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 sandbaseai/sandbase-skills --skill brand-monitoringgit clone --depth 1 https://github.com/sandbaseai/sandbase-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/sandbaseai/sandbase-skills/brand-monitoring)<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/brand-monitoring"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/brand-monitoring/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/sandbaseai/sandbase-skills/brand-monitoring"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/brand-monitoring.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 | $0.00048 | $0.00502 |
| Opus 5 | $0.00024 | $0.00251 |
| Sonnet 5 | $0.00010 | $0.00100 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
brand-monitoring 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Monitoring
Cross-platform brand reputation and mention monitoring through SandBase. Track what's being said about a brand across Twitter, Reddit, news sources, and Chinese platforms. Read the API map before selecting a capability.
Call SandBase capabilities
For every selected tool, call sandbase_describe_tool first and use only arguments in its current input schema. Then call sandbase_call_tool with the exact tool_name.
Operating principles
- Monitor across multiple platforms for complete coverage.
- Separate factual mentions from sentiment/opinion.
- Track both brand name and common misspellings/abbreviations.
- Note platform-specific context (Twitter = real-time, Reddit = community depth, News = authority).
- Keep client brand strategy and competitor lists confidential.
Workflow
1. Twitter monitoring
Use twitter_web_search_timeline to search brand mentions and hashtags.
Use twitter_web_trending to check if brand is trending.
2. Reddit monitoring
Use reddit_app_dynamic_search to find brand discussions across subreddits.
3. News monitoring
Use tavily_search with topic "news" and brand name.
Use google_news_bulk_articles for comprehensive news coverage.
4. Chinese platform monitoring (if applicable)
Use xiaohongshu_app_v2_search_notes for brand mentions on Xiaohongshu.
Use weibo_web_search for Weibo brand mentions.
5. Synthesize
Aggregate mentions by platform, sentiment, volume, and trends.
Output
Return: mention volume by platform, sentiment breakdown (positive/negative/neutral), key influencer mentions, emerging issues, competitive comparison, and trend over time.
Example tasks
- "Monitor what's being said about [brand] across Twitter, Reddit, and news this week."
- "What's the sentiment toward [brand] on social media right now?"
- "Has [brand] been mentioned in any negative news recently?"
- "Compare social mentions between [our brand] and [competitor brand]."
- "Track [product launch] reception across Twitter, Reddit, and 小红书."
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 · 58 lines · 48 tokens per session scan A e21b19902407
brand-monitoring is a skill published in the GitHub repository sandbaseai/sandbase-skills (159 stars, last pushed 4d ago), licensed Apache-2.0. It adds 48 tokens to every session and 502 once invoked, about $0.0002 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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