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 comment-mininggit 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/comment-mining)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/comment-mining"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/comment-mining/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/comment-mining"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/comment-mining.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00062 | $0.00487 |
| Opus 5.5 | $0.00025 | $0.00195 |
| Sonnet 5.5 | $0.00012 | $0.00097 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
comment-mining 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 1mo 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
Comment Mining — your audience already told you what to make
Reads your comments and DMs to surface the questions, objections and exact phrases your audience uses — and turns them into content ideas.
When to use
You're out of ideas, but your audience has been telling you what they want.
What you'll need
Your last 30-100 comments or DMs. The Ootto MCP can read them off your posts directly.
Instructions
Give Claude the input and run this.
You are my audience researcher. Here are my comments/DMs: [paste].
1. RECURRING QUESTIONS: group them — give me the 5-8 things people actually keep asking, with counts.
2. OBJECTIONS: what stops them buying, trying, or believing me.
3. THEIR WORDS: the exact phrases they use. Quote them verbatim — I want these in my hooks and captions.
4. MISREADS: where people misunderstood something I said (each one is a reel).
5. IDEAS: turn the top questions into reel ideas, ranked by how often they came up, each with a hook line.
Use their vocabulary, not mine.
Honesty: Every idea must trace to a real comment. Don't invent audience demand — the whole point is that this is measured, not guessed.
Next: viral-hook-writer → reel-scripter
Built by Ootto — the AI autopilot that connects your tools once and runs the busywork for you, automatically. Book a demo →
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.
- 1mo ago First seen · 51 lines · 62 tokens per session scan A c20be227fbce
comment-mining is a skill published in the GitHub repository Ootto-AI/claude-content-skills (301 stars, last pushed 10d ago), licensed MIT. It adds 62 tokens to every session and 487 once invoked, about $0.0002 per session on Opus 5.5. 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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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.
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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.