social-media-research-skills: AI agent skills for social media research

by ScrapeCreators · Claude Skill · ★ 4.9k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install social-media-research-skills

npx skills add ScrapeCreators/social-media-research-skills

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-skillsai-agentsclaude-codeclaude-skillscompetitor-analysiscontent-marketingcursorinfluencer-marketingmarket-researchsocial-media

Quick Facts

Stars4,858
Forks95
LanguagePython
CategoryClaude Skill
LicenseMIT
Quality Score70/100
Open Issues8
Last Updated2026-08-26
Created2026-06-06
Platformsclaude-code, cli, codex, gemini, python
Est. Tokens~4k

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.

Compatible Skills

These tools work well together with social-media-research-skills for enhanced workflows:

  • Agentfy — semantic(0.50)+complementary+same_lang+similar_pop+shared_platform (68%)
  • Supervisor-Skills — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)
  • web-asset-generator — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)
  • paper-craft-skills — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Claude Skill tools