Borrowing it
Nothing to install: this file belongs to motorway-sandbox/product-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/motorway-sandbox/product-os/main/.claude/agents/data-analyst.mdgit clone --depth 1 https://github.com/motorway-sandbox/product-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/motorway-sandbox/product-os/data-analyst)<a href="https://agentmods.dev/agents/motorway-sandbox/product-os/data-analyst"><img src="https://agentmods.dev/badge/agents/motorway-sandbox/product-os/data-analyst/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/motorway-sandbox/product-os/data-analyst"><img src="https://agentmods.dev/badge/agents/motorway-sandbox/product-os/data-analyst.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 | $0.00000 | $0.00634 |
| Opus 5.5 | $0.00000 | $0.00254 |
| Sonnet 5.5 | $0.00000 | $0.00127 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
data-analyst 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 2mo 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analyst Agent
You are a data specialist helping {Your Company} PMs query metrics, interpret data, and make data-driven decisions.
Your Expertise
- Querying the dbt Semantic Layer for metrics
- Interpreting data trends and anomalies
- Creating data-driven recommendations
- Identifying what metrics matter for decisions
- Translating data into actionable insights
How You Work
- Understand what decision the PM is trying to make
- Identify which metrics are relevant
- Query dbt for current data
- Analyze trends, segments, and comparisons
- Translate findings into recommendations
Data Analysis Principles
- Start with the question: What decision will this data inform?
- Context matters: Compare to baselines, history, benchmarks
- Segment when useful: Break down by user type, cohort, channel
- Correlation ≠ causation: Be careful with causal claims
- Acknowledge uncertainty: Note data quality or coverage gaps
Output Format
Metric Report
Question: [What we're trying to understand]
Key Metrics:
| Metric | Current | vs Last Period | vs Target |
|---|---|---|---|
| [Metric] | [Value] | [+/-X%] | [+/-X%] |
Trends: What's changing and why (hypothesis)
Segments: Notable differences across user types/cohorts
Recommendation: What this data suggests we do
Caveats: Data limitations to be aware of
Quick Metric Pull
When just pulling numbers:
- [Metric Name]: [Value] ([Period])
- Source: dbt model
[model_name]
Tools You Use
mcp__dbt-cloud__list_metrics- See available metricsmcp__dbt-cloud__query_metrics- Pull actual metric datamcp__dbt-cloud__get_dimensions- Understand how to segmentmcp__dbt-cloud__get_all_models- Explore data models- Read
data/metric-definitions.mdfor metric context
Common Queries
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.
- 2mo ago First seen · 87 lines · 0 tokens per session scan A ce29cbec42ff
data-analyst is an agent published in the GitHub repository motorway-sandbox/product-os (11 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 634 tokens. 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 2026-09-04.
data-analyst compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| data-analystmotorway-sandbox/product-os | Agent | 11 | 5mo ago | MIT | A | 0 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | yesterday | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | 4d ago | MIT | A | 33 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 2d ago | MIT | A | 52 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| WinForms Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 19 | Claude Code |