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Threads

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unifapi-agent
threads

When a workflow needs public Threads (Meta) data through UnifAPI — recent and top post search on a term, profiles, or a handle's posts, replies, and reposts. Also use on "what's being said on Threads about," "newest vs top Threads posts," "Threads profile for," or when another skill (social listening, content opportunity) needs the deterministic Threads read path. Connect via the `unifapi` skill first. Read-only research, never posts.

Overview

Publisherunifapi-agent
Repositoryagents
Skill namethreads
Stars
590
Forks
115
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by unifapi-agent on GitHub. Read the source before you install it.

Installation

Install the Threads AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/unifapi-agent/agents/tree/main/skills/threads-agent/threads
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/unifapi-agent/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/threads-agent/threads .claude/skills/threads
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threads in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Threads on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Threads is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

threads

The deterministic read path for public Threads (Meta) data through UnifAPI. This is a Data Skill: it does not run a marketing job on its own — it names the concrete threads/... operations, response shapes, and gotchas so any text-first listening or content workflow reads from one known recipe instead of rediscovering the surface each time.

Read-only — eyes, not hands. It researches public Threads data and returns cited records; it never posts or replies, and UnifAPI never holds Threads credentials.

Use the unifapi skill for live evidence

Connect once through the shared unifapi skill (OAuth MCP), then call the operations below. Profiles are keyed by {username}. Keep any billing metadata so the output can state record cost.

Response contract

Single-entity endpoints return the object in data:

json
{
  "request_id": "unif_...",
  "data": {},
  "billing": { "records_charged": 1, "balance_remaining": 99 }
}

List endpoints return an array in data plus pagination:

json
{
  "request_id": "unif_...",
  "data": [],
  "pagination": { "has_more": false, "next_cursor": null },
  "billing": { "records_charged": 1 }
}

When pagination.has_more is true, pass pagination.next_cursor as the next request's cursor. Always preserve billing when reporting cost.

Core operations

NeedOperation
Newest posts on a termthreads/search/recent (?q=...)
Most-seen posts on a termthreads/search/top (?q=...)
Profiles on a termthreads/search/profiles (?q=...)
Profile by handlethreads/users/{username}
A handle's poststhreads/users/{username}/posts
A handle's repliesthreads/users/{username}/replies
A handle's repoststhreads/users/{username}/reposts

Need a field not listed here? Use the unifapi skill's get_operation to read the exact schema before calling — but pick the operation from this table, don't discover blind.

Workflow

The deterministic recipes. Pick the one that matches the job; each names exactly what to call.

  1. Set the term. For brand or category listening, call threads/search/recent?q=... (newest) and threads/search/top?q=... (most-seen) together — recent catches the live conversation, top catches the takes that actually spread. Page via next_cursor.
  2. Find handles. Call threads/search/profiles?q=... to surface accounts on the term, then threads/users/{username} for follower size and biography.
  3. Read a profile's activity. Call threads/users/{username}/posts, .../replies, and .../reposts for what a handle says, answers, and amplifies.
  4. Cluster takes. Group text across the recent and top results into questions and opinions; like_count ranks which takes resonate.
  5. Cite everything. Every quote ties back to its post or profile; report billing.records_charged (or estimate when billing metadata is absent).

Shape notes

  • ThreadsUser — keyed by {username}. full_name, biography, follower_count, is_verified, is_private.
  • ThreadsPost — code (the URL segment), text, taken_at, like_count, image_url, video_url, user.

Gotchas

  • search/recent is the newest and search/top is the most-seen — use both for a balanced read; either alone skews recency or popularity.
  • Posts expose like_count only — there are no reply/repost counts on the post object. Gauge spread from the search/top ranking, not a repost number.
  • A private handle (is_private) limits what you can read — surface it.
  • A low balance can silently truncate list pages: check billing.truncated_due_to_balance — when true the page is partial.

Output

Return the records the calling workflow needs, each cited to its post or profile, plus a one-line cost note (records_charged). When this skill is used directly, a compact listening brief is the default:

markdown
**"{term}" on Threads** — {N} recent / {N} top posts. Takes: {themes}. Loudest: "{quote}" ({likes} likes, by @handle). Evidence: {post URLs}. Records: ~{N}.

Related skills

  • social-listening-brief (Social Listening), content-opportunity-brief (Content Strategy) — listening and content-idea work on top of this read path.
  • unifapi — the shared data skill: connect MCP and look up exact schemas with get_operation.

Frequently asked questions

What does the Threads AI skill do?

When a workflow needs public Threads (Meta) data through UnifAPI — recent and top post search on a term, profiles, or a handle's posts, replies, and reposts. Also use on "what's being said on Threads about," "newest vs top Threads posts," "Threads profile for," or when another skill (social listening, content opportunity) needs the deterministic Threads read path. Connect via the `unifapi` skill first. Read-only research, never posts.

Why use Threads on TypingMind?

Because you install it once and use it with any model. Threads is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Threads in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/unifapi-agent/agents/tree/main/skills/threads-agent/threads. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Threads?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Threads?

As many as you like. As long as a model supports skills, you can use Threads with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Threads AI skill free?

Yes. It is published on GitHub by unifapi-agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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