Facebook Page Posts logo

Facebook Page Posts

OrganizationPopular
browser-act
facebook-page-posts

Scrapes posts from any public Facebook Page timeline, returning structured data including post text, author info, engagement metrics (likes/comments/shares), reaction breakdowns (like/love/haha/wow/sad/angry/care), hashtags and external links, and media type. 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. Supports date range filtering (afterTime/beforeTime) and cursor-based pagination for bulk collection.

Overview

Publisherbrowser-act
Repositoryskills
Skill namefacebook-page-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 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-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-posts .claude/skills/facebook-page-posts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Facebook Page 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 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 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 Posts Scraper

Facebook page URL → list of posts with full engagement metrics and text references

Language

All process output to user (progress updates, process notifications) must be in English.

Objective

Extract posts from a public Facebook Page timeline, including post content, engagement counts, reaction breakdowns, and text references (hashtags/links).

Prerequisites

  • The target Facebook page 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 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",   // Numeric page ID used in all subsequent API calls
  "pageUrl": "https://www.facebook.com/cern"
}

API: Fetch posts from page timeline

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

Parameters:

  • page_id: Numeric Facebook page ID (from get-page-id above)
  • --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": [
    {
      "postId": "1417704873732571",                        // Numeric post ID
      "url": "https://www.facebook.com/cern/posts/pfbid…", // Post permalink
      "text": "CERN Council updates European Strategy…",   // Full post text
      "textReferences": [
        {
          "type": "ExternalUrl",                           // "ExternalUrl" or "Hashtag"
          "url": "https://home.cern/...",                  // Clean URL (not l.facebook.com redirect)
          "offset": 731,                                   // Position in text
          "length": 96
        },
        {
          "type": "Hashtag",
          "url": "https://www.facebook.com/hashtag/espp",
          "offset": 296,
          "length": 5
        }
      ],
      "creationTime": 1779460218,                          // Unix timestamp (seconds)
      "user": {
        "id": "100064792144187",                           // Page numeric ID
        "name": "CERN",                                    // Page display name
        "profileUrl": "https://www.facebook.com/cern"     // Page URL
      },
      "likes": 1220,                                       // Total reactions count
      "comments": 44,                                      // Comment count
      "shares": 122,                                       // Share count
      "topReactions": [
        { "name": "Like",  "count": 1108 },
        { "name": "Love",  "count": 92 },
        { "name": "Care",  "count": 9 },
        { "name": "Wow",   "count": 8 },
        { "name": "Haha",  "count": 1 },
        { "name": "Sad",   "count": 1 },
        { "name": "Angry", "count": 1 }
      ],
      "topReactionsCount": 1220,                           // Same as likes, total reactions
      "media": {
        "type": "Photo",                                   // "Photo", "Video", or null for text-only
        "id": "photo_id_string",                           // Media asset ID
        "viewsCount": null                                 // Video view count; null for photos
      },
      "feedbackId": "ZmVlZGJhY2s6MTQxNzcw…"               // Base64 feedback ID
    }
  ],
  "pagination": {
    "endCursor": "Cg8Ob3JnYW5pY19jd…",                   // Pass to --cursor for next page
    "hasNextPage": true                                    // false when no more posts
  }
}

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 — check experience notes for updates.

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].likes is non-null

Known Limitations

  • media.id is returned but media thumbnail/photo URLs are not included in the API response for this query; only type and ID are available
  • viewsCount is only populated for Video-type attachments; null for photos
  • feedbackId is present but detailed reaction dialog data (individual reactor profiles) requires a separate API call not covered by this Skill
  • The doc_id: 27278869228466784 is a Facebook internal query ID that may change when Facebook deploys updates; if all calls return errors, the doc_id may need to be recaptured via HAR recording on the page
  • 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 the command templates 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 work across them — each session has an independent fingerprint so rate limits apply per session
  • Test before batch execution: After writing a batch script, you must first test with 1-2 items to verify the script runs correctly; only then run the full batch. Never skip testing and execute in batch directly
  • 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-posts.memory.md

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 keywords were used or how many results 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 Posts AI skill do?

Scrapes posts from any public Facebook Page timeline, returning structured data including post text, author info, engagement metrics (likes/comments/shares), reaction breakdowns (like/love/haha/wow/sad/angry/care), hashtags and external links, and media type. 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 med...

Why use Facebook Page Posts on TypingMind?

Because you install it once and use it with any model. Facebook Page 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 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-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 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 Posts?

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

Is the Facebook Page 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