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Mechanism
Breaks down how the skill works and what it produces, so you can quickly judge whether it fits your scenario and value.
When the user provides an X (Twitter) conversation id (root tweet id) and wants to collect the full thread — focal tweet plus all replies, sub-replies, and quote chains.
scripts/build-conversation-url.py.TweetDetail GraphQL XHR via network requests, save response.scripts/parse-tweets.py to emit normalized JSON (tweets, cursor).A structured JSON list of every visible tweet in the conversation with author, engagement, media, hashtags, reply mapping, and pagination cursors for downstream analysis.
Use Cases
A brand monitor notices a viral tweet about their product and wants to understand public sentiment. They provide the conversation id to this skill, which harvests the focal tweet and all replies/sub-replies. The normalized JSON with engagement counts and reply trees lets the analyst quantify positive vs negative reactions, identify key critics, and export the thread for sentiment modeling — turning a chaotic reply section into structured data.
A researcher studying online AMA threads uses this skill to capture a long-running Q&A conversation on X. By supplying the root tweet id and letting pagination run, they recover the full discussion including nested replies. The output preserves in_reply_to mapping so the conversation tree is reconstructable, enabling academic analysis of community interaction patterns without manual copying.
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