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.
79
Quality
78%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agent/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 follows best practices. It uses third person voice, provides specific capabilities (operators, sensors, testing, deployment), includes an explicit 'Use when...' clause with natural trigger terms, and clearly distinguishes itself through Airflow-specific terminology.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Build production Apache Airflow DAGs' with explicit mention of 'operators, sensors, testing, and deployment' - these are concrete, actionable capabilities. | 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 | Clear niche focused specifically on Apache Airflow with distinct triggers like 'DAGs', 'Airflow', 'orchestrating workflows' - unlikely to conflict with general coding or other data tools skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is well-structured and concise with good progressive disclosure to detailed resources. However, it critically lacks actionable content - there are no code examples, specific commands, or concrete patterns in the main skill file. The instructions read as abstract guidance rather than executable steps Claude can follow.
Suggestions
Add at least one concrete, executable DAG example showing the recommended patterns (e.g., a minimal production-ready DAG with proper error handling)
Replace abstract instructions like 'Design idempotent tasks' with specific code patterns or templates that demonstrate idempotency
Include specific Airflow CLI commands for common operations (testing, backfills, debugging) rather than just mentioning them conceptually
Add validation checkpoints to the workflow, such as 'Run `airflow dags test dag_id execution_date` before deploying'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, avoiding unnecessary explanations of what Airflow is or how orchestration works. 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 | Clear overview structure with well-signaled one-level-deep references to implementation-playbook.md. Content is appropriately split between overview and detailed resources. | 3 / 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 |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- Dokhacgiakhoa/antigravity-ide
- Commit
3395991
- Reviewed
Table of Contents
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