airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Install with Tessl CLI
npx tessl i github:wshobson/agents --skill airflow-dag-patterns82
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillAgent success when using this skill
Validation for skill structure
Discovery
100%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 is a strong skill description that follows best practices. It uses third person voice, lists specific capabilities within the Airflow ecosystem, includes a clear 'Use when...' clause with natural trigger terms, and is distinctive enough to avoid conflicts with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Build production Apache Airflow DAGs', 'operators', 'sensors', 'testing', and 'deployment'. These are concrete, actionable capabilities within the Airflow domain. | 3 / 3 |
Completeness | Clearly answers both what ('Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment') AND when ('Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'Airflow', 'DAGs', 'data pipelines', 'orchestrating workflows', 'scheduling batch jobs'. These cover common variations of how users describe workflow orchestration needs. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with 'Apache Airflow' and 'DAGs' as clear differentiators. Unlikely to conflict with general coding skills or other workflow tools due to specific technology naming. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable Airflow skill with excellent code examples covering major patterns. However, it's overly long for a SKILL.md file, includes some explanatory content Claude doesn't need, and lacks an explicit development/deployment workflow with validation checkpoints. The content would benefit from being split into a concise overview with references to detailed pattern files.
Suggestions
Remove the 'When to Use This Skill' and 'Core Concepts' sections - Claude understands these from context and the code examples demonstrate the concepts better
Add an explicit DAG development workflow with validation steps: 'Write DAG → Run pytest → Validate DAG loads → Deploy to staging → Test execution → Deploy to production'
Split patterns into separate files (e.g., PATTERNS.md, TESTING.md) and keep SKILL.md as a concise quick-start with references
Remove the external Resources links - Claude can find documentation; focus on project-specific patterns and conventions
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary verbosity. The 'Core Concepts' table explains principles Claude already knows (idempotent, atomic), and the 'When to Use This Skill' section is redundant given the skill's purpose is clear from context. | 2 / 3 |
Actionability | Excellent executable code examples throughout - all patterns include complete, copy-paste ready Python code with proper imports, realistic configurations, and practical implementations covering DAGs, TaskFlow API, sensors, testing, and error handling. | 3 / 3 |
Workflow Clarity | While individual patterns are clear, there's no explicit validation workflow for DAG development (e.g., 'run tests before deploying', 'validate DAG loads before pushing'). The testing section exists but isn't integrated into a deployment workflow with checkpoints. | 2 / 3 |
Progressive Disclosure | Content is well-organized with clear sections, but it's a monolithic 400+ line file. The project structure and patterns could be split into separate reference files, with SKILL.md serving as a concise overview pointing to detailed pattern files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (526 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Reviewed
Table of Contents
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