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 extremely thin and template-like, providing almost no useful information beyond the skill name. It lacks concrete actions, natural trigger terms, and any explicit 'when to use' guidance. It would be very difficult for Claude to confidently select this skill from a pool of available skills based on this description alone.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Generates Apache Airflow DAG Python files with task dependencies, scheduling configuration, and operator definitions'.
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to create an Airflow DAG, build a data pipeline, set up workflow orchestration, or generate ETL workflows.'
Include natural keyword variations users might say: 'Airflow', 'DAG', 'data pipeline', 'ETL', 'workflow', 'task scheduling', 'Apache Airflow', '.py DAG file'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('Airflow DAG') but does not describe any concrete actions. There are no specific capabilities listed like 'generates DAG files', 'configures task dependencies', or 'sets scheduling parameters'. It only says 'Auto-activating skill for Data Pipelines' which is vague. | 1 / 3 |
Completeness | The 'what' is extremely weak — it only implies it generates Airflow DAGs without describing what that entails. The 'when' is missing entirely; there is no 'Use when...' clause or equivalent explicit trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'airflow dag generator' repeated twice. Missing natural variations users would say like 'create a DAG', 'data pipeline', 'Apache Airflow', 'workflow orchestration', 'ETL pipeline', 'task scheduling', or 'DAG file'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Airflow DAG' does narrow the domain somewhat, making it unlikely to conflict with non-pipeline skills. However, 'Data Pipelines' is broad enough to overlap with other ETL or pipeline-related skills, and the lack of specific actions reduces distinctiveness. | 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 essentially a placeholder with no actionable content. It repeatedly describes itself in abstract terms without providing any actual Airflow DAG generation guidance, code examples, templates, or workflows. It fails on every dimension because it contains no substantive technical content whatsoever.
Suggestions
Add a concrete, executable Airflow DAG template (e.g., a complete Python file with DAG definition, default_args, and at least one operator) that Claude can use as a starting point.
Define a clear workflow: 1) Gather requirements (schedule, data sources, dependencies), 2) Generate DAG skeleton, 3) Add operators/tasks, 4) Validate with `python dag_file.py` to check for import errors, 5) Test with `airflow dags test`.
Include specific examples of common DAG patterns (e.g., ETL with BashOperator/PythonOperator, sensor-triggered pipelines, dynamic task generation) with copy-paste-ready code.
Remove all the meta-description sections ('When to Use', 'Example Triggers', 'Capabilities') and replace them with actual technical content—best practices for DAG design, common pitfalls, and operator selection guidance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague information about 'airflow dag generator' without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code examples, no DAG templates, no specific Airflow operators, no commands. The skill describes rather than instructs, offering only vague promises like 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps, no sequencing, no validation checkpoints. The content merely states it can provide guidance without actually laying out any process for generating an Airflow DAG. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive document with no meaningful structure. There are no references to detailed files, no layered organization, and the sections are redundant rather than progressively informative. | 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 | |
- Commit
4dee593
- Reviewed
Table of Contents
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