airflow-dag-generator
Airflow Dag Generator - Auto-activating skill for Data Pipelines. Triggers on: airflow dag generator, airflow dag generator Part of the Data Pipelines skill category.
35
3%
Does it follow best practices?
Impact
94%
0.96xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/11-data-pipelines/airflow-dag-generator/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is severely underdeveloped, essentially just restating the skill name without explaining capabilities or providing meaningful selection guidance. It lacks concrete actions, natural trigger terms, and explicit 'when to use' criteria. Claude would struggle to appropriately select this skill from a larger skill library.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Generates Apache Airflow DAG Python files with task dependencies, scheduling configuration, and operator definitions'
Include a 'Use when...' clause with natural trigger terms like 'create a DAG', 'Airflow pipeline', 'schedule data workflow', 'ETL automation', 'task orchestration'
Add common file types or outputs (e.g., '.py DAG files', 'airflow configuration') to help distinguish from other pipeline tools
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the tool ('Airflow Dag Generator') and category ('Data Pipelines') without describing any concrete actions. No specific capabilities like 'creates DAG files', 'configures task dependencies', or 'generates Python code' are mentioned. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and the 'when' guidance is just a duplicate trigger phrase. There is no explicit 'Use when...' clause or meaningful trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'airflow dag generator' repeated twice - no natural variations users might say like 'create a DAG', 'pipeline workflow', 'schedule tasks', 'ETL job', or 'data pipeline automation'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'Airflow' is a specific technology that provides some distinctiveness, the generic 'Data Pipelines' category and lack of specific use cases could cause overlap with other pipeline or workflow automation skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a placeholder template with no actual instructional content. It contains only generic descriptions of what the skill claims to do without any concrete guidance, code examples, DAG templates, or Airflow-specific patterns. The content would be completely unhelpful for actually generating Airflow DAGs.
Suggestions
Add executable Python code examples showing a complete, minimal Airflow DAG with common operators (PythonOperator, BashOperator, etc.)
Include a concrete workflow: 1) Define DAG parameters, 2) Create tasks, 3) Set dependencies, 4) Validate with `airflow dags test`
Provide specific patterns for common use cases (ETL pipeline, sensor-triggered workflows, dynamic DAG generation) with copy-paste ready templates
Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with actual Airflow-specific guidance
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actionable information. | 1 / 3 |
Actionability | There is zero concrete guidance - no code examples, no DAG templates, no specific Airflow operators or patterns. The skill describes what it could do rather than instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is defined. There are no steps for creating a DAG, no validation checkpoints, and no sequence of operations. The content is entirely abstract with no process guidance. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no references to detailed materials, no links to examples, templates, or advanced documentation. There's nothing to progressively disclose. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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