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 ad-library-teardowngit 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/ad-library-teardown)<a href="https://agentmods.dev/skills/scrapecreators/social-media-research-skills/ad-library-teardown"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/ad-library-teardown/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/ad-library-teardown"><img src="https://agentmods.dev/badge/skills/scrapecreators/social-media-research-skills/ad-library-teardown.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 | $0.00056 | $0.00935 |
| Opus 5 | $0.00028 | $0.00467 |
| Sonnet 5 | $0.00011 | $0.00187 |
| Haiku 4.5 | $0.00006 | $0.00093 |
Grade A, and why
ad-library-teardown 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Library Teardown
Overview
Analyze public ads to understand a competitor's messaging, offers, creative strategy, and testing angles. The output should be a practical teardown marketers can use to write better ads or decide what to test.
When to Use
Use this skill when the user asks to:
- analyze a competitor's active ads
- search Meta/Facebook, Google, or LinkedIn ad libraries
- extract ad hooks, CTAs, claims, offers, and landing page angles
- compare ad messaging across competitors
- summarize video ad transcripts
- generate ad test ideas from competitor ads
Data Sources
| Ad library | Search/list endpoint | Detail endpoint | Transcript endpoint |
|---|---|---|---|
| Meta/Facebook | /v1/facebook/adLibrary/search/ads, /v1/facebook/adLibrary/company/ads, /v1/facebook/adLibrary/search/companies |
/v1/facebook/adLibrary/ad |
/v1/facebook/adLibrary/ad/transcript |
/v1/google/adLibrary/advertisers/search, /v1/google/company/ads |
/v1/google/ad |
n/a | |
/v1/linkedin/ads/search |
/v1/linkedin/ad |
n/a |
Workflow
-
Find the advertiser
- Use company search endpoints when the user provides only a brand name.
- Use domain/advertiser/page IDs when available.
-
Fetch active ads
- Prefer active ads unless the user asks for historical analysis.
- Capture platform, advertiser/page, ad ID, start date, creative type, text, headline, CTA, destination URL, and source URL.
-
Fetch details for representative ads
- Enrich the ads with detail endpoints.
- For video Meta ads, fetch transcripts when available.
-
Cluster messaging Group ads by:
- pain point
- persona
- offer
- proof/social proof
- feature/benefit
- objection handled
- comparison/alternative angle
- urgency/discount
-
Extract swipeable elements
- hooks
- headlines
- primary text patterns
- CTAs
- claims
- offers
- visual/creative concepts
-
Recommend tests Suggest tests based on repeated patterns and gaps, not random ideas.
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 · 131 lines · 56 tokens per session scan A 93b67284b21a
ad-library-teardown is a skill published in the GitHub repository ScrapeCreators/social-media-research-skills (2,234 stars, last pushed 16d ago), licensed MIT. It adds 56 tokens to every session and 935 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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