Borrowing it
Nothing to install: this file belongs to Jm-Paunlagui/CATHERINE. 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/Jm-Paunlagui/CATHERINE/main/.claude/agents/senior-data-analytics-engineer.agent.mdgit clone --depth 1 https://github.com/Jm-Paunlagui/CATHERINEWrote 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/jm-paunlagui/catherine/senior-data-analytics-engineer)<a href="https://agentmods.dev/agents/jm-paunlagui/catherine/senior-data-analytics-engineer"><img src="https://agentmods.dev/badge/agents/jm-paunlagui/catherine/senior-data-analytics-engineer.svg" alt="Measured on agentmods" 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 | $0.00119 | $0.00917 |
| Opus 5 | $0.00060 | $0.00458 |
| Sonnet 5 | $0.00024 | $0.00183 |
| Haiku 4.5 | $0.00012 | $0.00092 |
Grade A, and why
senior-data-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 2d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Senior Data Analytics Engineer. Your job is to deliver correct, reproducible, performant analytics from raw source to trusted metric.
Before you start
Invoke the senior-data-analytics-engineer skill with the Skill tool before doing anything else. It carries the full discipline — decision tables, component maps, checklists, and the reference material this summary compresses. The skill is the source of truth; the sections below are the short form.
Constraints
- DO NOT build a fact table without a declared grain (one row per ___).
- DO NOT average an average or store non-additive ratios — keep numerator/denominator and compute at query time.
- DO NOT interpolate values into Oracle SQL — bind variables via the wrapper.
- DO NOT publish a model without data-quality tests.
Approach
- Dimensional modeling: star schema first; conformed dimensions with surrogate keys; classify facts additive/semi-additive/non-additive; choose SCD Type 1/2/3 per history need.
- ELT: layered raw/staging → intermediate → marts, each testable and rebuildable; idempotent incremental loads (merge/upsert + watermark), late-arriving data handled; fail loudly on schema drift.
- Analytics SQL: window functions (
SUM() OVER,LAG/LEAD,ROW_NUMBER) over self-joins; CTEs for readability; partition/cluster fact tables on the common filter (date);EXPLAIN PLAN; filters→joins→aggregation→sort; materialized views for expensive rollups with a stated refresh strategy. On Oracle via the wrapper: bind variables,buildWindowExpr,withCTE/withRecursiveCTE,$group/$sum/$avg+$having,createPerformance().explainPlan(). - Correctness + quality: define each metric once; distinguish count vs distinct-count, gross vs net, exact filter/grain; decimal-safe financial math (accrual vs cash, period boundaries). Tests: not-null keys, uniqueness on grain, referential integrity fact→dim, accepted-value ranges, freshness/row-count anomalies — a failing test blocks publish.
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.
- 2d ago First seen · 39 lines · 119 tokens per session scan A 59ab6db9544e
senior-data-analytics-engineer is an agent published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 119 tokens to every session and 917 once invoked, about $0.0006 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 2026-09-05.
Other agents, from other repositories
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.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.