Social Media Research Skills Pack
Ready-to-use AI agent skills for social media research: find outlier posts, mine comments, and analyze competitors.
- Source repo
- ScrapeCreators/social-media-research-skills
- Stars
- ★ 4.1k
- Last updated
- 1mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 27/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specificChatGPT · Codex · Claude Code
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A content creator wants to find which of their recent YouTube Shorts are outperforming their usual metrics and what patterns they share.
- Not a fit if
- Users unwilling to pay for the ScrapeCreators API
- Environments without Node.js or internet access
- Source review
- 27/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
ScrapeCreators/social-media-research-skills is a collection of practical AI agent skills for social media research, built on ScrapeCreators' data extraction API. The repository includes 13 workflow-oriented skills, each designed to turn raw social data into actionable business artifacts. Examples include outlier-post-finder, transcript-intelligence, comment-mining, competitor-social-research, ad-library-teardown, trend-discovery, influencer-prospecting, audience-research, social-listening-brief, product-demand-research, creator-profile-teardown, and content-repurposing. The skills rely on the scrapecreators-api skill as the underlying data layer, routing requests to the appropriate ScrapeCreators endpoints. Installation is straightforward via npx skills add, and the skills work with AI agents that support the Agent Skills specification, such as Claude Code, Cursor, OpenAI Codex, and GitHub Copilot. The design emphasizes workflow-first approaches, public-data-only constraints, cited outputs, and baseline-aware analysis to ensure outputs are not just raw JSON but useful reports and recommendations.
The repository provides pre-built workflow skills that guide an AI agent through specific social media research tasks. For instance, outlier-post-finder fetches recent posts from a creator and identifies those that outperform their baseline; transcript-intelligence analyzes video transcripts from multiple platforms and extracts summaries, hooks, and quotes; comment-mining scans comments for questions, objections, and purchase intent; competitor-social-research compares competitors' strategies on TikTok and Instagram; ad-library-teardown examines active Meta, Google, and LinkedIn ads; trend-discovery uncovers trending topics and formats; influencer-prospecting builds creator prospect lists; audience-research evaluates audience fit; social-listening-brief generates a multi-source brand or topic brief; product-demand-research validates product ideas; creator-profile-teardown analyzes why an account works; and content-repurposing transforms videos and posts into LinkedIn articles and newsletters. Each skill uses the scrapecreators-api skill to make API calls and outputs structured artifacts like CSV files, reports, and recommendation tables.
- A content creator wants to find which of their recent YouTube Shorts are outperforming their usual metrics and what patterns they share.
- A brand marketer needs to tear down a competitor's Facebook and Google ads to extract hooks, offers, and CTAs for their next campaign.
- A product manager wants to validate a new product idea by mining Reddit and social comments for pain points and exact user language.
- A growth hacker seeks to build a prospect list of influencers in a niche, evaluating their audience fit and engagement.
- A content team wants to repurpose a long webinar into LinkedIn posts, X threads, and newsletter articles using transcript analysis.
How do you install or deploy this agent?
Ensure Node.js is installed on your system. Then run the command: npx skills add ScrapeCreators/social-media-research-skills. This fetches the skills from GitHub and installs them. After installation, set your ScrapeCreators API key as an environment variable: export SCRAPECREATORS_API_KEY=sk_... You can obtain a key from scrapecreators.com.
How do you use this agent?
Once installed and configured, you can invoke the skills through natural language prompts in any supporting AI agent. For example, in Claude Code, you could say: "Find outlier posts for @starterstory on YouTube Shorts." The agent will trigger the outlier-post-finder skill, which uses scrapecreators-api to fetch data and return a table of outliers, patterns, and suggested hooks. Ensure network access is available so the agent can call the ScrapeCreators API.
What are this agent's strengths and limitations?
- Offers complete research workflows rather than simple endpoint routing, producing business-ready outputs.
- All skills are integrated through a common data layer (scrapecreators-api), ensuring consistency and extensibility.
- Emphasizes cited sources and baseline-aware analysis, increasing credibility and practical value.
- Supports multiple major AI coding agents, including Claude Code, Cursor, Codex, and Gemini CLI, making it easy to adopt.
- Tied to ScrapeCreators' proprietary API, requiring a paid subscription or API key, creating vendor lock-in.
- Limited to public data only; cannot access private or logged-in social media data, which may restrict some research scenarios.
- Installation and execution require Node.js and network access to ScrapeCreators services, which may not be available in all environments.
- The repository lacks a clear license, which may raise legal concerns for commercial use.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Social Media Research Skills Pack This agent | 27 · Major gaps | Agent plugin / skillFree + model costs | ★ 4.1k | 1mo ago | Python | ChatGPT · Codex · Claude Code |
| Jev Social | 71 · Some gaps | CLIFree + model costs | ★ 173 | 7d ago | JavaScript | — |
| socai | 65 · Some gaps | — | ★ 243 | today | Rust | Codex · Claude Code |
| Principia | 66 · Some gaps | CLIFree + model costs | ★ 1.1k | 17d ago | Rich Text Format | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: The repository contains only a README, no code or configuration, so permissions, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, and source attribution cannot be assessed. All criteria deducted due to lack of evidence.
Evidence: README describes multiple skills and their collaboration, but no implementation details to verify self-consistency. Dependencies only mention ScrapeCreators API, but no availability guarantee. No failure messages or error handling described.
Evidence: README specifies target audience (AI agents) and multiple use cases (e.g., finding outlier posts, analyzing transcripts), but does not detail capability boundaries or trigger conditions. Environment fit only mentions support for multiple agents, but no specific configuration.
Evidence: README provides installation instructions and skill list, but no license, version history, or changelog. Naming stability and maintenance responsibility not clear. Known limitations only mention public data, but not detailed.
Evidence: README describes outputs for each skill (e.g., tables, reports), but no actual examples or user feedback. Marginal value may be high, but cost-benefit not quantified.
Evidence: README claims skills are effective, but no verifiable tests or independent sources. Facts and inferences not clearly separated.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- Repository contains only README, no actual code or configuration, functionality and security cannot be verified.
- No license information provided, usage risk unknown.
- Relies on external API, but no availability guarantee or error handling provided.