Social Media Research Skills is a collection of workflows that let AI coding agents research public social-media data across platforms such as TikTok, Instagram, YouTube, Reddit, and LinkedIn. Marketers and researchers use it to find unusually successful posts, mine comments, study competitors, analyze ads, and extract trends into business outputs. The catalogue skills and plugin package these workflows for supported AI agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ScrapeCreators/social-media-research-skills --skill social-listening-briefgit clone --depth 1 https://github.com/ScrapeCreators/social-media-research-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/scrapecreators/social-media-research-skills/social-listening-brief)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/social-listening-brief"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/social-listening-brief/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/social-listening-brief"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/social-listening-brief.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 87 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.00580 |
| Opus 5 | $0.00028 | $0.00290 |
| Sonnet 5 | $0.00011 | $0.00116 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
social-listening-brief scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Social Listening Brief
Overview
Research what people are saying publicly across social platforms. This is useful for brand monitoring, category research, product feedback, reputation checks, and understanding recent conversations.
When to Use
Use this skill when the user asks:
- what are people saying about X?
- monitor this brand/topic/category
- find complaints or praise about a product
- summarize recent social discussion
- compare sentiment across Reddit, TikTok, YouTube, LinkedIn, Instagram, or Threads
Useful Sources
- Reddit search, subreddit search, posts, comments
- TikTok search top/keyword/hashtag and comments
- YouTube search, transcripts, comments
- Instagram reels search and hashtag search
- LinkedIn post search
- Threads search
- Google search when platform search is not enough
Workflow
- Define topic, aliases, competitor names, and date window.
- Search multiple relevant sources, not every source blindly.
- Keep URLs, dates, platform, engagement metrics, and exact quotes.
- Cluster conversations into themes.
- Separate positive, negative, neutral, and mixed signals.
- Highlight representative examples and action items.
Output Format
# Social Listening Brief: {topic}
## Executive Summary
- Main takeaway:
- Conversation volume: Low/Medium/High
- Sentiment: Positive/Neutral/Negative/Mixed
- Confidence: High/Medium/Low
## Top Themes
| Theme | Sentiment | Evidence | Representative quote/source |
|---|---|---|---|
## Notable Posts
- [source](url) — why it matters
## Risks / Opportunities
- ...
## Recommended Actions
1. ...
Common Pitfalls
- Do not pretend this is exhaustive social monitoring. It is public-data research.
- Do not average sentiment across very different communities without caveats.
- Do not use engagement as a proxy for truth.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 89 lines · 55 tokens per session scan A c41b897c1b5b
social-listening-brief is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (2,260 stars, last pushed 16d ago), licensed MIT. It adds 55 tokens to every session and 580 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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