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.
npx agentmods add skills/d-padmanabhan/agent-engineering-handbook/data-engineeringnpx skills add d-padmanabhan/agent-engineering-handbook --skill data-engineeringgit 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/skills/d-padmanabhan/agent-engineering-handbook/data-engineering)<a href="https://agentmods.dev/skills/d-padmanabhan/agent-engineering-handbook/data-engineering"><img src="https://agentmods.dev/badge/skills/d-padmanabhan/agent-engineering-handbook/data-engineering.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 | $0.00079 | $0.00791 |
| Opus 5 | $0.00039 | $0.00396 |
| Sonnet 5 | $0.00016 | $0.00158 |
| Haiku 4.5 | $0.00008 | $0.00079 |
Grade A, and why
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 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering
Scope
Use this skill to:
- Review data pipeline PRs (batch or streaming)
- Design new datasets, ingestion pipelines, and transformations
- Plan backfills/replays safely
- Apply platform-specific guidance for Databricks, Snowflake, Confluent Kafka, and Teradata
- Route cross-service Outbox/Inbox and Saga consistency design to the distributed transactions skill (
${HANDBOOK_ROOT}/skills/distributed-transactions/SKILL.md)
Core principles (defaults)
- Idempotent: safe to re-run for a given window/offset
- Contract-driven: schema + semantics + SLA are explicit
- Observable: each run emits counts, timings, and progress/watermarks
- Governed: least privilege, masking/row filtering for sensitive data
- Cost-aware: incremental + pruning by default; avoid full scans
Quick start: PR review workflow (local)
- Determine base branch (usually
main). - Collect git facts:
git branch --show-current
git log main..HEAD --oneline
git diff --name-status main...HEAD
git diff --numstat main...HEAD
git diff main...HEAD
- Review using the structure below.
Review output format
- Critical: correctness, data loss/duplication, security/PII leaks, breaking contracts
- Recommended: performance/cost risks, operational gaps, maintainability
- Optional: style, naming, documentation improvements
Quick start: design workflow (new pipeline / dataset)
Produce a short design covering:
- Inputs: sources, formats, volumes, SLAs
- Contract: schema, keys, semantics (event vs processing time), evolution policy
- Processing: batch vs streaming, watermarking/offset tracking, dedupe/upsert strategy
- Outputs: layers (raw/curated/serving), consumers, downstream blast radius
- Quality: freshness/volume/uniqueness checks, quarantine strategy
- Security: PII classification, masking/row filters, least privilege
- Ops: alerting, retries, DLQ/quarantine, runbook for backfills
- Cost: partitioning/pruning, incremental strategy, warehouse sizing (if relevant)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 78 lines · 79 tokens per session scan A 397d2c16a958
data-engineering is a skill published in the GitHub repository d-padmanabhan/agent-engineering-handbook (16 stars, last pushed 6d ago), licensed MIT. It adds 79 tokens to every session and 791 once invoked, about $0.0004 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 2026-09-03.
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