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 skills add irahardianto/awesome-agv --skill data-engineeringgit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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/irahardianto/awesome-agv/data-engineering)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/data-engineering"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/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/skills/irahardianto/awesome-agv/data-engineering"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 79 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 80 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00031 | $0.00640 |
| Opus 5.5 | $0.00012 | $0.00256 |
| Sonnet 5.5 | $0.00006 | $0.00128 |
| Haiku 4.5 | $0.00003 | $0.00064 |
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 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering Principles
Guidelines for building reliable, scalable data pipelines and platforms.
When to Invoke
- Designing data pipelines (ETL/ELT)
- Evaluating batch vs stream processing
- Data quality and governance requirements
- Data warehouse/lake architecture decisions
Pipeline Architecture
Design Principles
- Idempotent pipelines — re-running produces same result. Use upserts, not inserts.
- Schema evolution — handle new fields without breaking consumers.
- Exactly-once processing — deduplication at ingestion, idempotency keys.
- Incremental processing — process only new/changed data, not full reloads.
Patterns
| Pattern | When to Use |
|---|---|
| Batch ETL | Scheduled, high volume, latency-tolerant |
| Streaming | Real-time, event-driven, low latency |
| Lambda | Both batch and stream (complexity trade-off) |
| Kappa | Stream-only, reprocessing via replay |
| Medallion | Bronze (raw) → Silver (cleaned) → Gold (curated) |
Data Quality
Checks (Non-Negotiable)
- Completeness — no unexpected nulls in required fields
- Uniqueness — no duplicate records on primary keys
- Referential integrity — foreign keys resolve
- Freshness — data arrives within SLA window
- Volume — row counts within expected range (±threshold)
Framework
Source → Validate (schema, nulls, types) → Transform → Validate (business rules) → Load → Verify (counts, checksums)
Orchestration
| Tool | Strength |
|---|---|
| Apache Airflow | Most mature, Python-native, DAG-based |
| Dagster | Type-safe, asset-oriented, modern |
| Prefect | Pythonic, flow-based, cloud-native |
Best Practices
- DAGs should be idempotent and retriable
- Separate orchestration from computation
- Use backfill capabilities for historical reprocessing
- Alert on SLA breaches, not just failures
Data Modeling
| Model | When |
|---|---|
| Star schema | Analytics, BI dashboards, simple queries |
| Data Vault | Enterprise, auditability, multiple sources |
| Dimensional | Aggregated reporting, OLAP |
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 · 81 lines · 31 tokens per session scan A d884bd1ef02d
data-engineering is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 9d ago), licensed MIT. It adds 31 tokens to every session and 640 once invoked, about $0.0001 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.
Other skills, from other repositories
mlops-automation
Automate an MLOps project with mise tasks, lefthook hooks, Docker images, GitHub Actions, and MLflow tracking on a SQL backend. Use when adding a task runner, git hooks, CI/CD, or experiment tracking to a working package.
mlops-prototyping
Structure reproducible Jupyter notebooks with a fixed section layout, hoisted configuration, and leakage-free scikit-learn pipelines. Use when exploring a dataset, training a first model, or preparing a notebook for promotion.
mlops-observability
Make an ML system a glass box with reproducible runs, MLflow dataset lineage, drift monitoring, alerting, and SHAP explanations. Use when a deployed model needs traceability, monitoring, alerting, or explanation.
mlops-industrialization
Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.
ai-instruction-standards
A guide to creating and maintaining instruction files such as CLAUDE.md, AGENTS.md, and editor rules files for AI coding tools.
mlops-initialization
Initialize a production-ready Python MLOps project with uv, git, mise, and a shared editor setup. Use when starting a new repository, writing its first pyproject.toml, or repairing an inconsistent project skeleton.