head to head · open source
social-media-research-skills vs shepherd
social-media-research-skills has 5,319 GitHub stars, 111 forks, 3 open issues and last shipped 2 months ago. shepherd has 2,503 stars, 219 forks, 12 open issues and last shipped 2 months ago. social-media-research-skills leads on adoption by 113% (5,319 vs 2,503 stars). social-media-research-skills is written in Python under MIT; shepherd is written in Python under MIT. social-media-research-skills has attracted 2% as many forks as stars, shepherd 9%. shepherd was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (workflow-automation), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| social-media-research-skills | shepherd | |
|---|---|---|
| GitHub stars | ★ 5.3K | ★ 2.5K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-08-26 | 2026-09-09 |
| Forks | ⑂ 111 | ⑂ 219 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick social-media-research-skills if
- You weight community size — 5.3K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick shepherd if
- You want the shepherd feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and shepherd fits your workflow better
About social-media-research-skills
Social Media Research Skills for AI Agents Practical AI agent skills for social media research, powered by ScrapeCreators. These skills help agents find outlier posts, mine comments, summarize transcripts, tear down competitors, analyze ad libraries, and turn public social data into useful business artifacts. This is not just endpoint routing. The goal is to give your AI agent complete research workflows it can run across TikTok, Instagram, YouTube, Reddit, X/Twitter, LinkedIn, Facebook, Threads, Bluesky, Pinterest, Rumble, ad libraries, and more. Install Works with Claude Code, Cursor, OpenAI Codex, GitHub…
read the full social-media-research-skills overview →
About shepherd
Shepherd: Programmable Meta Agents via Reversible Execution Traces [!IMPORTANT] Shepherd is in early alpha and under active development. APIs may still change between releases. Feedback and issues are very welcome! Install Quickstart Permissions Examples Docs Citation Shepherd is a runtime substrate for agent work that needs inspection, reversibility, and supervision. It records agent runs as durable, inspectable execution traces, with retained workspace outputs that can be reviewed before they are selected, applied, released, or discarded. Platforms. Shepherd requires Python 3.11+ . OS level grant enforcement…
read the full shepherd overview →
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Frequently asked questions
Is social-media-research-skills or shepherd more popular?
social-media-research-skills has 5,319 GitHub stars and shepherd has 2,503. social-media-research-skills has the larger community by that measure.
Are social-media-research-skills and shepherd free?
Both are open source. social-media-research-skills is licensed under MIT and shepherd under MIT. Neither carries a licence fee.
What is the difference between social-media-research-skills and shepherd?
social-media-research-skills is written in Python and shepherd in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, social-media-research-skills or shepherd?
Choose social-media-research-skills if you want the larger community (5,319 stars) or its MIT licence terms. Choose shepherd if its feature set, stack or MIT licence fits better. Both are self-hostable.