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 product-demand-researchgit 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/product-demand-research)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/product-demand-research"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/product-demand-research/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/product-demand-research"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/product-demand-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00053 | $0.00578 |
| Opus 5 | $0.00026 | $0.00289 |
| Sonnet 5 | $0.00011 | $0.00116 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
product-demand-research 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 13d 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
Product Demand Research
Overview
Use public social data to understand whether people actually complain about, ask for, buy, hack around, or recommend solutions in a product category. The output should help founders and marketers decide what to build, position, or test.
When to Use
Use this skill when the user asks to:
- validate a startup or product idea
- find pain points in a niche
- mine Reddit/TikTok/YouTube comments for product ideas
- find buyer language and objections
- understand alternatives people use today
- create messaging from demand evidence
Signals to Extract
- repeated complaints
- "I wish" / "is there a tool" / "how do I" phrases
- workarounds and spreadsheets/manual processes
- comparison and alternative mentions
- buying intent
- objections to existing solutions
- exact words people use for the problem
Workflow
- Turn the idea into search queries and synonyms.
- Search Reddit and relevant social platforms.
- Pull comments/transcripts for promising posts/videos.
- Cluster pains, triggers, alternatives, and desired outcomes.
- Score demand by frequency, intensity, recency, and willingness-to-pay hints.
- Produce messaging and product implications.
Output Format
# Product Demand Research: {idea/category}
## Verdict
- Demand signal: Strong/Medium/Weak
- Confidence: High/Medium/Low
- Why:
## Pain Points
| Pain | Evidence | Exact language | Source |
|---|---|---|---|
## Existing Alternatives / Workarounds
- ...
## Objections and Barriers
- ...
## Messaging Angles
- "..."
## Product Ideas / Tests
1. ...
2. ...
Common Pitfalls
- Do not claim market validation from a handful of comments.
- Do not ignore negative evidence or existing alternatives.
- Do not paraphrase away the best customer language.
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.
- 13d ago First seen · 93 lines · 53 tokens per session scan A 88d57b03fa52
product-demand-research is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (2,260 stars, last pushed 17d ago), licensed MIT. It adds 53 tokens to every session and 578 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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