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 superamped/ai-marketing-skills --skill channel-discoverygit clone --depth 1 https://github.com/superamped/ai-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/superamped/ai-marketing-skills/channel-discovery)<a href="https://agentmods.dev/skills/superamped/ai-marketing-skills/channel-discovery"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/channel-discovery/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/superamped/ai-marketing-skills/channel-discovery"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/channel-discovery.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 | $0.00057 | $0.03345 |
| Opus 5 | $0.00028 | $0.01673 |
| Sonnet 5 | $0.00011 | $0.00669 |
| Haiku 4.5 | $0.00006 | $0.00334 |
Grade A, and why
channel-discovery 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 13d 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Channel Discovery
Usage
Use when deciding where to spend marketing dollars and time. Ideal after defining your target market but before committing to specific tactics. Also useful when a current channel is underperforming and you need alternatives.
Process
Step 1: Gather Inputs
Ask the user for:
- Target market definition — who they're targeting (
{Identity} + {Industry} + {Business type & stage}). Example: "B2B SaaS CTOs at seed-stage startups" - Product context — what they sell, pricing, current stage
- Constraints — budget (monthly), team size, timeline, skills available
- Current channels — what they've already tried and results
Step 2: Understand the Target Market
Parse the target market definition. Identify:
- Who they are: Role, seniority, industry
- How they buy: Self-serve vs. sales-led, individual vs. committee, impulse vs. considered
- Deal size signal: Low-touch (< $50/mo) vs. mid-touch ($50-500/mo) vs. high-touch ($500+/mo)
- Digital behaviour: Where do professionals in this role/industry spend time online?
Step 2b: Run the 12 Customer Channel Questions
Before mapping channels, get inside the customer's head. Run through these 12 questions for the target market. Answer them based on research and web searches — not assumptions.
- Search — Do they use search engines? What are they searching for? What keywords or phrases?
- Websites — What websites and blogs do they visit?
- Communities — Which communities and groups do they hang out in (Reddit, Slack, Discord, industry forums)?
- Video — What videos do they watch? Who do they subscribe to on YouTube?
- Social — What social media platforms do they use? Which influencers do they follow?
- Email — What email newsletters do they subscribe to?
- Podcasts — What podcasts do they listen to?
- Professional network — Do they have a work email address? Are they active on LinkedIn?
- Credentials — Do they have specific job qualifications, certifications, or training? Are they part of professional membership bodies or associations?
- Events — Do they attend trade shows, conferences, or industry meetups?
- Publications — Do they read trade publications or industry news sites?
- Books and thought leaders — What books do they read? Who are the thought leaders they follow?
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.
- 13d ago First seen · 265 lines · 57 tokens per session scan A e28f4b670558
channel-discovery is a skill published in the GitHub repository superamped/ai-marketing-skills (67 stars, last pushed 25d ago), licensed MIT. It adds 57 tokens to every session and 3,345 once invoked, about $0.0003 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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