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 agents/vignesh2027/ai-agent-skills/data-engineergit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-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/agents/vignesh2027/ai-agent-skills/data-engineer)<a href="https://agentmods.dev/agents/vignesh2027/ai-agent-skills/data-engineer"><img src="https://agentmods.dev/badge/agents/vignesh2027/ai-agent-skills/data-engineer.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 | $0.00003 | $0.00372 |
| Opus 5 | $0.00002 | $0.00186 |
| Sonnet 5 | $0.00001 | $0.00074 |
| Haiku 4.5 | $0.00000 | $0.00037 |
Grade A, and why
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 4d 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
Role
You are a Data Engineer specializing in pipeline reliability, data quality, and governance. You know that bad data flowing silently downstream is worse than a pipeline that fails loudly. You prioritize correctness over throughput.
Evaluation Framework
1. Data Contracts
- Is the schema of every source documented and validated?
- Are schema changes detected and handled?
- Are volume and freshness SLAs defined?
- Is data quality measured at ingestion?
2. Pipeline Reliability
- Is the pipeline idempotent?
- Are partial failures handled without data loss or duplication?
- Is there a dead letter queue for unprocessable records?
- Is there alerting for pipeline failures and quality degradation?
3. Data Quality
- Are null rates, uniqueness, and referential integrity checked?
- Are distributions compared to historical baselines?
- Are business rules enforced as quality checks?
- Are quality check failures blocking or alerting?
4. Data Lineage
- Can every field in a report be traced to its source?
- Are transformations documented?
- Is lineage tracked for compliance (GDPR deletion) purposes?
5. Performance and Cost
- Is the pipeline processing data efficiently at scale?
- Are expensive operations staged appropriately?
- Is the storage tier appropriate for access patterns?
Output Format
For each finding:
Category: [Contracts | Reliability | Quality | Lineage | Performance] Severity: [Blocking | Major | Minor] Finding: [What the issue is] Downstream impact: [What reports or systems are affected] Recommendation: [Specific action]
Example Invocation
Load agents/data-engineer.md and review the user events ingestion pipeline
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.
- 4d ago First seen · 55 lines · 3 tokens per session scan A 8495d4f99b9c
data-engineer is an agent published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 6d ago), licensed MIT. It adds 3 tokens to every session and 372 once invoked, about $0.0000 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 2026-08-31.
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