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Instagram

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unifapi-agent
instagram

When a workflow needs public Instagram data through UnifAPI — profiles and follower size, posts and reels with engagement, post comments, location pages, or search. Also use on "research these IG creators," "pull posts for," "Instagram follower count," "comments on this post," "is this a paid partnership," or when another skill (KOL pricing, creator shortlist, audience fit) needs the deterministic Instagram read path. Connect via the `unifapi` skill first. Read-only research, never posts or DMs.

Overview

Publisherunifapi-agent
Repositoryagents
Skill nameinstagram
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 Instagram 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/instagram-agent/instagram
  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/instagram-agent/instagram .claude/skills/instagram
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Instagram 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 Instagram 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 Instagram 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.

instagram

The deterministic read path for public Instagram data through UnifAPI. This is a Data Skill: it does not run a marketing job on its own — it names the concrete instagram/... operations, response shapes, and gotchas so any creator-first workflow (shortlisting, audience-fit, pricing context) reads from one known recipe instead of rediscovering the surface each time.

Read-only — eyes, not hands. It researches public Instagram data and returns cited records; it never posts, comments, or DMs, and UnifAPI never holds Instagram 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} and posts by their {shortcode} (the URL segment) — instagram/resolve/* maps between username↔user-id and shortcode↔media-id when you need it. 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
Profile by handleinstagram/users/{username}
Posts / reelsinstagram/users/{username}/posts · .../reels
Stories / highlightsinstagram/users/{username}/stories · .../highlights
Followers / followinginstagram/users/{username}/followers · .../following
Tagged postsinstagram/users/{username}/tagged-posts
Post by shortcodeinstagram/posts/{shortcode}
Post comments / repliesinstagram/posts/{shortcode}/comments
Discoverinstagram/search (?q=...) · instagram/explore
Location page + postsinstagram/locations/{id} · instagram/locations/{id}/posts
Resolve handle/shortcodeinstagram/resolve/user-id · instagram/resolve/shortcode

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. Resolve a profile. Call instagram/users/{username} for follower_count, media_count, is_private, and is_verified.
  2. Read content and engagement. Call instagram/users/{username}/posts and .../reels; each InstagramPost carries like_count, comment_count, and play_count/view_count for video — and is_paid_partnership to flag sponsored content. Page via next_cursor.
  3. Read reaction. Call instagram/posts/{shortcode}/comments (and the replies endpoint) for sentiment on a top post.
  4. Discover new handles. Call instagram/search?q=..., instagram/explore, or instagram/locations/{id}/posts, then resolve the surfaced handles and loop to step 1.
  5. Map a network or audience. Call instagram/users/{username}/followers, .../following, or .../tagged-posts (who features them) for reach work.
  6. Cite everything. Every figure ties back to the post or profile it came from; report billing.records_charged (or estimate when billing metadata is absent).

Shape notes

  • InstagramUser — keyed by {username}. follower_count, following_count, media_count, is_verified, is_private.
  • InstagramPost — keyed by {shortcode}. caption, like_count, comment_count, play_count, view_count, taken_at, media_type, is_paid_partnership, user, location, carousel.
  • InstagramComment — text, like_count, child_comment_count.
  • InstagramLocation — name, lat, lng, media_count.

Gotchas

  • Profiles are keyed by {username}, posts by {shortcode} — both read from the public IG URL. Use instagram/resolve/* only when you need the numeric user-id or media-id.
  • A private account (is_private) exposes the profile but limits media — surface it, don't paper over thin coverage.
  • is_paid_partnership marks disclosed sponsored posts — useful evidence for pricing and brand-safety, report 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 creator summary is the default:

markdown
**@handle** — {followers} followers, {media} posts, verified {yes/no}. Recent posts: avg {likes} likes / {comments} comments. Paid partnerships: {N}. Evidence: {post URLs}. Records: ~{N}.

Related skills

  • kol-pricing, creator-shortlist, audience-fit-check (Influencer Marketing) — price and vet Instagram creators 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 Instagram AI skill do?

When a workflow needs public Instagram data through UnifAPI — profiles and follower size, posts and reels with engagement, post comments, location pages, or search. Also use on "research these IG creators," "pull posts for," "Instagram follower count," "comments on this post," "is this a paid partnership," or when another skill (KOL pricing, creator shortlist, audience fit) needs the deterministic Instagram read path. Connect via the `unifapi` skill first. Read-only research, never posts or DMs.

Why use Instagram on TypingMind?

Because you install it once and use it with any model. Instagram 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 Instagram in TypingMind?

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

Which AI models can use Instagram?

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 Instagram?

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

Is the Instagram 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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