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 ekinciio/saas-growth-marketing-skills --skill brand-mention-scannergit clone --depth 1 https://github.com/ekinciio/saas-growth-marketing-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/ekinciio/saas-growth-marketing-skills/brand-mention-scanner)<a href="https://agentmods.dev/skills/ekinciio/saas-growth-marketing-skills/brand-mention-scanner"><img src="https://agentmods.dev/badge/skills/ekinciio/saas-growth-marketing-skills/brand-mention-scanner/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/ekinciio/saas-growth-marketing-skills/brand-mention-scanner"><img src="https://agentmods.dev/badge/skills/ekinciio/saas-growth-marketing-skills/brand-mention-scanner.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 | $0.00071 | $0.02495 |
| Opus 5 | $0.00036 | $0.01247 |
| Sonnet 5 | $0.00014 | $0.00499 |
| Haiku 4.5 | $0.00007 | $0.00249 |
Grade A, and why
brand-mention-scanner 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Mention Scanner
A multi-platform brand monitoring skill that scans Reddit, Hacker News, and GitHub for mentions of your brand or product. Identifies where you are being discussed, analyzes sentiment context, and surfaces unresponded opportunities.
Intro Banner
When starting a scan, display this intro before fetching:
""" 🔍 Brand Mention Scanner
What I'll do: Search Reddit, Hacker News, and GitHub for mentions of "[brand]".
What you'll get: → Total mention count across all 3 platforms → Sentiment breakdown (positive/negative/neutral/question/comparison) → Top mentions sorted by engagement → Unresponded opportunities
Note: 3 platforms scanned sequentially. Takes ~15-30 seconds total. Rate limits apply (see SKILL.md for optional API keys).
Output: Saved to BRAND-MENTIONS-REPORT.md
Scanning... """
Then proceed immediately.
Commands
/brand-mention-scanner scan <brand-name>
Performs a full mention scan across all three platforms (Reddit, Hacker News, GitHub).
Usage:
/brand-mention-scanner scan "vercel"
/brand-mention-scanner scan "your-product-name"
Output includes:
- Total mention count across all platforms
- Platform breakdown (mentions per platform)
- Sentiment summary (positive, negative, neutral, question, comparison)
- Top mentions sorted by engagement
- Unresponded opportunities
- Trending analysis
Report: Save output to BRAND-MENTIONS-REPORT.md
/brand-mention-scanner reddit <brand-name>
Scans Reddit only for brand mentions.
Usage:
/brand-mention-scanner reddit "linear"
Output includes:
- Reddit threads mentioning the brand
- Subreddit distribution
- Sentiment classification per mention
- Engagement metrics (upvotes, comments)
- Thread age and recency
Report: Save output to BRAND-MENTIONS-REDDIT-REPORT.md
/brand-mention-scanner hn <brand-name>
Scans Hacker News only for brand mentions.
Usage:
/brand-mention-scanner hn "supabase"
Output includes:
- HN stories and comments mentioning the brand
- Points and comment counts
- Author information
- Discussion context and sentiment
What ships with it
2 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 · 282 lines · 71 tokens per session scan A cea161f04abb
brand-mention-scanner is a skill published in the GitHub repository ekinciio/saas-growth-marketing-skills (12 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 2,495 once invoked, about $0.0004 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.
Other skills, from other repositories
agent-reach
An internet-search and platform-access router for finding information across websites and services such as Reddit, X, GitHub, YouTube, and job sites.
xerj-code
Reference-coding with XERJ. Clone the libraries that already solved your problem, index them locally, and retrieve the exact implementation before writing code — so the agent reads passages instead of re-deriving algorithms across retry loops. Use when starting a task in an unfamiliar API, porting an algorithm, or…
vs-product-qa
Answer Viking AI Search product questions, CLI usage questions, API/auth questions, configuration questions, and troubleshooting questions by grounding every claim in either the installed vs CLI's own output or official Volcengine documentation. Never fabricate.
app-analytics
When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For…
creator-ugc-marketing
When the user wants to plan, brief, source, or measure organic creator / influencer / UGC marketing for their app — including TikTok creators, Instagram Reels, YouTube Shorts, micro-influencers, paid creator briefs, UGC ad creative for Meta/TikTok, affiliate programs, and seeding strategy. Use when the user mentions…
custom-product-pages
When the user wants to design, deploy, or measure Apple Custom Product Pages (CPP) — the alternate App Store product pages with different screenshots, preview videos, and promo text shown to users coming from specific URLs (typically ad campaigns or social posts). Use when the user mentions "Custom Product Page"…