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 Ootto-AI/claude-content-skills --skill content-researchgit clone --depth 1 https://github.com/Ootto-AI/claude-content-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/ootto-ai/claude-content-skills/content-research)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/content-research"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/content-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/ootto-ai/claude-content-skills/content-research"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/content-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00120 | $0.00854 |
| Opus 5 | $0.00060 | $0.00427 |
| Sonnet 5 | $0.00024 | $0.00171 |
| Haiku 4.5 | $0.00012 | $0.00085 |
Grade A, and why
content-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 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Research — stop guessing what to post
Most people spend hours scrolling to figure out what to post. This skill turns Claude into a research machine: point it at any account or niche and it finds the exact moments that went viral and the patterns behind them — so you copy what works instead of guessing.
What it does
- Pull the posts. For a creator/account, gather their recent reels + view counts (Instagram Graph API via Composio, or any list of reel URLs the user provides).
- Find the spikes. Rank posts by views and flag the outliers — the exact posts where the account blew up vs their baseline. Those are the moments worth studying.
- Break down WHY. For each outlier, study the reel frame-by-frame + transcript (hand off to the
reel-analyzerskill / ootto-watch) and extract the hook (first 2s), the format/structure, the pacing, and the retention pattern. - Surface the playbook. Across the outliers, surface the repeatable patterns — the hook types, formats, and topics that consistently earn saves/shares — and turn them into a short, copyable plan for the user's own niche.
How to run it
- Ask for a creator @handle, an account URL, or a niche (+ a few example accounts).
- Pull their reels + view counts. Options:
- sandcastles.ai — a research engine that pulls top channels and auto-surfaces the viral outliers + the frameworks behind them (fastest path; connect it to Claude and let it do the heavy lifting).
- Composio Instagram tools — free/DIY: list a creator's media + insights from the Graph API.
- Or a plain list of reel URLs the user pastes.
- Rank by views, compute each post's ratio vs the account median, and mark anything ~2-3x median as an outlier ("blew up here").
- For the top 3-5 outliers, run
reel-analyzer(or ootto-watch — github.com/Ootto-AI/ootto-watch) to break down hook / format / retention. - Synthesize: list the winning hook patterns, formats, and topics, then write 3 ready-to-shoot ideas for the user's own account modeled on what actually worked.
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 · 66 lines · 120 tokens per session scan A 7ea145b56cc3
content-research is a skill published in the GitHub repository Ootto-AI/claude-content-skills (30 stars, last pushed 20d ago), licensed MIT. It adds 120 tokens to every session and 854 once invoked, about $0.0006 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.
Other skills, from other repositories
scrapecreators-api
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio…
outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.