Threads audience insights

Skill sergebulaev/threads-skills/.codex-marketplace/threads-skills/skills/threads-audience-insights

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).From its SKILL.md

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.

2 things to look at

reads credentialsReads from 2 credential sources: `APIFY_TOKEN` and 1 more.
17 stars17 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.

SKILL.md

5.3 KB, ~1.2k tokens by cl100k_base, 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

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)
Profile read - the account's follower count, bio, verified status, and its recent posts ranked by engagement
Action list - what to write more of, which accounts to watch, which formats travel now. Route drafts to threads-post-writer.

Steps

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.
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.
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.
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.
Build the action list. Write-more-of (the winning pattern), watch (specific accounts), formats-that-travel. Route drafts to threads-post-writer.
Deliver the report in the Output shape, with the raw ranked posts attached.

What the read layer exposes

Method	Returns
`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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most docs writing skills give in ~1.2k tokens

Counted across 1,951 of the 3,904 authors here whose files we hold, read 2026-09-06

Use third-person for skill descriptionsin 54 of 1951, across 35 files
Start descriptions with Use whenin 43 of 1951, across 29 files
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Ask one question at a timein 35 of 1951
Offer execution options after saving the planin 33 of 1951, across 24 files
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Design units with clear boundaries and interfacesin 31 of 1951, across 23 files
Announce the skill usage at the startin 30 of 1951
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Said here and by no other author read

Fetch niche posts using Apify
Normalize engagement by follower count
Identify recurring patterns in top posts
Fetch profile data for specified username
Weight replies higher than likes for ranking
Report follower count and bio for profiles

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.