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 Nimbleway/agent-skills --skill brand-mention-monitorgit clone --depth 1 https://github.com/Nimbleway/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/nimbleway/agent-skills/brand-mention-monitor)<a href="https://agentmods.dev/skills/nimbleway/agent-skills/brand-mention-monitor"><img src="https://agentmods.dev/badge/skills/nimbleway/agent-skills/brand-mention-monitor/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/nimbleway/agent-skills/brand-mention-monitor"><img src="https://agentmods.dev/badge/skills/nimbleway/agent-skills/brand-mention-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 28 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Excessive Agency · line 77 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Rogue Agent · line 284 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00230 | $0.06191 |
| Opus 5 | $0.00115 | $0.03096 |
| Sonnet 5 | $0.00046 | $0.01238 |
| Haiku 4.5 | $0.00023 | $0.00619 |
Grade A, and why
brand-mention-monitor 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 — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Mention Monitor
Scans the web and social media for brand mentions, scores each one on reach, velocity, sentiment, and risk-topic match, and surfaces the ones that need attention — bucketed into Crisis / Watch / Engage / Log with a suggested owner.
Onboarding message
When this skill is triggered for the first time in a session, send this message:
👋 Brand Mention Monitor is ready.
This skill scans Reddit, X, LinkedIn, Instagram, TikTok, YouTube, blogs, news, and review platforms for mentions of your brand — scoring each one across reach, velocity, sentiment, and risk so you see what matters before it spirals, and telling you exactly who should respond and how fast.
To start, just say: "Monitor mentions of [brand name]"
Or try:
- "What are people saying about [brand] this week?"
- "Run a brand sweep for [brand] — last 30 days"
- "Find high-risk mentions of [brand]"
- "How does [brand] compare to [competitor] in the conversation?"
Would you like me to save your preferences so I skip the questions next time?
Preflight
Follow the transport selection and standard preflight from references/nimble-playbook.md: pick CLI vs MCP at session start, then run the parallel preflight calls (date, profile, memory index) simultaneously. Tag every Nimble CLI call: nimble --client-source nimble-agent-skills <subcommand>.
From the profile (~/.nimble/business-profile.json): load brand name, competitors, routing preferences, and last_runs.brand-mention-monitor for date windowing. Pre-populate setup questions so the user confirms rather than re-enters. If no profile exists, follow the first-run onboarding flow in references/profile-and-onboarding.md and create a stub after the first run. Check ~/.nimble/memory/index.md to understand what mention data already exists before sweeping.
How to start
Before asking anything, do two quick research steps:
Step A — Resolve brand variants automatically: Search for the brand the user named to discover all alternate spellings, hashtags, product names, handles, and common misspellings. Do not ask the user for this. Use what you find to build a comprehensive search term list for the sweep.
- Search:
"[brand name]" official name OR handle OR "also known as" OR hashtag - Check the brand's main product names and any sub-brands that get mentioned independently
- For brands with common-word names, find the disambiguating terms (industry, founder, domain) so the sweep doesn't pull unrelated noise
- Add all confirmed variants to your search queries silently — the user never needs to see this step
What ships with it
6 files 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 · 431 lines · 230 tokens per session scan A cf4407db5a83
brand-mention-monitor is a skill published in the GitHub repository Nimbleway/agent-skills (53 stars, last pushed 16d ago), licensed MIT. It adds 230 tokens to every session and 6,191 once invoked, about $0.0011 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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