data-engineering
Transforms, validates, and loads data in ETL pipelines. Use when building scrapers, validating NDJSON feeds, or importing data into CMS/DB targets.
100
100%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
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, well-crafted description that concisely communicates specific capabilities and explicit trigger conditions. It uses third person voice correctly, includes domain-specific trigger terms (ETL, NDJSON, CMS/DB), and clearly delineates both what the skill does and when to use it. The description is concise without being vague.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'transforms', 'validates', 'loads data', 'building scrapers', 'validating NDJSON feeds', 'importing data into CMS/DB targets'. These are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (transforms, validates, loads data in ETL pipelines) and 'when' (explicit 'Use when' clause covering scrapers, NDJSON feeds, and CMS/DB imports). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'ETL', 'scrapers', 'NDJSON', 'feeds', 'CMS', 'DB', 'importing data', 'pipelines'. These cover both technical terms and natural language variations relevant to the domain. | 3 / 3 |
Distinctiveness Conflict Risk | The combination of ETL pipelines, NDJSON feeds, scrapers, and CMS/DB targets creates a clear niche. Terms like 'NDJSON' and 'ETL pipelines' are highly specific and unlikely to conflict with general data processing or database skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an excellent skill file that balances conciseness with completeness. It provides executable code, a well-structured multi-step workflow with validation checkpoints and recovery paths, and appropriately delegates detailed references to a separate file. The content respects Claude's intelligence while providing the specific, non-obvious configuration values and patterns needed for data engineering pipelines.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It doesn't explain what NDJSON is, what ETL means, or how headless browsers work—it assumes Claude knows these concepts. Every section delivers specific, actionable configuration values or code. | 3 / 3 |
Actionability | Provides fully executable bash commands for the pipeline, a copy-paste-ready Zod-based NDJSON validator in JS, specific configuration values (retryLimit: 3, retryDelay: 5000, timeout: 30000), and a concrete schema table. Guidance is specific and directly usable. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly numbered with explicit validation checkpoints at each stage, recovery actions for failures, and includes a backup step before destructive import. The feedback loops (fix selectors → re-run, revert staging → adjust dedupe) are well-defined. | 3 / 3 |
Progressive Disclosure | The SKILL.md serves as a clear overview with quick-start content inline, while pointing to REFERENCE.md for full scraper code, extended validators, and project-specific schemas. References are one level deep and clearly signaled. | 3 / 3 |
Total | 12 / 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
- monkilabs/opencastle
- Commit
f5c8508
- Reviewed
Table of Contents
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