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/d-padmanabhan/agent-engineering-handbookWrote 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/d-padmanabhan/agent-engineering-handbook/480-data-engineering)<a href="https://agentmods.dev/rules/d-padmanabhan/agent-engineering-handbook/480-data-engineering"><img src="https://agentmods.dev/badge/rules/d-padmanabhan/agent-engineering-handbook/480-data-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/d-padmanabhan/agent-engineering-handbook/480-data-engineering"><img src="https://agentmods.dev/badge/rules/d-padmanabhan/agent-engineering-handbook/480-data-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 | $0.01462 | $0.01462 |
| Opus 5.5 | $0.00585 | $0.00585 |
| Sonnet 5.5 | $0.00292 | $0.00292 |
| Haiku 4.5 | $0.00146 | $0.00146 |
Grade A, and why
480-data-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.
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering Ruleset
Audience: data engineers and platform engineers building batch + streaming pipelines
Goal: make data systems safe-by-default, reproducible, observable, and cost-aware across engines (Databricks, Snowflake, Kafka, Teradata)
[!NOTE] This rule is cross-platform. Pair it with engine-specific rules:
481-databricks.mdc482-snowflake.mdc483-kafka.mdc484-teradata.mdc
Non-negotiables (defaults)
- Idempotency: re-running the same job for the same input window must not duplicate or corrupt outputs.
- Deterministic replays: backfills and replays must be reproducible from inputs + code + config.
- Schema contracts: define and enforce what changes are allowed (additive vs breaking).
- Data quality: validate critical invariants close to ingestion and before publishing.
- Governance: treat PII and secrets as production incidents; enforce least privilege.
- Observability: every pipeline run emits enough signals to debug quickly.
- Cost discipline: design for pruning/incremental processing; avoid full scans by default.
Data contracts and schema evolution
Contract basics
- Publish a stable contract per dataset/topic (schema + semantics + SLA).
- Include:
- keys (natural/business keys and/or surrogate keys)
- event time vs processing time meaning
- nullability and defaults
- allowed late-arrival window
- dedupe strategy (if any)
Schema evolution rules of thumb
- Prefer additive changes (new nullable column) over destructive changes.
- Breaking changes require an explicit migration plan:
- dual-write/dual-read window
- versioned outputs (
v1,v2) or compatibility mode - clear rollback strategy
[!WARNING] Changing meaning without changing schema is still a breaking change (for example: units, currency, time zone, enums).
Idempotency patterns (batch + streaming)
“Prove then publish” pattern
- Write to a staging location/table.
- Validate row counts and invariants.
- Publish via an atomic swap/merge (engine-specific).
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 · 191 lines · 1,462 tokens per session scan A 3d9a9124171e
480-data-engineering is a cursor rule published in the GitHub repository d-padmanabhan/agent-engineering-handbook (19 stars, last pushed 2d ago), licensed MIT. It adds 1,462 tokens to every session, about $0.0058 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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