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 social-listeninggit 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/social-listening)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/social-listening"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/social-listening/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/social-listening"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/social-listening.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 | $0.00125 | $0.00703 |
| Opus 5 | $0.00063 | $0.00351 |
| Sonnet 5 | $0.00025 | $0.00141 |
| Haiku 4.5 | $0.00013 | $0.00070 |
Grade A, and why
social-listening 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Listening
Turn evidence the operator already has into a usable demand map without pretending that a small sample represents the whole market.
1. Set the evidence boundary
Ask for the source material, where it came from, its date range, and the decision it should inform. Keep comments, DMs, reviews, support tickets, and interview notes labelled by source. If the user has only a claim about what people say, ask for the underlying text before analysing it.
2. Extract the signal without flattening it
For each item, capture the speaker's own wording, the situation they describe, the job they are trying to do, the obstacle, and any stated outcome. Keep a short supporting excerpt beside every finding so a reader can check it. Separate direct customer language from the operator's interpretation.
3. Cluster by decision-relevant theme
Group repeated evidence into questions, desired outcomes, objections, alternatives, proof requests, and vocabulary. Count only the supplied evidence. Mark a theme as isolated when it appears once, recurring when it appears across independent items, and unresolved when the context is too thin to tell.
4. Produce a demand map
Return a compact table with: theme, evidence count, representative wording, likely stage of the journey, confidence, and the next action. Next actions can be a research question, an FAQ update, an input for audience-personas, or a test for positioning-audit. Call out contradictions instead of averaging them away.
Hard rules
- Do not infer age, location, income, intent, or sentiment beyond what the evidence says.
- Never present supplied comments as a representative market sample without a sampling basis.
- Preserve anonymisation: do not expose names, handles, private messages, or customer details unnecessarily.
- Do not manufacture quotes or improve a speaker's wording inside quotation marks.
- Treat volume as a clue, not proof of importance; a repeated complaint from one thread is not independent corroboration.
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 · 46 lines · 125 tokens per session scan A 3ed728abda25
social-listening is a skill published in the GitHub repository Ootto-AI/claude-content-skills (30 stars, last pushed 20d ago), licensed MIT. It adds 125 tokens to every session and 703 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.
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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.