AIAny
AI Agent2026
Icon for item

Social Media Research Skills for AI Agents

Provides reusable AI agent skills that turn public social posts, transcripts, comments and ad libraries into actionable research artifacts. Workflow-first design covers outlier-post detection, comment mining, transcript intelligence, competitor teardowns and content repurposing for agent-driven automation.

Introduction

Social media research is noisy and multi-platform: useful signals live across short videos, comments, ad libraries and community threads. This repository reframes that problem by shipping ready-made "skills" an AI agent can call to gather, normalize and transform public social data into business-ready artifacts — not just raw scrapes.

What Sets It Apart
  • Workflow-first, not endpoint-first — the repo provides end-to-end research workflows (outlier-post-finder, comment-mining, transcript-intelligence, ad-library-teardown, trend-discovery, content-repurposing) so agents deliver practical outputs (tables, briefs, swipe files) instead of raw JSON. So what: saves prompt engineering and glue-code work when automating social research tasks.
  • Agent-native integrations — designed to run with agent runtimes like Claude Code, Cursor, OpenAI Codex, Gemini CLI and others. So what: you can plug these skills into different agent environments without redesigning the research logic.
  • Public-data and repeatability focus — emphasizes baseline-aware analysis, exact language preservation, and citation of source URLs. So what: reports are reproducible, auditable, and tuned to a creator’s own performance rather than vanity metrics.
  • Practical output templates — each skill produces artifacts (VOC reports, outlier tables, competitor briefs, content ideas) intended to be immediately actionable for content teams, growth, product demand validation, or influencer outreach.
Who it's for + Tradeoffs

Great fit if you need automated, repeatable social research pipelines that agents can run at scale—content strategists, growth teams, product researchers, or engineers building agent-driven workflows. It’s especially useful when you want cited examples, exact audience language, and production-ready artifacts rather than raw data. Look elsewhere if you need private/authenticated data (this repo and its data layer work only with public social data), deep platform-native analytics behind logins, or a turnkey SaaS dashboard — this is a toolkit for agents and engineers, not a hosted analytics product.

More Items

GitHub
AI Agent2025

Provides a Python and TypeScript SDK to build and run production AI agent harnesses — the agent loop plus tools, memory, sessions, context management and lifecycle controls — with model portability and built-in observability.

GitHub
AI Agent2024

Autonomously performs market analysis, risk sizing, and trade execution for crypto venues. Built as a modular multi-agent pipeline (Director, Quant, Risk, Execution) with structured JSON outputs and enterprise logging; currently supports full autonomous trading on Solana.

GitHub
AI Agent2025

Orchestrates code-running agents to hunt verifiable open problems: schedules runs, applies null tests and skeptic re-checks, and packages found objects with short checkers so anyone can reproduce or validate a claim.