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 essentially a restated title with no substantive content. It fails to describe what the skill actually does, provides no natural trigger terms beyond the skill name repeated, and lacks any 'Use when...' guidance. It would be nearly indistinguishable from other data pipeline skills in a multi-skill environment.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Generates Apache Airflow DAG Python files, configures task dependencies, sets scheduling intervals, and defines operators for ETL workflows.'
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, define workflow orchestration, or generate ETL task graphs.'
Include natural keyword variations users would say: 'Airflow', 'DAG', 'pipeline', 'workflow', 'ETL', 'task scheduling', 'Apache Airflow', 'data orchestration', '.py DAG file'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('Airflow DAG') and category ('Data Pipelines') but does not describe any concrete actions. There are no specific capabilities listed like 'generates DAG files', 'configures task dependencies', or 'sets scheduling parameters'. | 1 / 3 |
Completeness | The 'what' is extremely vague (just the skill name restated) and the 'when' is missing entirely. There is no explicit 'Use when...' clause or equivalent guidance for when Claude should select this skill. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'airflow dag generator' repeated twice. Missing natural variations users would say like 'DAG', 'pipeline', 'workflow', 'Apache Airflow', 'task scheduling', 'ETL', or 'data pipeline orchestration'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Airflow DAG' provides some specificity that distinguishes it from generic data pipeline skills, but the lack of concrete actions and the broad 'Data Pipelines' category label could cause overlap with other pipeline-related 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 an empty placeholder with no actual instructional content. It repeatedly references 'airflow dag generator' without providing any concrete guidance, code examples, DAG templates, or workflow steps. It fails on every dimension as it contains only meta-description and no actionable substance.
Suggestions
Add a concrete, executable example of a minimal Airflow DAG (e.g., a Python file with DAG definition, default_args, and at least one operator) that Claude can use as a template.
Define a clear workflow: 1) Gather requirements (schedule, dependencies, data sources), 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`.
Remove all the meta-description sections ('Purpose', 'When to Use', 'Example Triggers') that provide no technical value and replace with actionable content like common operator patterns (BashOperator, PythonOperator, sensor patterns) and best practices (idempotency, XCom usage, connection management).
Add references to advanced topics like dynamic DAG generation, TaskFlow API, custom operators, and testing strategies, either inline or via linked files.
| 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, no configuration snippets. The content only describes rather than instructs, offering nothing executable or copy-paste ready. | 1 / 3 |
Workflow Clarity | No workflow steps are defined. Claims to provide 'step-by-step guidance' but none is actually present. There are no sequences, validation checkpoints, or any process description for generating an Airflow DAG. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive structure with no references to detailed materials, no links to examples or advanced topics, and no meaningful organization beyond boilerplate section headers. | 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
3e83543
- Reviewed
Table of Contents
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