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 anysiteio/agent-skills --skill anysite-brand-reputationgit clone --depth 1 https://github.com/anysiteio/agent-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/anysiteio/agent-skills/anysite-brand-reputation)<a href="https://agentmods.dev/skills/anysiteio/agent-skills/anysite-brand-reputation"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-brand-reputation/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/anysiteio/agent-skills/anysite-brand-reputation"><img src="https://agentmods.dev/badge/skills/anysiteio/agent-skills/anysite-brand-reputation.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 | $0.00099 | $0.03327 |
| Opus 5 | $0.00049 | $0.01664 |
| Sonnet 5 | $0.00020 | $0.00665 |
| Haiku 4.5 | $0.00010 | $0.00333 |
Grade A, and why
anysite-brand-reputation 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 — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.
anysite Brand Reputation Monitoring
Monitor and protect your brand reputation across social media platforms. Track mentions, analyze sentiment, and identify issues before they escalate.
Overview
- Track brand mentions across social platforms
- Analyze sentiment (positive, negative, neutral)
- Monitor conversations about your brand
- Identify reputation risks and crisis signals
- Measure brand health over time
Coverage: 65% - Pivoted from review platforms to social media monitoring; strong for Twitter, Reddit, Instagram, YouTube, LinkedIn
v2 Tool Interface
All data fetching uses the anysite MCP v2 universal meta-tools:
execute(source, category, endpoint, params)— fetch data from any source. Returns first page +cache_key.get_page(cache_key, offset, limit)— paginate through results whennext_offsetis returned.query_cache(cache_key, conditions, sort_by, aggregate, group_by)— filter, sort, or aggregate cached data without new API calls.export_data(cache_key, format)— export full dataset as CSV, JSON, or JSONL for reports.
Always call discover(source, category) first if unsure about endpoint names or params.
Error Handling
v2 responses may include llm_hint fields with guidance on how to fix errors (e.g., wrong URN format, missing params). Always check llm_hint in error responses before retrying.
Supported Platforms
- Twitter/X: Real-time mentions, sentiment, viral content
- Reddit: Community discussions, detailed feedback, sentiment
- Instagram: Visual brand mentions, hashtag tracking, influencer posts
- YouTube: Video mentions, comment sentiment, brand coverage
- LinkedIn: Professional mentions, company updates, B2B sentiment
Quick Start
Step 1: Set Up Monitoring
Define:
- Brand keywords (company name, product names, misspellings)
- Platforms to monitor (Twitter, Reddit, Instagram, etc.)
- Monitoring frequency (real-time, daily, weekly)
- Alert thresholds (negative sentiment, volume spikes)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 436 lines · 99 tokens per session scan A c8acf9775f62
anysite-brand-reputation is a skill published in the GitHub repository anysiteio/agent-skills (19 stars, last pushed 28d ago), licensed MIT. It adds 99 tokens to every session and 3,327 once invoked, about $0.0005 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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