Facebook Page Profile Posts logo

Facebook Page Profile Posts

OrganizationPopular
browser-act
facebook-page-profile-posts

Scrapes posts from any public Facebook Page or personal Profile timeline, returning structured data including post text, author info with profile picture, engagement metrics (likes/comments/shares), full reaction breakdown (Like/Love/Wow/Haha/Sad/Angry/Care as both array and flat counts), hashtags and external links, media assets with full thumbnail URLs and dimensions, page ad library status, and collaborators. Use when user wants to scrape Facebook posts, extract Facebook page content, get Facebook post data, collect Facebook engagement stats, download Facebook posts, monitor a Facebook page, crawl Facebook timeline, get Facebook reactions, get like count comment count share count from Facebook, Facebook post bulk export, Facebook social media analytics, get profile picture, get page ad library status, fetch Facebook collaborators. Supports date range filtering (afterTime/beforeTime) and cursor-based pagination for bulk collection.

Overview

Publisherbrowser-act
Repositoryskills
Skill namefacebook-page-profile-posts
Stars
6.1K
Forks
310
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by browser-act on GitHub. Read the source before you install it.

Installation

Install the Facebook Page Profile Posts 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/browser-act/skills/tree/main/solutions/social-listening/facebook-page-profile-posts
  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/browser-act/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/solutions/social-listening/facebook-page-profile-posts .claude/skills/facebook-page-profile-posts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Facebook Page Profile Posts 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 Facebook Page Profile Posts 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 Facebook Page Profile Posts 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.

Facebook — Page / Profile Posts Scraper

Facebook page or profile URL → list of posts with full fields including media thumbnails, author profile pics, page ad library status, and flat reaction counts

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Extract posts from a public Facebook Page or personal Profile timeline, including post content, engagement counts, full reaction breakdown, media thumbnails, author profile picture, page ad library status, and text references (hashtags/links).

Prerequisites

  • The target Facebook page or profile is open in the browser (e.g., https://www.facebook.com/cern)
  • User is logged into Facebook (user avatar visible in the top right)

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

2. Login Verification

If login status for Facebook has been confirmed in the current session → skip this step.

Otherwise: navigate to https://www.facebook.com and check:

  • User avatar or name visible in top right → logged in, continue
  • Login button visible → not logged in, inform the user and assist with login

User refuses or cannot log in → terminate execution.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; it is recommended to use the bash tool for execution.

API: Resolve Facebook page URL to numeric page ID

eval "$(python scripts/get-page-id.py '{page_url}')"

Parameters:

  • page_url: Full Facebook page or profile URL, e.g. https://www.facebook.com/cern

Must run while the target page is open (or any Facebook page is open) so the browser has Facebook cookies.

Output example:

json
{
  "pageId": "100064792144187",
  "pageUrl": "https://www.facebook.com/cern"
}

Error handling: If pageId is not found (page not found or not logged in), verify login status and that the URL is a valid Facebook page.

API: Fetch posts from page/profile timeline

eval "$(python scripts/get-page-posts.py '{page_id}' --page-url '{page_url}' --cursor '{cursor}' --after-time {after_time} --before-time {before_time} --count {count})"

Parameters:

  • page_id: Numeric Facebook page/profile ID (from get-page-id above)
  • --page-url: Original input URL (used for facebookUrl, inputUrl, pageName fields); omit if not needed
  • --cursor: Pagination cursor string from previous response pagination.endCursor; omit or pass null for first page
  • --after-time: Unix timestamp (seconds); only return posts after this time; omit or pass null for no filter
  • --before-time: Unix timestamp (seconds); only return posts before this time; omit or pass null for no filter
  • --count: Number of posts per batch, default 5, max recommended 10

Output example:

json
{
  "posts": [
    {
      "facebookUrl": "https://www.facebook.com/cern",
      "postId": "1417704873732571",
      "pageName": "cern",
      "url": "https://www.facebook.com/cern/posts/pfbid0n87...",
      "time": "2026-05-22T14:30:18.000Z",
      "timestamp": 1779460218,
      "user": {
        "id": "100064792144187",
        "name": "CERN",
        "profileUrl": "https://www.facebook.com/cern",
        "profilePic": "https://scontent-lax3-1.xx.fbcdn.net/v/t39.30808-1/..."
      },
      "collaborators": [],
      "text": "Post text content here...",
      "textReferences": [
        { "type": "ExternalUrl", "url": "https://home.cern/...", "offset": 731, "length": 96 },
        { "type": "Hashtag", "url": "https://www.facebook.com/hashtag/espp", "offset": 296, "length": 5 }
      ],
      "link": "https://home.cern/...",
      "likes": 1285,
      "comments": 45,
      "shares": 125,
      "topReactions": [
        { "name": "Like", "count": 1167 },
        { "name": "Love", "count": 98 },
        { "name": "Care", "count": 9 },
        { "name": "Wow", "count": 8 },
        { "name": "Haha", "count": 1 },
        { "name": "Sad", "count": 1 },
        { "name": "Angry", "count": 1 }
      ],
      "topReactionsCount": 1285,
      "reactionLikeCount": 1167,
      "reactionLoveCount": 98,
      "reactionCareCount": 9,
      "reactionWowCount": 8,
      "reactionHahaCount": 1,
      "reactionSadCount": 1,
      "reactionAngryCount": 1,
      "media": [
        {
          "__typename": "Photo",
          "id": "1417649567071435",
          "thumbnail": "https://scontent-lax3-1.xx.fbcdn.net/v/t39.30808-6/...",
          "photo_image": { "uri": "https://scontent-lax3-1.xx.fbcdn.net/...", "width": 960, "height": 540 },
          "url": "https://www.facebook.com/photo.php?fbid=1417649567071435&...",
          "ocrText": "Artistic representation of the Future Circular Collider",
          "feedback": { "can_viewer_comment": true, "id": "ZmVlZGJhY2s6..." }
        }
      ],
      "feedbackId": "ZmVlZGJhY2s6MTQxNzcwNDg3...",
      "facebookId": "100064792144187",
      "topLevelUrl": "https://www.facebook.com/100064792144187/posts/1417704873732571",
      "pageAdLibrary": {
        "is_business_page_active": false,
        "id": "169005736520113"
      },
      "inputUrl": "https://www.facebook.com/cern"
    }
  ],
  "pagination": {
    "endCursor": "Cg8Ob3JnYW5pY19j...",
    "hasNextPage": true
  }
}

