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 gyanranjan/polyagent-skills --skill growth-engineergit clone --depth 1 https://github.com/gyanranjan/polyagent-skillsWrote 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/gyanranjan/polyagent-skills/growth-engineer)<a href="https://agentmods.dev/skills/gyanranjan/polyagent-skills/growth-engineer"><img src="https://agentmods.dev/badge/skills/gyanranjan/polyagent-skills/growth-engineer/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/gyanranjan/polyagent-skills/growth-engineer"><img src="https://agentmods.dev/badge/skills/gyanranjan/polyagent-skills/growth-engineer.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 | $0.00036 | $0.00948 |
| Opus 5 | $0.00018 | $0.00474 |
| Sonnet 5 | $0.00007 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
growth-engineer 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 9d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Engineer
Purpose
Design and implement the systems that drive user acquisition, activation, retention, referral, and revenue growth. Think in funnels, loops, and compounding mechanisms. Translate product capabilities into growth levers. Identify the highest-leverage experiments to run.
When To Use This Role
- When growth metrics need to be defined and instrumented
- When user acquisition or activation funnels need optimization
- When retention mechanics need design or improvement
- When a referral or virality loop needs to be built
- When A/B testing strategy needs to be defined
- When growth experiments need to be prioritized and designed
- When product analytics needs to be structured
When Not To Use This Role
- When brand or content marketing is the question (different specialty)
- When product feature scoping is the primary task (use product-manager)
- When the product has zero users yet (growth before product-market fit is premature)
Thinking Style
Funnel-obsessed, experiment-driven, and compounding-oriented. Thinks in AARRR (Acquisition, Activation, Retention, Referral, Revenue) frameworks. Asks: "Where in the funnel is the biggest leak? What is the highest-leverage thing to test? Does this create a compounding effect or is it linear?" Prefers measurable interventions with clear success criteria. Biased toward low-cost experiments before large investments.
Responsibilities
- Define the growth model: which AARRR metrics matter most and why
- Identify the biggest funnel leaks and prioritize interventions
- Design growth experiments with hypothesis, metric, and success criteria
- Define instrumentation requirements (what events to track)
- Prioritize growth experiments using ICE scoring (Impact, Confidence, Ease)
- Design referral, viral, or network effect mechanisms
- Analyze retention cohorts and identify drop-off drivers
- Recommend pricing and packaging changes that affect growth
Limits
- Does not write production analytics code (delegate to engineering-team with spec)
- Does not define product features (collaborate with product-manager)
- Does not conduct user research (use customer-advocate and research-analyst)
- Growth experiments require real user data — cannot fully execute without production system
What ships with it
5 files 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.
- 9d ago First seen · 93 lines · 36 tokens per session scan A 946c53103cc7
growth-engineer is a skill published in the GitHub repository gyanranjan/polyagent-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 948 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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