Name: senior-data-engineer
Rating: 60.8 (1 reviews)
Author: alirezarezvani

senior-data-engineer

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

Quality

71%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./.gemini/skills/senior-data-engineer/SKILL.md

The canonical home for this skill is senior-data-engineer in alirezarezvani/claude-skills

Quality

Content

53%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well organized with clear navigation and a useful decision framework, but it leans on reference and script files that are not present in the bundle, and it re-explains data-engineering concepts Claude already knows. Adding the missing bundle files and tightening redundant sections would raise the score.

Suggestions

Ship the referenced bundle files (references/workflows.md, data_pipeline_architecture.md, data_modeling_patterns.md, dataops_best_practices.md, troubleshooting.md) and the scripts/ files invoked in Quick Start, or remove the dangling references.

Remove or compress the Trigger Phrases section, since it duplicates the frontmatter description and adds token cost without new guidance.

Tighten the Batch/Streaming, Lambda/Kappa, and Warehouse/Lakehouse comparison tables to decision criteria only, cutting the concept explanations Claude already knows.

Dimension	Reasoning	Score
Conciseness	Mostly efficient, but the Trigger Phrases section largely duplicates the frontmatter description and the Batch/Streaming, Lambda/Kappa, and Warehouse/Lakehouse tables re-explain concepts Claude already knows.	3 / 5
Actionability	Quick Start gives concrete commands with specific flags, but they invoke scripts under scripts/ and schemas/ that do not exist in the bundle, leaving the guidance incomplete rather than copy-paste executable.	3 / 5
Workflow Clarity	A rough generate/validate/optimize sequence is implied in Quick Start, but the actual pipeline-building workflow is deferred to references/workflows.md (which is absent) and no explicit validation checkpoints or feedback loops are present in the body.	3 / 5
Progressive Disclosure	Good structure with a table of contents and clearly signaled one-level-deep references to references/*.md files; the main gap is that none of the referenced files actually exist in the bundle.	4 / 5
	Total	13 / 20 Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it uses third-person voice, names concrete capabilities comprehensively, and includes an explicit 'Use when...' trigger clause. It is slightly held back from a perfect score by trigger-term synonym coverage and minor overlap risk with adjacent engineering skills.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities—building scalable data pipelines, ETL/ELT systems, data modeling, pipeline orchestration, data quality, and DataOps—giving comprehensive coverage of the domain.	5 / 5
Completeness	Explicitly answers both what the skill does and when to use it, with a concrete 'Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues' clause.	5 / 5
Trigger Term Quality	Good coverage of natural phrases users say ('data pipelines', 'ETL/ELT', 'data architectures', 'data governance', 'troubleshooting data issues'), though a few common synonyms are absent.	4 / 5
Distinctiveness Conflict Risk	Clearly niches on data engineering with distinct triggers, with only minor overlap risk against closely related software-engineering or data-analysis skills.	4 / 5
	Total	18 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 8 missing	Warning

	Total	15 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/senior-data-engineer/SKILL.md
Commit: 19392f7

Reviewed: ~14 hours ago

Table of Contents

Discovery Implementation Validation

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