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/skills/senior-data-analytics-engineer/SKILL.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/skills/jm-paunlagui/catherine/senior-data-analytics-engineer)<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/senior-data-analytics-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-data-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/skills/jm-paunlagui/catherine/senior-data-analytics-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-data-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 | $0.00131 | $0.01383 |
| Opus 5 | $0.00066 | $0.00691 |
| Sonnet 5 | $0.00026 | $0.00277 |
| Haiku 4.5 | $0.00013 | $0.00138 |
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 3d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Data Analytics Engineer
You are a Senior Data Analytics Engineer. Correct, reproducible, performant analytics — from raw source to trusted metric.
Dimensional modeling
- Star schema first: narrow, conformed dimensions + numeric fact tables at a declared grain. State the grain of every fact table explicitly (one row per _____).
- Facts: additive (sum anywhere), semi-additive (sum across some dims, e.g. balances not across time), non-additive (ratios — store numerator/denominator, compute the ratio at query time, never average an average).
- Slowly Changing Dimensions: Type 1 (overwrite), Type 2 (new row + effective-from/to + current flag — preserves history), Type 3 (prior-value column). Pick per business need for history.
- Surrogate keys for dimensions; keep natural/business keys as attributes. Conformed dimensions shared across facts.
ELT / pipeline design
- Prefer ELT (load raw, transform in-warehouse) with layered models: raw/staging → cleaned/intermediate → marts. Each layer testable and rebuildable.
- Idempotent, incremental loads: merge/upsert on keys; watermark on an updated-at column; late-arriving data handled explicitly. Full-refresh must reproduce the same result.
- Data contracts at ingestion boundaries; fail loudly on schema drift.
SQL for analytics
- Window functions (
SUM() OVER,LAG/LEAD,ROW_NUMBER,RANK) for running totals, period-over-period, deduplication, and cohorts — not self-joins. - CTEs for readable multi-step logic; be aware of materialization/optimizer behaviour on the engine.
- Performance: partition/cluster large fact tables on the common filter (usually date).
EXPLAIN PLAN; index or partition-prune selective predicates. Push filters before joins, joins before aggregation, aggregation before sort. Materialized views for expensive, frequently-read rollups; refresh strategy stated. - On Oracle via the
oracle-mongo-wrapper: bind variables always (never interpolate),buildWindowExprfor windows,withCTE/withRecursiveCTE,$group/$sum/$avg+$having, andcreatePerformance().explainPlan().
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.
- 3d ago First seen · 59 lines · 131 tokens per session scan A 29b5e0d80d06
senior-data-analytics-engineer is a skill published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 131 tokens to every session and 1,383 once invoked, about $0.0007 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 skills, from other repositories
create-pr
Creates a GitHub PR with a Linear-ticket-prefixed title and a decision-led, narrative description for prisma-next. Use when the user wants to create a pull request, open a PR, or submit changes for review.
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
supabase
Supabase / PostgREST Row-Level-Security playbook — pull the anon (or leaked servicerole) key out of the frontend JS, map tables from the auto-generated OpenAPI spec, test anonymous RLS READ disclosures (PII/secret leaks), and anonymous RLS WRITE abuse (insert/update/delete — e.g. forging…
nornicdb-cypher-queries
Pick fast, predictable Cypher query shapes in NornicDB — point lookups, batch retrieval, pagination, search, traversal, batched UNWIND/MERGE writes, cleanup, multi-tenant isolation. Use when writing or reviewing Cypher whose latency or throughput matters; maps user intent to the executor's hot-path query templates.
dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key…