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xhoantranxhoantran/scrapeio-agent/skills/comment-insights/SKILL.md

comment-insights

Skill66 starsChanged 6 days agoMITView on GitHub

Pull comments from TikTok, Instagram, YouTube, X or Facebook posts and turn them into insights - top questions, objections, sentiment, feature requests, buying signals and audience language. Use when the user asks what people say about a post, product, creator or brand, or wants voice-of-customer research. Uses the ScrapeIO API.

  • Reads credentials

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Pick posts: Use the user's links, or take the top posts by plays from a profile's recent posts.

    line 20
  2. Very specific:

    With the scrapeio MCP server: use the named tool when it exists (for example tiktok_profile).

    line 34

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Comment insights

Comment endpoints (all take the post url)

  • TikTok: tiktok_comments (most liked first), replies via tiktok_comment_replies
  • Instagram: instagram_comments (sort: popular for the top 15, recent to page), replies via instagram_comment_replies
  • YouTube: youtube_comments (order: relevance or time), replies via youtube_comment_replies
  • X: twitter_replies
  • Facebook: facebook_comments, replies via facebook_comment_replies (pass feedback_id and expansion_token from the comment)

Each page costs 1 credit. Ask before pulling more than 10 pages per post.

Steps

  1. Pick posts. Use the user's links, or take the top posts by plays from a profile's recent posts.
  2. Pull comments, 1 to 5 pages per post. Keep text, likes, replies and created_at. Pull replies only for comments with many replies.
  3. Clean: drop emoji-only, spam and repeated bot text, but count them (bot share is a useful signal).
  4. Group into themes: questions, complaints and objections, praise, feature or product requests, purchase intent ("where can I buy", "link?"), comparisons with other brands.
  5. Weight themes by comment likes, not just counts.

Output

Top themes with a count, total likes, and 2 or 3 real example comments each (quoted exactly). Then a short "what to do with this" section: FAQ answers to write, objections to address, content ideas. Note how many comments were read and the credits spent.

How to call ScrapeIO

Needs SCRAPEIO_API_KEY. If it is missing, ask the user to get a free key at https://scrapeio.com/login (100 credits, no card). Never print the key.

  • With the scrapeio MCP server: use the named tool when it exists (for example tiktok_profile). For any other path, use call_endpoint with { "path": "/v1/...", "params": { ... } }.
  • Without MCP: curl -s "https://scrapeio.com<path>?<params>" -H "x-api-key: $SCRAPEIO_API_KEY".
  • Answers are in data; lists are data.items with data.cursor and data.has_more. Fields are the same on every platform: handle, followers, plays, likes, comments, shares, created_at, url.
  • 403 (private) and 404 (not found) are free: skip and move on. Retry 502 once. 402 means the user needs credits (https://scrapeio.com/pricing).
  • Watch credits_remaining and tell the user before a step would spend more than they expect.

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

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