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Social Fetch

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coreyhaines31
social-fetch

When you or another skill needs to fetch the content of a social media post by URL — tweet, X thread, LinkedIn post, Instagram post, TikTok video, Bluesky post, Reddit thread, Mastodon status, Threads post, Hacker News thread. Returns normalized structured data (author, posted_at, text, engagement counts, media URLs, replies if requested) regardless of platform. Tries strategies in order: direct API (Bluesky, Mastodon, HN, Reddit), agent-browser with modal dismissal (LinkedIn, X preview), Wayback Machine (older posts), paid APIs (ScrapeCreators / Apify — only if env keys present). Triggers on "/social-fetch <url>," "fetch this tweet," "fetch this post," "what does this LinkedIn say," "read this thread," "pull this post." Used by deep-research (citing specific posts), jab-hook (inspiration account analysis), business-brainstorm (competitor / operator commentary).

Overview

Publishercoreyhaines31
Repositorymakerskills
Skill namesocial-fetch
Stars
855
Forks
71
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by coreyhaines31 on GitHub. Read the source before you install it.

Installation

Install the Social Fetch 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/coreyhaines31/makerskills/tree/main/skills/social-fetch
  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/coreyhaines31/makerskills.git /tmp/makerskills
mkdir -p .claude/skills
cp -r /tmp/makerskills/skills/social-fetch .claude/skills/social-fetch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Social Fetch 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 Social Fetch 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 Social Fetch 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.

/social-fetch — Pull any social post by URL

Normalized fetcher for social posts across platforms. Detects platform from URL, tries strategies in order, returns the same JSON shape regardless of source.

Step 1 — Detect platform

URL patternPlatform
x.com/<user>/status/<id> or twitter.com/<user>/status/<id>x (Twitter)
linkedin.com/posts/<slug> or linkedin.com/feed/update/urn:li:activity:<id>linkedin
linkedin.com/in/<handle> (profile, recent activity)linkedin-profile
instagram.com/p/<id> or instagram.com/reel/<id>instagram
tiktok.com/@<user>/video/<id>tiktok
bsky.app/profile/<handle>/post/<rkey>bluesky
reddit.com/r/<sub>/comments/<id>/...reddit
<mastodon-instance>/@<user>/<id> (e.g. mastodon.social, hachyderm.io)mastodon
threads.net/@<user>/post/<id>threads
news.ycombinator.com/item?id=<id>hn
youtube.com/watch?v=<id> or youtu.be/<id>→ defer to watch-video

If the URL doesn't match any pattern, ask the user what platform it is.

Step 2 — Pick strategy chain

Read references/strategies.md for the per-platform strategy chain. Each platform has 2–5 strategies tried in order.

Key principles:

  • Free strategies first (direct APIs, agent-browser)
  • Paid only as fallback (ScrapeCreators / Apify) — and only if the env key is set
  • Bluesky / Mastodon / HN / Reddit are free + reliable (public APIs)
  • X / LinkedIn / Instagram / TikTok / Threads need paid or scraping fallback for full data

Step 3 — Execute strategy

For each strategy in the chain:

  1. Try it
  2. If success: parse → normalize → return
  3. If failure (404, 402, auth wall, empty response): note the failure and try the next strategy

After exhausting the chain, return a clear error: which strategies were tried, why each failed, and what's needed to unlock (e.g., "Add $SCRAPECREATORS_API_KEY for X — see references/auth-keys.md").

Step 4 — Normalize output

Return this shape regardless of platform (see references/output-schema.md for the full spec + platform-specific examples):

json
{
  "platform": "x",
  "url": "https://x.com/example/status/1234567890",
  "fetched_at": "2026-06-17T14:35:00Z",
  "raw_source": "scrapecreators",
  "author": {
    "handle": "@example",
    "name": "the user Ganim",
    "verified": true
  },
  "posted_at": "2026-06-17T16:53:00Z",
  "text": "The 80/20 of a useful AI second brain: ...",
  "media": [],
  "engagement": {
    "likes": 51,
    "reposts": 13,
    "replies": 9,
    "bookmarks": 7,
    "views": 32700
  },
  "is_thread": true,
  "thread": [],
  "replies": []
}

Fields with no equivalent on a platform (e.g., bookmarks on Mastodon) get null, not 0. Missing data is different from zero data.

