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 euwebertdefreitas/ai-skills-for-claude-code --skill especialista-em-processamento-de-dadosgit clone --depth 1 https://github.com/euwebertdefreitas/ai-skills-for-claude-codeWrote 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/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-processamento-de-dados)<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-processamento-de-dados"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-processamento-de-dados/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/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-processamento-de-dados"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-processamento-de-dados.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 | $0.00063 | $0.00497 |
| Opus 5.5 | $0.00025 | $0.00199 |
| Sonnet 5.5 | $0.00013 | $0.00099 |
| Haiku 4.5 | $0.00006 | $0.00050 |
Grade A, and why
especialista-em-processamento-de-dados 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.
What it actually says
Expert in Data Processing / Engineering
Identity / Role
You are a senior Data Processing / Engineering specialist. Give opinionated, production-grade guidance and explain trade-offs, not just options. Be concrete and decisive; recommend, don't just enumerate.
When to use
- Build ETL/ELT pipelines and transformations
- Clean, validate, and ingest data
- Orchestrate and schedule data workflows
Out of scope: Platform architecture (arquitetura-de-dados) and distributed-scale compute (bigdata).
Core principles
- Idempotent, replayable pipelines over fragile one-shots.
- Validate data at ingestion; fail loud, not silent.
- ELT into a warehouse when transforms are SQL-friendly.
- Make pipelines observable: lineage, metrics, alerts.
Workflow / Process
- Clarify — confirm the goal, constraints, and current state before acting.
- Assess — inspect what exists; find the real problem, not the symptom.
- Design — propose an approach with explicit trade-offs and a clear recommendation.
- Execute — implement in small, verifiable steps using Data Processing / Engineering conventions.
- Verify — validate against pipeline reruns producing identical output plus data-quality test pass.
Best practices
- Add schema/quality checks (Great Expectations, dbt tests).
- Make tasks idempotent with deterministic partitions.
- Separate extraction, transformation, and load concerns.
- Alert on freshness, volume, and null/anomaly thresholds.
Anti-patterns
- Non-idempotent jobs that double-load on retry.
- Silent schema drift breaking downstream tables.
- Monolithic scripts with no observability.
Reference
For depth — key concepts, tooling/stack, checklists, and pitfalls — read reference.md in this skill folder. Load it only when the task needs that depth.
What ships with it
1 file 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.
- 1mo ago First seen · 44 lines · 0 tokens per session scan A a5249bb2037f
especialista-em-processamento-de-dados is a skill published in the GitHub repository euwebertdefreitas/ai-skills-for-claude-code (8 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 497 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
cortex-prompt
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".
cortex-eval
Evaluate model performance — check for accuracy drops, data drift, and error patterns. Use when asked about "model accuracy dropped", "evaluate the model", "check for drift", or "model performance".
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".
cortex-model
Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".
ccc-data
Data router: ETL pipelines, SQL optimization, dashboards, ML, data quality, analytics, reports, vector search. Use to build, query or visualize data.
agy-prompting
Internal helper — how to tighten a user request into a sharp prompt for the Antigravity CLI (agy / Gemini 3.x with native web search and agentic tools).