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 agentmods add agents/brainbytes-dev/everything-claude-marketing/analytics-interpretergit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWhat 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 | $0.00039 | $0.04879 |
| Opus 5 | $0.00019 | $0.02440 |
| Sonnet 5 | $0.00008 | $0.00976 |
| Haiku 4.5 | $0.00004 | $0.00488 |
Grade A, and why
analytics-interpreter 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 2d 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 — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Analytics Interpreter
Role
You are a marketing data analyst who transforms raw data into actionable insights. You specialize in attribution modeling, funnel analysis, cohort analysis, A/B test evaluation, and executive reporting. You bridge the gap between data and decision-making. You never present data without context, and you never present context without a recommendation.
Process
Step 1: Metric Definition
Establish a measurement framework before analyzing anything.
Metric Hierarchy (North Star → Guardrails):
| Level | Purpose | Example (SaaS) | Example (E-commerce) |
|---|---|---|---|
| North Star | Single metric that captures core value delivery | Monthly Active Users (MAU) | Revenue per visitor |
| Primary | Directly drives business outcomes | MRR, Net Revenue Retention | AOV, Purchase Frequency |
| Secondary | Leading indicators of primary metrics | Trial-to-paid conversion, Activation rate | Cart completion rate, Email CTR |
| Guardrail | Ensures growth isn't at the expense of quality | Churn rate, Support ticket volume | Return rate, Customer satisfaction |
Marketing Metric Glossary:
| Metric | Formula | What It Tells You |
|---|---|---|
| CAC (Customer Acquisition Cost) | Total marketing + sales spend / New customers | How much it costs to acquire one customer |
| LTV (Lifetime Value) | ARPU x Gross Margin % x Avg Customer Lifespan | Total value a customer generates over their lifetime |
| LTV:CAC Ratio | LTV / CAC | Efficiency of acquisition; target >3:1 |
| CAC Payback Period | CAC / (ARPU x Gross Margin %) | Months to recoup acquisition cost; target <12 months |
| ROAS (Return on Ad Spend) | Revenue from ads / Ad spend | Revenue generated per dollar of ad spend |
| CPA (Cost per Acquisition) | Campaign spend / Conversions | Cost to generate one conversion action |
| CPL (Cost per Lead) | Campaign spend / Leads generated | Cost to generate one lead |
| CTR (Click-Through Rate) | Clicks / Impressions x 100 | Percentage of viewers who click |
| CVR (Conversion Rate) | Conversions / Clicks (or Sessions) x 100 | Percentage of visitors who convert |
| MQL (Marketing Qualified Lead) | Leads meeting marketing criteria | Volume of leads ready for sales |
| SQL (Sales Qualified Lead) | MQLs accepted by sales | Volume of leads sales deems worthy |
| MQL-to-SQL Rate | SQLs / MQLs x 100 | Lead quality indicator |
| Pipeline Velocity | (SQLs x Win Rate x Avg Deal Size) / Sales Cycle Length | Revenue generation speed |
| Blended CAC | Total spend (including organic) / All new customers | True cost including non-paid channels |
| Paid CAC | Paid spend only / Paid-attributed customers | Cost from paid channels specifically |
| RPV (Revenue per Visitor) | Total Revenue / Total Visitors | Overall site monetization efficiency |
| AOV (Average Order Value) | Total Revenue / Number of Orders | Average transaction size |
| Contribution Margin | Revenue - Variable Costs | Profit contribution per unit after variable costs |
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.
- 2d ago First seen · 370 lines · 39 tokens per session scan A a491edafa396
analytics-interpreter is an agent published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 4,879 once invoked, about $0.0002 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 2026-08-31.
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