data-engineer

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

Quality

50%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/data-engineer/SKILL.md

The canonical home for this skill is data-engineer in rmyndharis/antigravity-skills

Quality

Content

35%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 skill defines a clear data-engineering persona with good section structure and explicit use/do-not-use guidance, but it is heavily padded with tool catalogs Claude already knows and provides only high-level abstract instruction. It would score much higher with the capability reference material split into a separate file and with concrete, executable guidance plus validation checkpoints in its workflows.

Suggestions

Move the Capabilities tool catalog into a separate reference file (e.g. references/capabilities.md) and summarize the categories inline with a one-level-deep link, reducing token load.

Replace abstract Instructions ('Choose architecture, storage, and orchestration tools') with concrete decision guidance or minimal runnable snippets for the common cases.

Add explicit validation checkpoints to the Response Approach workflow (e.g. 'Validate data quality before writing to production sinks; halt and remediate on failure') to lift workflow clarity past the batch-operation cap.

Dimension	Reasoning	Score
Conciseness	The Capabilities section is a multi-hundred-line catalog of widely-known tools (Snowflake, BigQuery, Kafka, Spark, etc.) grouped into categories, and 'Purpose'/'Knowledge Base' restate the same material; noticeably verbose with padded sections Claude already knows. Not a 3 because the padding is pervasive rather than occasional.	2 / 5
Actionability	Instructions are high-level hints ('Define sources, SLAs, and data contracts', 'Choose architecture, storage, and orchestration tools') with no concrete commands, code, or specific procedures to execute. Not a 3 because even for an instruction-only skill the guidance stays abstract rather than giving specific, actionable direction.	2 / 5
Workflow Clarity	The Response Approach provides an 8-step sequenced methodology, but there are no validation checkpoints or feedback loops for batch pipeline operations, which per the rubric caps this at 3. Not a 2 because a clear sequence does exist.	3 / 5
Progressive Disclosure	The body is well-sectioned with clear headers, but all 200+ lines are inline in one file with no bundle files (references/scripts/assets absent) and the capabilities catalog is content that would belong in a separate reference file. Not a 4 because no references or navigation exist, and not a 2 because section structure is genuinely organized.	3 / 5
	Total	10 / 20 Passed

Description

66%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 clearly communicates what the skill does and names specific tools, but it omits any 'Use when...' trigger clause, which caps completeness at 3. It is concise and reasonably specific but would benefit from explicit usage triggers and broader keyword coverage.

Suggestions

Add a 'Use when...' clause naming concrete triggers, e.g. 'Use when designing batch or streaming data pipelines, building data warehouses/lakehouses, or orchestrating dbt/Airflow workflows.'

Broaden trigger-term coverage to include synonyms users naturally say: 'ETL/ELT', 'data engineering', 'data lakehouse', 'data orchestration'.

Add file-extension or format cues where relevant (e.g., '.sql', 'DAGs') to sharpen distinctiveness.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('Build scalable data pipelines, modern data warehouses, and real-time streaming architectures') plus specific tools ('Implements Apache Spark, dbt, Airflow, and cloud-native data platforms'), with only minor coverage gaps. Not a 5 because the named actions are broad categories rather than granular concrete operations.	4 / 5
Completeness	The 'what' is clearly stated (build pipelines/warehouses/streaming, implement Spark/dbt/Airflow/cloud platforms) but there is no 'Use when...' or equivalent explicit trigger guidance, which per the rubric caps completeness at 3; not a 2 because the 'what' is concrete rather than vague.	3 / 5
Trigger Term Quality	Includes natural terms users would say ('data pipelines', 'data warehouses', 'streaming architectures', 'Spark', 'dbt', 'Airflow') with good coverage; not a 5 because common variations like 'ETL/ELT', 'data engineering', and 'data lakehouse' are missing.	4 / 5
Distinctiveness Conflict Risk	A distinct data-engineering niche with specific tool triggers (Spark, dbt, Airflow) that mostly separate it from adjacent skills; not a 5 because the broad 'cloud-native data platforms' framing leaves minor overlap with general data/analytics skills.	4 / 5
	Total	15 / 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/data-engineer/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

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