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Threads audience insights

Skill sergebulaev/threads-skills/skills/threads-audience-insights

Threads marketing skills for Claude Code and Codex: posts, multi-post threads, hooks, replies, and a weekly plan in your voice. Publish via Publora. Open source, MIT.

Install
npx -y skills add sergebulaev/threads-skills --skill threads-audience-insights

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Read your Threads (Meta) audience and niche from real data. Scan a niche query or hashtag for the top posts right now with likes, replies, reposts, and quotes, and pull a profile (yours or a competitor) for follower count, bio, verified status, and recent posts with engagement. Powered by Apify, no login. Triggers on "analyze Threads", "what is working on Threads", "top posts for", "competitor Threads profile", "niche scan", "follower count". Not for writing posts (use threads-post-writer).

SKILL.md

5.3 KB, as published. Nobody here has run it

Threads Audience Insights

Turn real Threads data into a read on what is working: which posts in your niche are landing right now and why, and how an account performs (its follower count, its recent posts, the engagement each one pulls). This is the read layer, so the skill sees actual numbers instead of guessing.

One honest limit: Threads does not expose a usable list of who liked or engaged with a post. That data is cookie-gated, capped at around 100, and has no public API. So there is no engager roster here. The signal is niche discovery + profile stats + per-post engagement counts (likes, replies, reposts, quotes), which on Threads is what tells you whether a post traveled. A reply is the heaviest ranking signal on Threads, so replies matter more than raw likes when you read a winner.

When to use

  • "What is working on Threads for [topic]" / "scan the niche for top posts"
  • "Analyze [my / a competitor's] Threads profile"
  • "How many followers does [account] have, and what are they posting"
  • "Which hooks and formats are traveling on Threads right now"

Not for writing a post or a thread (use threads-post-writer) or reverse-engineering one hook (use threads-hook-extractor).

Setup (optional)

The read layer uses Apify (no login, no cookies). Get a free token at https://console.apify.com/settings/integrations and set APIFY_TOKEN in .env. No token? Paste the posts you already have and the skill runs the same analysis on them.

Input

  • A niche query or hashtag (e.g. "AI marketing", "#buildinpublic"), or
  • A username (yours or a competitor's), or
  • Both, plus the goal (niche scan / profile read)

Output

  1. Niche scan - top posts for the query ranked by engagement, with the pattern behind the winners (hook shape, length, single vs thread, reply-bait vs statement)
  2. Profile read - the account's follower count, bio, verified status, and its recent posts ranked by engagement
  3. Action list - what to write more of, which accounts to watch, which formats travel now. Route drafts to threads-post-writer.

Steps

  1. Pull the data. For a niche: lib.ApifyClient().fetch_niche_posts(query, max_items=30, sort="top"). For a profile: fetch_profile(username). Falls back to pasted data if no token.
  2. Rank by engagement. Sort by a blend of replies + likes + reposts + quotes. Weight replies highest (heaviest Threads ranking signal). Normalize against the author's follower count when you have it, so a small account's breakout is not buried under a big account's baseline.
  3. Extract the pattern. For the top posts, name what they share: the hook shape (one-liner, confession, list, question), the length, single vs thread, whether it opens a reply loop. That is the repeatable part.
  4. Read the profile. From fetch_profile, report follower count, bio, verified status, and which of its recent posts pulled the most engagement. Note the format mix.
  5. Build the action list. Write-more-of (the winning pattern), watch (specific accounts), formats-that-travel. Route drafts to threads-post-writer.
  6. Deliver the report in the Output shape, with the raw ranked posts attached.

What the read layer exposes

MethodReturns
fetch_niche_posts(query, max_items, sort)top/recent posts for a niche query or hashtag: text, likes, replies, reposts, quotes, author, verified, url
fetch_profile(username)profile stats: username, full_name, followers, biography, verified, bio_links, plus recent posts with engagement

There is intentionally no engager/liker method. Threads walls that data; do not imply otherwise.

Hard rules

Global voice rules: see root SKILL.md Voice rules. Additional skill-specific rules:

  • Be honest that there is no engager or liker list on Threads (cookie-gated, ~100 cap, no public API). The read is discovery + profile stats + per-post counts, not a roster of who engaged.
  • Weight replies over likes. A reply is the heaviest ranking signal on Threads, so a post with many replies beats one with many likes at the same total.
  • Normalize engagement by follower count before calling a post a winner, or you extract "big account" effects, not "good post" effects.
  • Never invent a post, a number, or a pattern. If the search returns thin, say so and pull a known account's profile instead.
  • A pattern is only a pattern if it recurs across several top posts, not one.
  • fetch_niche_posts runs a minimum of 20 posts per Apify run even when you ask for fewer (actor floor); it trims the return to max_items.

Related skills

  • threads-post-writer - write more of what the data shows is working
  • threads-hook-extractor - reverse-engineer a hook from a top-performing post
  • threads-reply-drafter - reply to a high-traction post the scan surfaced
  • threads-content-planner - feed the winning patterns into a weekly plan

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.