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:sickn33/antigravity-awesome-skills --skill airflow-dag-patterns75
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 clearly specifies the technology (Apache Airflow), lists concrete capabilities (DAGs, operators, sensors, testing, deployment), and provides explicit 'Use when' triggers with natural language terms users would actually say. The description is concise, uses third person voice, and creates a distinct niche.
| 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
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 any concrete code examples, specific Airflow patterns, or executable commands. The heavy reliance on an external playbook file means the SKILL.md itself provides minimal standalone value.
Suggestions
Add at least one concrete, executable DAG example showing a basic pattern (e.g., a simple ETL DAG with proper retry configuration)
Replace abstract instructions like 'Design idempotent tasks' with specific patterns or code snippets demonstrating idempotency in Airflow
Include a quick-start section with copy-paste ready code for common operations (creating a DAG, defining task dependencies, setting up alerts)
Add validation commands or testing steps (e.g., 'airflow dags test dag_id execution_date') to improve workflow clarity
| 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 without padding. | 3 / 3 |
Actionability | The instructions are vague and abstract ('Identify data sources', 'Design idempotent tasks') with no concrete code examples, specific commands, or executable guidance. 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 be actionable. | 2 / 3 |
Progressive Disclosure | References to external resources exist and are one level deep, but the SKILL.md itself provides almost no substantive content - it's essentially just a pointer to another file without a useful quick-start or concrete examples. | 2 / 3 |
Total | 8 / 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 |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Reviewed
Table of Contents
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