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.
83
75%
Does it follow best practices?
Impact
96%
1.01xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/airflow-dag-patterns/SKILL.mdQuality
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 well-crafted skill description that excels across all dimensions. It provides specific capabilities (DAGs, operators, sensors, testing, deployment), includes natural trigger terms users would actually say, explicitly states when to use it, and is clearly distinguishable from other skills through its Airflow-specific focus.
| 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 focus. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is concise but lacks actionability - it reads more like a table of contents than a skill file. The instructions are abstract guidance without concrete code examples, DAG templates, or specific commands. The heavy reliance on an external playbook file means the skill body itself provides minimal standalone value.
Suggestions
Add at least one complete, executable DAG example showing the recommended patterns (e.g., a simple ETL DAG with proper error handling and retries)
Replace abstract instructions like 'Design idempotent tasks' with concrete guidance: specific code patterns, configuration snippets, or command examples
Include a quick-start section with copy-paste ready code that demonstrates core concepts before pointing to the detailed playbook
Add validation commands or checks (e.g., 'airflow dags test', 'airflow tasks test') as explicit workflow checkpoints
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, avoiding unnecessary explanations of what Airflow is or how DAGs work. Every section serves a purpose and assumes Claude's competence with the technology. | 3 / 3 |
Actionability | The instructions are vague and abstract ('Identify data sources', 'Design idempotent tasks') with no concrete code examples, executable commands, or copy-paste ready DAG templates. It describes rather than instructs. | 1 / 3 |
Workflow Clarity | Steps are listed in sequence (identify, design, implement, validate) but lack validation checkpoints, specific commands, or feedback loops for error recovery. The workflow is too high-level to guide actual implementation. | 2 / 3 |
Progressive Disclosure | References to implementation-playbook.md are present and one-level deep, but the SKILL.md itself provides almost no actionable content - it's essentially just a pointer to another file with minimal overview value. | 2 / 3 |
Total | 8 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- haniakrim21/everything-claude-code
- Commit
ae2cadd
- Reviewed
Table of Contents
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