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/hamzaamjad/cursor-rulesWrote 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/rules/hamzaamjad/cursor-rules/203-analytics-engineering)<a href="https://agentmods.dev/rules/hamzaamjad/cursor-rules/203-analytics-engineering"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/203-analytics-engineering/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/rules/hamzaamjad/cursor-rules/203-analytics-engineering"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/203-analytics-engineering.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 | $0.00408 |
| Opus 5.5 | $0.00000 | $0.00163 |
| Sonnet 5.5 | $0.00000 | $0.00082 |
| Haiku 4.5 | $0.00000 | $0.00041 |
Grade A, and why
203-analytics-engineering 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.
What it actually says
analytics-engineering.mdc
- Purpose: Guide development of Mirror's analytics engineering layer using dbt+DuckDB for transforming raw personal data into AI-consumable information.
- Requirements:
- Privacy-First: All transformations run locally. Set
send_anonymous_usage_stats: falsein dbt_project.yml - Incremental Processing: Use
{{ is_incremental() }}for efficiency on personal devices - Semantic Layer: Models follow staging → intermediate → marts pattern for DIKW progression
- Testing Coverage: Every model needs primary key test + business logic validation
- Documentation: Each model requires description explaining its role in the semantic layer
- Privacy-First: All transformations run locally. Set
- Validation:
- Check: Does model compile with
dbt compile? - Check: Are PII fields hashed using
{{ hash_pii() }}macro? - Check: Do incremental models have proper unique_key?
- Check: Is there a corresponding test in schema.yml?
- Check: Does model compile with
- Patterns:
- Daily Aggregations: Use
date_trunc('day', timestamp)for consistent grouping - Score Calculations: Normalize to 0-100 range for AI interpretation
- Anomaly Handling: Filter outliers in staging, don't propagate to marts
- Mobile Sync: Only sync mart tables, never raw/staging data
- Daily Aggregations: Use
- Wildcard Ideas:
- Real-time CDC: Use DuckDB's APPENDER API for streaming inserts
- Graph Analytics: Model social health metrics using DuckDB's recursive CTEs
- Federated Learning: Pre-aggregate personal models for privacy-preserving ML
- Source References: Mirror dbt evaluation, DuckDB best practices
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 · 28 lines · 0 tokens per session scan A 58974df28e24
203-analytics-engineering is a cursor rule published in the GitHub repository hamzaamjad/cursor-rules (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 408 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 2026-08-31.
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