posts
"is_reply": false, // true if this post is a reply
"media_type": 19, // 1=photo, 2=video, 8=carousel, 19=text-only
"has_media": false, // true if post contains image/video/carousel
"user": {
"pk": "14803522782",
"username": "flower_popy",
"full_name": "Flower",
"is_verified": false
}
}
],
"count": 17,
"page_info": {
"end_cursor": null,
"has_next_page": false,
"has_previous_page": false,
"start_cursor": null
}
}
Error handling: If error: true is returned, check that the search page loaded correctly by verifying the URL contains the query parameter. If searchResults not found, the page may have failed to render SSR data — retry navigate and wait stable once. If count: 0, no posts matched the keyword.
Hashtag search uses the same URL pattern. Prefix keyword with #:
navigate https://www.threads.com/search/?q=%23{hashtag}&serp_type={filter}
%23AI for #AIwait stableeval "$(python scripts/extract-search-results.py '#AI' --filter top)"The # prefix in the keyword argument is for labeling only; the URL encoding %23 is what Threads uses to identify hashtag searches.
[DOM] filter — values: top (most relevant/popular posts), recent (newest posts first). Set via --filter argument and serp_type URL parameter.
Pagination is not available for unauthenticated access on the search endpoint (has_next_page: false is returned regardless of result volume). Results are limited to approximately 17-18 posts per query without login. For broader coverage, run multiple searches with related keywords.
result.count >= 1 and result.posts[0].id != null and result.posts[0].user.username != null
taken_at (Unix timestamp) client-side after extractionnavigate to the search URL — the search parameters are baked into the URL.Path: {working-directory}/browser-act-skill-forge-memories/threads-scraper-threads-keyword-search.memory.md
Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions; adjust strategy order accordingly.
After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line:
{YYYY-MM-DD}: {what happened} → {conclusion}
Normal execution does not write to the file. Do not record what keywords were used or how many results were returned — those are task outputs, not experience.