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 Dev-Dennis-040/openclaw-agency-skills --skill engineering-data-engineergit clone --depth 1 https://github.com/Dev-Dennis-040/openclaw-agency-skillsWrote 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/dev-dennis-040/openclaw-agency-skills/engineering-data-engineer)<a href="https://agentmods.dev/skills/dev-dennis-040/openclaw-agency-skills/engineering-data-engineer"><img src="https://agentmods.dev/badge/skills/dev-dennis-040/openclaw-agency-skills/engineering-data-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/dev-dennis-040/openclaw-agency-skills/engineering-data-engineer"><img src="https://agentmods.dev/badge/skills/dev-dennis-040/openclaw-agency-skills/engineering-data-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 | $0.00044 | $0.03330 |
| Opus 5.5 | $0.00018 | $0.01332 |
| Sonnet 5.5 | $0.00009 | $0.00666 |
| Haiku 4.5 | $0.00004 | $0.00333 |
Grade A, and why
engineering-data-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 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 316 lines · 44 tokens per session scan A 0d86ecb9a169
engineering-data-engineer is a skill published in the GitHub repository Dev-Dennis-040/openclaw-agency-skills (11 stars, last pushed 7mo ago), with no licence file. It adds 44 tokens to every session and 3,330 once invoked, about $0.0002 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
Data Engineer
Expert data engineer specializing in building reliable data pipelines, lakehouse architectures, and scalable data infrastructure. Masters ETL/ELT, Apache Spark, dbt, streaming systems, and cloud data platforms to turn raw data into trusted, analytics-ready assets.
AI Engineer
Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.
ai-prompt-regression-testing
Use when comparing prompt changes against a fixed evaluation set: record actual model settings, required checks, missing results, judge limitations and case-level regression evidence.
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
big-data-cloud-automation
Automate Big Data Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
cortex-integrate
Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".