For video/reel posts, media[0] has these fields instead of photo_image:

json
{
  "__typename": "Video",
  "id": "1540366350780488",
  "thumbnail": "https://scontent-lax3-1.xx.fbcdn.net/v/t15.5256-10/...",
  "url": "https://www.facebook.com/reel/2188342222016401/",
  "playableUrlSd": "https://video.xx.fbcdn.net/...",
  "playableUrlHd": "https://video.xx.fbcdn.net/...",
  "viewsCount": null
}

Error handling: If response contains "error": true, check that the target page is open and the user is logged in, then retry once. If the error persists, the doc_id may have expired — see Known Limitations for recapture steps.

Pagination

API Pagination: cursor-based. Pass pagination.endCursor from each response as --cursor in the next call. Start value: omit --cursor (first page). Termination: pagination.hasNextPage === false.

Success Criteria

posts.length >= 1 AND posts[0].postId is non-null AND posts[0].user.id is non-null AND posts[0].user.profilePic is non-null

Known Limitations

  • viewsCount for video posts is always null — Facebook does not include video view counts in the timeline feed GraphQL response; this field requires a separate video-specific API call not covered by this Skill
  • media.playableUrlSd/Hd requires the user to be logged in; unauthenticated sessions may return null
  • collaborators is an empty array for most posts (only populated when the post has tagged co-authors)
  • The doc_id: 27278869228466784 is a Facebook internal query ID that may change when Facebook deploys updates; if all calls return "Unexpected response format" errors, recapture via:
    1. Open any Facebook page while logged in
    2. Scroll down to trigger a new batch of posts
    3. network requests --filter api/graphql --method POST
    4. Find the request with X-FB-Friendly-Name: ProfileCometTimelineFeedRefetchQuery
    5. Extract doc_id from the POST body and update the value in scripts/get-page-posts.py
  • pageName is derived from the URL path and may be incorrect for profile pages with numeric IDs (e.g., https://www.facebook.com/100012345 → pageName = 100012345)
  • Requires an active Facebook login session; public/unauthenticated access is not supported
  • Date filtering (afterTime/beforeTime) applies to the timeline cursor position, not a strict server-side filter; posts near the boundary may occasionally appear outside the specified range

Execution Efficiency

  • Batch orchestration: Write a bash script to loop through paginated calls serially within a single session; do not parallelize within one browser (prone to triggering anti-scraping restrictions). Add a 1–2 second delay between paginated calls to avoid rate limiting. To increase throughput, open multiple browser sessions and distribute pages across them — each session has an independent fingerprint so rate limits apply per session
  • Test before batch execution: After writing a batch script, test with 1-2 items first to verify the script runs correctly; only then run the full batch
  • Reduce redundant pre-operations: Run get-page-id once per page URL and reuse the result for all paginated calls
  • Error resumption: Save results page by page during batch processing; on failure, resume from the last successful endCursor rather than starting over

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/facebook-posts-scraper-facebook-page-profile-posts.memory.md (working directory is determined by the Agent running the Skill, typically the project root or current working directory)

Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.

After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line: {YYYY-MM-DD}: {what happened} → {conclusion}

Normal execution does not write to the file. Do not record what pages were scraped or how many posts were returned — those are task outputs, not experience.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Facebook Page Profile Posts AI skill do?

Scrapes posts from any public Facebook Page or personal Profile timeline, returning structured data including post text, author info with profile picture, engagement metrics (likes/comments/shares), full reaction breakdown (Like/Love/Wow/Haha/Sad/Angry/Care as both array and flat counts), hashtags and external links, media assets with full thumbnail URLs and dimensions, page ad library status, and collaborators. Use when user wants to scrape Facebook posts, extract Facebook page content, get Facebook post data, collect Facebook engagement stats, download Facebook posts, monitor a Facebook p...

Why use Facebook Page Profile Posts on TypingMind?

Because you install it once and use it with any model. Facebook Page Profile Posts 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 Facebook Page Profile Posts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/browser-act/skills/tree/main/solutions/social-listening/facebook-page-profile-posts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Facebook Page Profile Posts?

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 Facebook Page Profile Posts?

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

Is the Facebook Page Profile Posts AI skill free?

Yes. It is published on GitHub by browser-act 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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