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 san-npm/skills-ws --skill customer-acquisitiongit clone --depth 1 https://github.com/san-npm/skills-wsWrote 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/san-npm/skills-ws/customer-acquisition)<a href="https://agentmods.dev/skills/san-npm/skills-ws/customer-acquisition"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/customer-acquisition/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/san-npm/skills-ws/customer-acquisition"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/customer-acquisition.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 | $0.00072 | $0.05240 |
| Opus 5.5 | $0.00029 | $0.02096 |
| Sonnet 5.5 | $0.00014 | $0.01048 |
| Haiku 4.5 | $0.00007 | $0.00524 |
Grade A, and why
customer-acquisition 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 2mo 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Acquisition
Workflow
1. CAC Calculation
Blended CAC (company-level):
Blended CAC = (Total Sales + Marketing spend) / New customers acquired
Per-channel CAC (more actionable):
Channel CAC = Channel spend (ads + tools + headcount allocation) / Customers from that channel
Fully-loaded CAC (most accurate):
Fully-loaded CAC = (Ad spend + Sales salaries + Marketing salaries + Tools + Agency fees + Content production) / New customers
What to include:
| Include | Don't include |
|---|---|
| Ad spend (all platforms) | Product development costs |
| Sales team compensation (base + commission) | Customer success costs |
| Marketing team compensation | Infrastructure/hosting |
| Marketing tools (HubSpot, analytics, etc.) | General overhead (rent, legal) |
| Content production costs | |
| Agency/contractor fees | |
| Event/sponsorship costs |
2. Channel Evaluation
There is no universal channel CAC. A "$150 Google CAC" is meaningless without industry, ACV, country, the funnel stage you count as a "customer" (lead vs trial vs paid vs net-of-refund), gross margin, and the measurement window. Benchmark against your own history and unit economics, not a generic table. Derive each channel's CAC from the formulas in §1, then score relative scalability/time/quality.
Scoring matrix — fill CAC from YOUR data (§1), score the rest 1–5:
| Channel | Your CAC (compute) | Scalability | Time to first result | Acquired-cohort LTV/quality | Score |
|---|---|---|---|---|---|
| Organic search / SEO | $___ | High | 6–12 mo | Often high intent | |
| Paid search (Google) | $___ | High | Immediate | High intent, capped by query volume | |
| Paid social (Meta Advantage+) | $___ | High | 1–2 wk | Varies by creative/offer | |
| LinkedIn ads | $___ | Medium | 1–2 wk | High for B2B/high-ACV | |
| Content / thought leadership | $___ | High | 3–6 mo | Compounding, high quality | |
| Referral program | $___ | Medium | 1–3 mo | Usually highest LTV, lowest CAC | |
| Outbound (SDR/cold) | $___ | Medium | 2–4 wk | High if ICP-targeted | |
| Partnerships / co-marketing | $___ | Low–Med | 3–6 mo | High, trust-transferred | |
| Events / field | $___ | Low | 1–3 mo | High-touch enterprise | |
| Product-led (PLG/viral) | $___ | Very high | Varies | Varies; watch self-serve→paid rate |
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.
- 2mo ago First seen · 257 lines · 72 tokens per session scan A 07525c24a894
customer-acquisition is a skill published in the GitHub repository san-npm/skills-ws (4 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 5,240 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
customer-acquisition compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| customer-acquisitionsan-npm/skills-ws | Skill | 4 | 1mo ago | MIT | A | 72 | Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |