data-engineer

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.

Quality

60%

Does it follow best practices?

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Securityby

Passed

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

Quality

Content

20%

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

The body functions as a persona catalog rather than actionable guidance: it is verbose with tool lists Claude already knows, lacks any executable code or commands, and provides only abstract workflow steps without validation checkpoints. Structure exists but the bulk should be offloaded to reference files.

Suggestions

Replace the large Capabilities/Knowledge Base tool catalogs with a concise overview and move the detailed tool inventory into a references file (e.g. CAPABILITIES.md), keeping only what Claude does not already know in SKILL.md.

Add concrete, executable guidance — example dbt project snippets, Airflow DAG skeletons, or Spark job templates — instead of abstract instructions like 'Choose architecture, storage, and orchestration tools.'

Insert explicit validation checkpoints into the Response Approach workflow (e.g. run data-quality checks with Great Expectations and only write to production sinks on pass), turning the linear steps into a validate-fix-retry feedback loop.

Dimension	Reasoning	Score
Conciseness	The Capabilities, Behavioral Traits, and Knowledge Base sections re-catalog technologies Claude already knows (Snowflake, BigQuery, Spark, Kafka, etc.), padding the body with unnecessary context rather than adding net-new guidance.	1 / 3
Actionability	There is no executable code or concrete command anywhere; instructions like "Define sources, SLAs, and data contracts" and "Choose architecture, storage, and orchestration tools" describe rather than instruct.	1 / 3
Workflow Clarity	The Response Approach lists a sequenced 8-step process, but there are no explicit validation checkpoints or error-recovery feedback loops for batch operations that write to production sinks, which caps this dimension at 2.	2 / 3
Progressive Disclosure	The skill is a monolithic ~215-line file with ~150 lines of capability catalog inlined that would be better split into a reference file; no bundle files or external references are signaled, matching the inline-content anchor.	2 / 3
	Total	6 / 12 Passed

Description

100%

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

A strong description that concisely states concrete capabilities, names specific tools, and provides an explicit use-when trigger clause with a clear engineering niche. Third-person voice is maintained throughout with no first/second-person phrasing.

Dimension	Reasoning	Score
Specificity	"Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow" lists multiple concrete actions naming specific tools, matching the anchor for multiple specific concrete actions.	3 / 3
Completeness	It states what the skill does (Build/Implements...) and when to use it via the explicit "Use PROACTIVELY for..." trigger clause, clearly answering both what and when.	3 / 3
Trigger Term Quality	"data pipeline design, analytics infrastructure, modern data stack implementation" combined with "data pipelines, data warehouses, streaming" gives good coverage of natural terms a user would say, matching the high-coverage anchor.	3 / 3
Distinctiveness Conflict Risk	The data-engineering niche is signaled by specific infrastructure/orchestration tools (Spark, dbt, Airflow), making it unlikely to trigger for unrelated skills.	3 / 3
	Total	12 / 12 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
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: rmyndharis/antigravity-skills
Path: skills/data-engineer/SKILL.md
Commit: e63f7dd

Reviewed: about 4 hours ago

Table of Contents

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