Step 5 — Optional enrichments

Based on flags / asks:

FlagBehavior
--with-repliesFetch top-level replies (1 hop). Costs extra API quota.
--threadIf the post is part of a thread by the same author, fetch the whole thread.
--rawInclude the raw API/scrape response in the output (for debugging)
--mediaDownload media files (images/videos) to ~/Documents/social-fetches/<platform>-<id>/

Default: just the post itself, no replies, no media download (just URLs).

Step 6 — Cache (optional)

If ~/Documents/social-fetches/_cache/ exists, cache successful fetches there by {platform}-{id}.json for 24h. Saves API quota when the same post is referenced repeatedly across skills.

Skip cache if --no-cache flag is set or for --with-replies / --thread (likely-stale).

Composes with

  • deep-research — cite specific posts in research briefs. When research surfaces a relevant tweet/post URL, fetch and include in the brief.
  • jab-hook — pull recent posts from inspiration accounts for deeper format analysis (currently uses agent-browser inline; should call this skill instead).
  • business-brainstorm — pull competitor / operator commentary as evidence during scoring.
  • second-brain — capture a post into raw/ with the tweet- / bookmark- prefix; the structured output makes for cleaner raw files than a screenshot or copy-paste.
  • watch-video — for YouTube URLs (or any video — Loom, Vimeo, Riverside, MP4), route there instead.

Known limits

  • X: free strategies return tweet preview only (text, author, basic engagement). Full thread + replies need $SCRAPECREATORS_API_KEY or $APIFY_API_TOKEN.
  • LinkedIn: agent-browser works for profile recent-activity (after dismissing the modal). Specific post URLs (linkedin.com/posts/...) often need paid fallback.
  • Instagram / TikTok / Threads: heavy anti-bot. Paid fallback strongly recommended.
  • Bluesky / Mastodon / HN / Reddit: free + reliable.
  • Private / deleted posts: nothing helps. Try Wayback Machine for deleted content.

If a platform consistently fails on free strategies and the user uses it often, prompt to set up the paid key (see references/auth-keys.md).

Notes on quality

  • Strategy chain, not single-source. Every platform has a fallback ladder (native oEmbed → agent-browser → SCS API → Apify). If one step fails, degrade gracefully to the next. Never fail hard on the first attempt.
  • Structured output over screenshots. Downstream skills (jab-hook, deep-research, second-brain) need JSON with author + text + engagement fields, not an image. Even when the underlying strategy is a screenshot, extract text before returning.
  • Cache aggressively, invalidate honestly. 24h TTL on ~/Documents/social-fetches/_cache/ prevents API burn when the same post is referenced across multiple skills in a session. --with-replies / --thread skip cache because replies age fast.
  • Respect paid-key economics. ScrapeCreators / Apify calls cost real money. Prompt before hitting paid strategies if the user hasn't confirmed they want depth. Free strategies first, always.
  • Media download is opt-in. Default is post text only; --media downloads images/videos. Silent media downloads eat disk quickly.
  • Private / deleted content is a hard stop. No strategy chain rescues private accounts or deleted posts. Suggest Wayback Machine for deleted content and stop.
  • Rate-limits are per-platform. X free strategies hit rate limits fast; LinkedIn agent-browser burns session fingerprints. Space out calls in loops or the workflow degrades to worse-than-manual.

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 Social Fetch AI skill do?

When you or another skill needs to fetch the content of a social media post by URL — tweet, X thread, LinkedIn post, Instagram post, TikTok video, Bluesky post, Reddit thread, Mastodon status, Threads post, Hacker News thread. Returns normalized structured data (author, posted_at, text, engagement counts, media URLs, replies if requested) regardless of platform. Tries strategies in order: direct API (Bluesky, Mastodon, HN, Reddit), agent-browser with modal dismissal (LinkedIn, X preview), Wayback Machine (older posts), paid APIs (ScrapeCreators / Apify — only if env keys present). Triggers...

Why use Social Fetch on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coreyhaines31/makerskills/tree/main/skills/social-fetch. 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 Social Fetch?

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 Social Fetch?

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

Is the Social Fetch AI skill free?

Yes. It is published on GitHub by coreyhaines31 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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