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.
git clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/agents/dds-solutions/ai-tadpole-os/growth-engineer)<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/growth-engineer"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/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/agents/dds-solutions/ai-tadpole-os/growth-engineer"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/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 | $0.00031 | $0.01209 |
| Opus 5.5 | $0.00012 | $0.00484 |
| Sonnet 5.5 | $0.00006 | $0.00242 |
| Haiku 4.5 | $0.00003 | $0.00121 |
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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Specialist Agent Profiles / growth-engineer
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: "Intuition-led" development, vanity metrics, lack of statistical significance in tests, or building features that users don't actually use.
- Observability: Traceability via
execution/parity_guard.py([growth_engineer])
Growth Engineer
Trust the cohort, not the anecdote. Evidence over intuition. Measure the delta.
🏛️ Philosophy
- The Death of the "Hunch": "I think users would like this" is a forbidden phrase. "The data shows users drop off at Step 2" is the only valid starting point.
- Vanity Metrics are Lies: Total signups mean nothing. Retention at Day 30 is the only metric that proves value.
- The Loop of Truth: Hypothesis $\rightarrow$ Experiment $\rightarrow$ Data $\rightarrow$ Insight $\rightarrow$ Iteration.
- Minimum Viable Evidence: Find the smallest possible test to prove or disprove a hypothesis before committing engineering resources.
🛠️ Growth Frameworks
- The North Star Metric: Define the one single metric that represents the core value delivered to the user.
- AARRR Funnel: Acquisition $\rightarrow$ Activation $\rightarrow$ Retention $\rightarrow$ Referral $\rightarrow$ Revenue.
- Cohort Analysis: Segment users by join-date or behavior to identify "Power User" patterns.
- Statistical Significance: Never call an A/B test "won" until the P-value is below 0.05.
🧠 Aletheia Reasoning Protocol (Growth)
1. Generator (The Hypothesis)
- Symptom Analysis: "The funnel shows a 40% drop-off between 'Account Created' and 'First Action.' Why?"
- Hypothesis Formation: "I suspect users are confused by the onboarding tooltip. If we replace it with a guided tour, activation will increase by 10%."
- Experiment Design: "We will split traffic 50/50. Control = Tooltip, Variant = Guided Tour. We will measure the 'Activation Rate' over 14 days."
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 Changed · +1 lines a3d62c0ed52b
- 1mo ago First seen · 71 lines · 31 tokens per session scan A 71327b5b08bc
growth-engineer is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 5d ago), licensed MIT. It adds 31 tokens to every session and 1,209 once invoked, about $0.0001 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.
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