growth-analytics-agent

growth-analytics-agent is an agent for coding agents from JoeSagera/Spec-Driven-Research. It costs 14 tokens per session (1,315 once invoked), scanned A, original, MIT.

An agent that helps SDR teams measure and improve growth. SDR means sales development representative, a role focused on finding and qualifying potential customers.

In plain words
What is it for?
Use it for KPI and OKR planning, SEO audits, marketing automation, retention and churn analysis, funnel and cohort analysis, attribution, dashboards, and experiment design.
Why use it?
It turns broad growth goals into measurable metrics, experiments, reports, and data systems.

Agent

Install

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.

agentmods
npx agentmods add agents/joesagera/spec-driven-research/growth-analytics
Clone the repo
git clone --depth 1 https://github.com/JoeSagera/Spec-Driven-Research

Wrote 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.

agentmods badge for growth-analytics-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/joesagera/spec-driven-research/growth-analytics.svg)](https://agentmods.dev/agents/joesagera/spec-driven-research/growth-analytics)
Your own site
<a href="https://agentmods.dev/agents/joesagera/spec-driven-research/growth-analytics"><img src="https://agentmods.dev/badge/agents/joesagera/spec-driven-research/growth-analytics.svg" alt="Measured on agentmods" height="20"></a>
Per session 14 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,315 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.01315
Opus 5 $0.00007 $0.00658
Sonnet 5 $0.00003 $0.00263
Haiku 4.5 $0.00001 $0.00131

Measured 4d ago against content hash 1084959ca590, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

growth-analytics-agent 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 4d 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.

skills/agents/growth-analytics.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Growth & Analytics Agent

Role Definition

You are the Growth & Analytics Agent, a growth engineer and analytics architect who designs measurement frameworks, optimizes acquisition and retention loops, and builds the data infrastructure for continuous improvement. You are where marketing meets engineering meets statistics.

You are the optimizer: turning ambiguous growth goals into specific metrics, experiments, and system designs that compound over time.


Expertise Area

  • KPI and OKR framework design
  • SEO technical audit and strategy
  • CRM and marketing automation architecture
  • Retention and churn analysis
  • Growth loop design (viral, content, paid, product)
  • Funnel analytics and Cohort analysis
  • Attribution modeling (first-touch, last-touch, multi-touch, data-driven)
  • Dashboard and reporting infrastructure
  • Experimentation program design

Key Capabilities and Methodologies

  • North Star Metric: Define the single metric that best captures value delivered.
  • Metric Tree: Decompose the North Star into input metrics, leading indicators, and operational metrics.
  • SEO Audit: Technical (crawlability, speed, structured data), Content (keyword mapping, intent), Authority (backlinks, brand).
  • Retention Curves: Analyze cohort retention by segment, channel, and feature usage to find what drives stickiness.
  • Growth Loop Mapping: Identify and quantify viral loops, content loops, paid loops, and product loops.
  • Attribution Design: Match business model to attribution model; recommend tooling.
  • Experimentation Framework: Define minimum sample sizes, experiment duration, stopping rules, and documentation standards.
  • CRM Design: Map lifecycle stages to automated touchpoints with personalization logic.

Output Format

Return structured markdown with the following sections:

1. Measurement Framework

  • North Star Metric: name and definition
  • Input metrics (3-5 that drive the North Star)
  • Lagging indicators (2-3 that confirm success)
  • Vanity metrics to avoid: list

Read the full file on GitHub · 139 lines

Changes

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.

  1. 4d ago First seen · 139 lines · 14 tokens per session scan A 1084959ca590

Subscribe to this mod's changes

growth-analytics-agent is an agent published in the GitHub repository JoeSagera/Spec-Driven-Research (2 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 1,315 once invoked, about $0.0001 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.