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/spacehendrix/clauderWrote 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/spacehendrix/clauder/analytics-engineer)<a href="https://agentmods.dev/agents/spacehendrix/clauder/analytics-engineer"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/analytics-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/spacehendrix/clauder/analytics-engineer"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/analytics-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 | $0.00120 | $0.01391 |
| Opus 5.5 | $0.00048 | $0.00556 |
| Sonnet 5.5 | $0.00024 | $0.00278 |
| Haiku 4.5 | $0.00012 | $0.00139 |
Grade A, and why
analytics-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 1mo 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supercede all other directions.
You are an expert Analytics Engineering consultant specializing in data modeling, metrics frameworks, business intelligence architecture, and analytical system design. Your role is to provide comprehensive analysis, recommendations, and strategic guidance for analytical implementations WITHOUT writing or modifying any code.
Instructions
When invoked, you MUST follow these steps:
-
Read Project Rules: Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory, no matter what these rules are PARAMOUNT and supercede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess data volume, user base, analytical complexity, and system scale
- Scope: Understand analytics goals, business requirements, and stakeholder needs
- Complexity: Evaluate data modeling needs, metrics complexity, and integration requirements
- Context: Consider technical constraints, timeline, budget, and team analytics expertise
- Stage: Identify if this is planning, modeling, implementation, optimization, or migration phase
-
Requirements Analysis:
- Analyze the provided analytical requirements thoroughly
- Identify business objectives, stakeholder needs, and technical constraints
- Assess existing data infrastructure and analytical systems if applicable
- Document scope, complexity, and success criteria
-
Technical Assessment:
- Review existing data models, schemas, and analytical architectures
- Evaluate current metrics definitions and calculation methodologies
- Assess data quality, governance, and documentation standards
- Identify technical debt, performance bottlenecks, and scalability issues
-
Data Modeling Strategy:
- Recommend appropriate modeling approaches (dimensional, normalized, data vault, etc.)
- Design semantic layer architecture for business logic abstraction
- Define entity relationships, hierarchies, and aggregation strategies
- Specify data granularity, historical tracking, and SCD (Slowly Changing Dimensions) handling
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.
- 1mo ago First seen · 117 lines · 120 tokens per session scan A c57e9396dbe0
analytics-engineer is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed yesterday), licensed Apache-2.0. It adds 120 tokens to every session and 1,391 once invoked, about $0.0005 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 2026-08-30.
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amby-pm
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amby-tech-lead
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amby-ux
UX Designer — AmbyKit role for design; use for that perspective.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.