CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

data-engineer

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

38

Quality

48%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/antigravity-bundle-data-engineering/skills/data-engineer/SKILL.md

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

SKILL.md
Quality
Evals
Security

Quality

Content

10%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 a persona-style capability catalogue rather than actionable skill guidance: it pads the context window with tool lists Claude already knows while providing no executable steps, no validation checkpoints, and no reference structure. It needs to be replaced with concise, task-specific instructions (with code/commands where relevant) and offload capability catalogues to reference files.

Suggestions

Cut the 'Capabilities', 'Knowledge Base', and 'Behavioral Traits' catalogues — Claude already knows what Spark, Airflow, dbt, and Kafka are; keep only non-obvious guidance (e.g. project-specific conventions, chosen stack, naming standards).

Replace the abstract 'Instructions'/'Response Approach' lists with concrete, executable guidance: example pipeline code, dbt model skeletons, or specific design checklists for the common request types listed in 'Example Interactions'.

If detailed capability/per-platform detail must be kept, move it into a references/ file (one level deep, clearly linked from SKILL.md) instead of inlining ~180 lines in the main file, and add explicit validation checkpoints (e.g. 'run data quality checks before loading to production') to the workflow steps.

DimensionReasoningScore

Conciseness

Roughly 180 of ~215 lines are tool catalogues ('Relational databases: PostgreSQL, MySQL, SQL Server integration', 'Data lakes: AWS S3, Azure Data Lake, Google Cloud Storage...') enumerating technologies and capabilities Claude already knows. This is severely padded content that adds almost nothing Claude does not already know, matching the lowest anchor.

1 / 5

Actionability

The instructions ('Define sources, SLAs, and data contracts', 'Choose architecture, storage, and orchestration tools') are entirely abstract with no code, commands, or executable steps — the body only describes rather than instructs, matching the 'entirely vague or abstract' anchor.

1 / 5

Workflow Clarity

Two step lists exist ('Instructions' 1–4, 'Response Approach' 1–8) but the steps are loosely defined and validation appears only as a Safety principle ('Validate data before writing to production sinks'), never as a checkpoint in the actual sequence. Pipeline builds writing to production sinks are batch operations, so missing feedback loops cap this at 3; the rough, gap-filled sequence lands it at 2.

2 / 5

Progressive Disclosure

There are no bundle files at all, and ~180 lines of capability/reference-style catalogue content that clearly belongs in separate files are inlined in SKILL.md. Section headers exist (so not a 1), but the structure is minimal and nothing is split out, matching the 'content that clearly belongs in separate files is inlined' anchor.

2 / 5

Total

6

/

20

Passed

Description

62%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.

A moderately strong description with specific tools and deliverables, but it lacks any explicit 'when to use' trigger clause, capping its completeness. Adding a 'Use when...' sentence with natural trigger terms (ETL, data engineering, orchestration) would materially improve it.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when the user asks to build ETL/ELT pipelines, data warehouses, or streaming data platforms.'

Include common user-facing synonyms such as 'ETL', 'ELT', 'data engineering', and 'orchestration' alongside the tool names.

State what the skill actually does with the named tools (e.g. 'writes Spark jobs and dbt models') rather than just 'Implements Apache Spark, dbt, Airflow'.

DimensionReasoningScore

Specificity

The description names three concrete deliverables ('scalable data pipelines, modern data warehouses, and real-time streaming architectures') and specific tools ('Apache Spark, dbt, Airflow'), matching the anchor for several specific actions with minor gaps. It is not a 5 because the tool names come without accompanying actions and coverage omits data quality/governance work the skill body claims.

4 / 5

Completeness

It clearly answers 'what' (builds pipelines, warehouses, streaming architectures; implements Spark, dbt, Airflow) but contains no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Terms like 'data pipelines', 'data warehouses', 'streaming', 'Spark', 'dbt', and 'Airflow' are natural phrases users would say, giving good keyword coverage. It falls short of comprehensive because common variations such as 'ETL/ELT', 'data engineering', and 'orchestration' are missing.

4 / 5

Distinctiveness Conflict Risk

The domain is somewhat specific (data engineering tooling) but the breadth ('cloud-native data platforms') creates overlap risk with data-analysis, ML-platform, and general cloud-infrastructure skills, matching the 'somewhat specific but could still overlap' anchor.

3 / 5

Total

14

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

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/antigravity-bundle-data-engineering/skills/data-engineer/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.