Agent skill · Testing & QA

acquisition-channel-advisor

Evaluate acquisition channels using unit economics, customer quality, and scalability. Use when deciding whether to scale, test, or kill a growth channel.

deanpetersgithub.com/deanpetersGitHub ↗
claude-codeNOASSERTION
Install
npx skills add deanpeters/Product-Manager-Skills --skill acquisition-channel-advisor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 22 KB
Bundled scripts: none
Path: skills/acquisition-channel-advisor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 6,255
Language: Shell
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

Evaluates an acquisition channel by examining unit economics (CAC, LTV, payback), customer quality (retention, NRR), and scalability (magic number, volume, CAC trend). It frames go-to-market decisions as go/no-go on scaling, testing, or killing a channel, and provides actionable recommendations across multiple patterns (scale aggressively, test & optimize, kill or pause, invest to learn).

How it works

The skill guides the agent through up to 4 adaptive questions, then synthesizes findings into structured sections:

  • Step 0: Gather Context (channel details, acquisition metrics, business context)
  • Step 1: Evaluate Unit Economics (calculate CAC if needed; compute LTV:CAC, payback; compare to blended metrics; flag economics as strong, marginal, or poor)
  • Step 2: Assess Customer Quality (retention, NRR, ICP fit; categorize as high/medium/low quality; provide qualitative flags)
  • Step 3: Evaluate Scalability (Magic Number, addressable volume, CAC trend; categorize scalability as highly, moderately, or not scalable)
  • Step 4: Deliver Recommendations (synthesize into 3-4 recommendations using predefined patterns with concrete numeric placeholders and steps to scale, test, kill, or invest strategically)

The skill uses predefinedRecommendation Patterns to produce concrete action plans:

  • Pattern 1: Scale Aggressively
  • Pattern 2: Test & Optimize
  • Pattern 3: Kill or Pause
  • Pattern 4: Invest to Learn (Strategic Channel)

These patterns specify the exact structure for unit economics, customer quality, scalability, and implementation steps (budgets, monitoring cadence, and exit criteria).

When to use it

Use when deciding whether to scale, test, or kill an acquisition channel and when comparing multiple channels to optimize ROI. Triggered by a request to evaluate a specific channel and the availability of metrics (CAC, LTV, payback, retention, NRR, growth targets) or a need to assess strategic fit for longer horizons.

What it can touch

The skill references calculations and metrics such as CAC, LTV, payback period, retention, NRR, Magic Number, addressable volume, and CAC trend. It requires channel-level data and blended benchmarks to perform comparisons and produce recommendations.

Caveats

The review is limited to what the skill explicitly states: it does not infer outcomes beyond the provided framework. It uses explicit thresholds and patterns (e.g., LTV:CAC >3:1, payback <12 months) and refrains from making claims outside those criteria. No promises are made about achieving the suggested targets; outcomes depend on actual data and context.

From the SKILL.md

## 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. ## Input **Works best with:** The acquisition channel you're evaluating (e.g., paid search, outbound SDR, partner referrals). **Also useful:** Any metrics you already have — CAC, LTV, payback period, retention/NRR by channel — plus company stage and the decision on the table (scale, test, or kill). Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended `ARGUMENTS:` line — counts as answers already given. Use it and skip whatever it covers; don't re-ask. **Arriving empty-handed? That works too.** The advisor opens by asking which channel you're evalua

What's inside
Steps it walks through
  1. Purpose
  2. Input
  3. Key Concepts
  4. The Channel Evaluation Framework
  5. Decision Matrix
  6. Anti-Patterns (What This Is NOT)
  7. When to Use This Framework
  8. Facilitation Source of Truth
  9. Application
  10. Step 0: Gather Context
  11. Step 1: Evaluate Unit Economics
  12. Step 2: Assess Customer Quality
  13. Step 3: Evaluate Scalability
  14. Step 4: Deliver Recommendations
Ships with 1 file
  • examples/conversation-flow.md
More from Product-Manager-Skills
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About this skill
What does the acquisition-channel-advisor skill do?

Evaluate acquisition channels using unit economics, customer quality, and scalability. Use when deciding whether to scale, test, or kill a growth channel.

How do I install it?

Run `npx skills add deanpeters/Product-Manager-Skills --skill acquisition-channel-advisor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From deanpeters/Product-Manager-Skills, a repository with 6,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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