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data-engineering

数据工程。Airflow、Dagster、Kafka Streams、Flink、dbt、数据管道、流处理、数据质量。当用户提到数据管道、ETL、流处理、数据质量时路由到此。

61

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./templates/skills/domains/data-engineering/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

68%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 dense, well-structured domain reference with concrete patterns and useful checklists, scoring well on conciseness, actionability, and organization. Its main weakness is the absence of explicit sequenced workflows with validation feedback loops, since it is reference material rather than a procedural guide.

Suggestions

Add at least one explicit end-to-end workflow with validate→fix→retry feedback (e.g., build a dbt model → run dbt test --store-failures → fix and re-run) to raise workflow clarity.

Promote fragmentary API patterns into one or two complete, copy-paste-ready examples per framework to push actionability to 5.

Consider splitting the per-framework deep-dive patterns into reference files (e.g., references/airflow.md) linked from the overview to improve progressive disclosure for the larger sections.

DimensionReasoningScore

Conciseness

A lean bullet/table/checklist reference that assumes Claude's competence and avoids explaining concepts, though its encyclopedic breadth adds tokens that are not all strictly necessary.

4 / 5

Actionability

Provides concrete API patterns and config snippets (DAG definitions, Great Expectations calls, Soda YAML), but many are fragmentary patterns rather than complete copy-paste-ready scripts.

4 / 5

Workflow Clarity

The content is reference-oriented rather than a sequenced workflow; the per-domain checklists serve as validation aids but lack explicit validate→fix→retry feedback loops.

3 / 5

Progressive Disclosure

Self-contained with well-organized section headers, comparison tables, and checklists; the large per-framework reference could arguably be split into separate files but is reasonably structured inline.

4 / 5

Total

15

/

20

Passed

Description

73%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 establishes a clear, distinctive niche with explicit trigger routing and good keyword coverage, but describes technologies as nouns rather than concrete actions. Strengthening the 'what' with verbs would lift specificity and completeness.

Suggestions

Rewrite the 'what' clause as concrete actions (e.g., '编排数据管道、处理实时流、校验数据质量') instead of a noun/keyword list to raise specificity.

Add a few common synonyms/variants (数据仓库、CDC、实时计算) to the trigger list for fuller keyword coverage.

Keep the explicit '当用户提到…时路由到此' trigger clause — it is the strongest part and should be preserved.

DimensionReasoningScore

Specificity

Names the domain and several specific frameworks (Airflow, Dagster, Kafka Streams, Flink, dbt), but these are technologies/nouns rather than concrete actions, so it sits at 'names domain and items but not comprehensive actions'.

3 / 5

Completeness

Both 'what' (domain + tool list) and an explicit 'when' ('当用户提到…时路由到此') are present, but the 'what' is a keyword enumeration rather than a clear action statement.

4 / 5

Trigger Term Quality

Includes natural user phrases (数据管道、ETL、流处理、数据质量) and explicit tool names, but misses some common synonyms like 数据仓库 or CDC.

4 / 5

Distinctiveness Conflict Risk

A clear data-engineering niche anchored by named frameworks with distinct triggers, giving minimal conflict risk with other skills.

5 / 5

Total

16

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
fengshao1227/ccg-workflow
Reviewed

Table of Contents

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