airflow-operator-creator
Airflow Operator Creator - Auto-activating skill for Data Pipelines. Triggers on: airflow operator creator, airflow operator creator Part of the Data Pipelines skill category.
36
3%
Does it follow best practices?
Impact
96%
1.02xAverage 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-operator-creator/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 title and category label with no substantive content. It fails to describe concrete capabilities, lacks natural trigger terms users would use, and provides no guidance on when Claude should select this skill. It would be nearly indistinguishable from any other Airflow-related skill.
Suggestions
Add concrete actions the skill performs, e.g., 'Generates custom Apache Airflow operators, sensors, and hooks with proper base class inheritance, template fields, and execute methods.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to create a custom Airflow operator, build a sensor, write a hook, or scaffold DAG task components.'
Include common keyword variations users might say, such as 'Apache Airflow', 'custom operator', 'DAG task', 'sensor class', 'BaseOperator', 'data pipeline component'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('Airflow Operator') and a vague action ('Creator') but does not describe any concrete actions like 'generates custom operator classes', 'scaffolds sensor definitions', or 'creates DAG task templates'. It is essentially just a title repeated. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the title and completely lacks a 'when should Claude use it' clause. There is no explicit trigger guidance or use-case description. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'airflow operator creator' repeated twice. It lacks natural user keywords like 'custom operator', 'Airflow plugin', 'DAG', 'sensor', 'hook', 'data pipeline task', or 'Apache Airflow'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Airflow Operator' provides some specificity to a niche domain, which reduces conflict with generic skills. However, the vague 'Data Pipelines' category and lack of concrete scope could still 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 actionable content whatsoever. It contains only generic boilerplate describing what the skill theoretically does without providing any actual guidance on creating Airflow operators—no code, no workflows, no examples, no references. It would provide zero value to Claude when attempting to help a user create a custom Airflow operator.
Suggestions
Add a concrete, executable code example showing how to create a custom Airflow operator by subclassing BaseOperator, implementing the execute() method, and defining the template_fields.
Define a clear multi-step workflow: 1) Create operator class, 2) Define parameters and template fields, 3) Implement execute(), 4) Write unit tests, 5) Register in plugin or DAG—with validation at each step.
Remove all meta-content about when the skill activates and what it can do; replace with actual technical guidance, patterns, and best practices specific to Airflow operator development.
Add concrete examples of common operator patterns (e.g., API-to-database operator, sensor operator, transfer operator) with copy-paste-ready code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, provides no specific technical content about creating Airflow operators, and pads extensively with meta-descriptions of when the skill activates and what it can do. | 1 / 3 |
Actionability | There is zero concrete guidance—no code examples, no specific commands, no operator templates, no API references. Every section describes what the skill could do rather than providing executable instructions for creating Airflow operators. | 1 / 3 |
Workflow Clarity | No workflow steps are defined at all. Creating a custom Airflow operator involves multiple steps (subclassing BaseOperator, implementing execute(), registering, testing) and none are mentioned, let alone sequenced with validation checkpoints. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no references to supporting files, no structured navigation, and no bundle files to support it. There is nothing to progressively disclose because there is no substantive content. | 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
172d892
- Reviewed
Table of Contents
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