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 vaquarkhan/data-engineering-agent-skills --skill enterprise-etl-and-data-integration-modernizationgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-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/skills/vaquarkhan/data-engineering-agent-skills/enterprise-etl-and-data-integration-modernization)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/enterprise-etl-and-data-integration-modernization"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/enterprise-etl-and-data-integration-modernization/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/vaquarkhan/data-engineering-agent-skills/enterprise-etl-and-data-integration-modernization"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/enterprise-etl-and-data-integration-modernization.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 | $0.00070 | $0.00684 |
| Opus 5 | $0.00035 | $0.00342 |
| Sonnet 5 | $0.00014 | $0.00137 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
enterprise-etl-and-data-integration-modernization 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise ETL And Data Integration Modernization
Overview
Use this skill when delivery depends on classic enterprise ETL tooling, not only modern code-first platforms. It helps agents reason about mapping logic, scheduler dependencies, restart behavior, metadata export, migration sequencing, and coexistence between legacy ETL platforms and newer Spark, dbt, Airflow, or lakehouse stacks.
When to Use
- working with
Informatica,Talend,DataStage,SSIS, orMatillion - reverse-engineering existing ETL mappings and workflows
- modernizing or migrating legacy ETL jobs
- adding quality, lineage, and operational controls to GUI-driven pipelines
- running hybrid estates where old and new tooling must coexist
Do not assume enterprise ETL logic is simple just because much of it is configured through a UI.
Workflow
-
Inventory the actual delivery surface. Capture:
- mappings and transformations
- parameter files and environment variables
- scheduler dependencies
- restart and checkpoint behavior
- external scripts and post-load actions
-
Recover business logic from the platform implementation. Identify:
- joins and filters
- surrogate-key logic
- SCD behavior
- reject handling
- data-quality rules embedded in mappings
-
Make environment and deployment assumptions explicit. Document:
- connection differences by environment
- credential handling
- promotion rules
- metadata dependencies
- non-obvious manual runbooks
-
Plan coexistence or migration safely. Decide:
- what remains on the legacy platform
- what moves to code-first pipelines
- how parity will be validated
- how cutover and rollback will work
-
Add observability and control points around the jobs. Include:
- run metadata
- reconciliation checks
- lineage capture
- failure classification
- repeatable deployment evidence
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The ETL tool already handles everything." | GUI tooling still hides logic, dependencies, and failure modes that must be made explicit. |
| "We can just rewrite it later." | Legacy ETL estates become harder to migrate the longer the hidden assumptions remain undocumented. |
| "The mapping is self-explanatory." | Parameter files, scheduler behavior, and reject handling often carry critical business logic. |
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.
- 11d ago First seen · 85 lines · 70 tokens per session scan A 3d0a52e1bff3
enterprise-etl-and-data-integration-modernization is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 684 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
kafka-shadowtraffic
Generate a ShadowTraffic configuration to populate a Kafka topic with realistic synthetic data. Discovers the target topic, its key and value schemas, and the correct serializers from the live cluster via any attached Kafka MCP server, then writes a ready-to-run shadowtraffic-config.json and Docker command. Use when…
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports anthropic/@anthropic-ai/sdk; user asks for…
migrating-ai-sdk-to-common-ai
Migrates Airflow projects from airflow-ai-sdk to apache-airflow-providers-common-ai 0.4.0+. Use when replacing airflow-ai-sdk with the official Airflow AI provider - migrating LLM decorators (@task.llm, @task.agent, @task.llmbranch, @task.embed), switching from model strings/objects to connection-based LLM…
creating-openlineage-extractors
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
telnyx-ai-inference-curl
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides REST API (curl) examples.
kafka-connector-review
Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect…