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 onfire7777/universal-ai-skills-library --skill acquisition-channel-advisorgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/acquisition-channel-advisor)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/acquisition-channel-advisor"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/acquisition-channel-advisor/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/onfire7777/universal-ai-skills-library/acquisition-channel-advisor"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/acquisition-channel-advisor.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 | $0.00034 | $0.05402 |
| Opus 5 | $0.00017 | $0.02701 |
| Sonnet 5 | $0.00007 | $0.01080 |
| Haiku 4.5 | $0.00003 | $0.00540 |
Grade A, and why
acquisition-channel-advisor 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.
This is a copy
100% identical to acquisition-channel-advisor — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 643 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Guide product managers through evaluating whether to scale, test, or kill an acquisition channel based on unit economics (CAC, LTV, payback), customer quality (retention, NRR), and scalability (magic number, volume potential). Use this to make data-driven go-to-market decisions and optimize channel mix for sustainable growth.
This is not a channel strategy framework—it's a financial lens for channel evaluation that helps you avoid scaling unprofitable channels or killing channels with fixable problems. Use when deciding how to allocate marketing budget across channels.
Key Concepts
The Channel Evaluation Framework
A systematic approach to evaluate acquisition channels:
-
Unit Economics — What does it cost to acquire, and what's the return?
- CAC (Customer Acquisition Cost)
- LTV (Lifetime Value)
- LTV:CAC ratio
- Payback period
-
Customer Quality — Do customers from this channel stick around and expand?
- Cohort retention rate (by channel)
- Churn rate (by channel)
- NRR (Net Revenue Retention by channel)
- Expansion rate
-
Scalability — Can this channel sustain growth at the volume you need?
- Magic Number (S&M efficiency)
- Addressable volume (TAM of channel)
- Saturation risk (diminishing returns)
- CAC trend (increasing, stable, decreasing)
-
Strategic Fit — Does this channel align with your go-to-market strategy?
- Customer segment match (SMB vs. enterprise)
- Sales motion compatibility (PLG vs. sales-led)
- Brand positioning alignment
Decision Matrix
| LTV:CAC | Payback | Customer Quality | Scalability | Decision |
|---|---|---|---|---|
| >3:1 | <12mo | Good retention | High volume | Scale aggressively |
| 2-3:1 | 12-18mo | Average retention | Medium volume | Test & optimize |
| <2:1 | >18mo | Poor retention | Low volume | Kill or fix |
Anti-Patterns (What This Is NOT)
- Not vanity metrics: "We got 10,000 signups!" means nothing if they churn in 30 days
- Not CAC-only thinking: Low CAC with terrible retention is worse than high CAC with great retention
- Not ignoring payback: 5:1 LTV:CAC with 36-month payback is a cash trap
- Not scaling broken channels: Pouring money into inefficient channels accelerates failure
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 · 643 lines · 34 tokens per session scan A 6f4c7fc96584
acquisition-channel-advisor is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 5,402 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to acquisition-channel-advisor, differing in 25 lines, and is treated as a copy.
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