data-engineering

Transforms, validates, loads data in ETL pipelines. Use when building scrapers, validating NDJSON feeds, or importing data into CMS/DB targets.

Quality

96%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

92%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a strong, well-structured skill that provides concrete, executable guidance for data engineering pipelines. The workflow section is exemplary with explicit checkpoints and recovery steps. The only minor weakness is that REFERENCE.md is referenced but not provided in the bundle, making it impossible to verify the progressive disclosure chain.

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 information without padding.	3 / 3
Actionability	Provides executable bash commands for the full pipeline, a complete inline Zod-based NDJSON validator in JavaScript, specific configuration values (retryLimit, timeout), and a concrete schema table. All guidance is copy-paste ready.	3 / 3
Workflow Clarity	The 5-step workflow is clearly numbered with explicit checkpoints at each stage, recovery actions for failures, a backup step before production import, and tolerance thresholds (±5%). The feedback loops for error recovery are well-defined.	3 / 3
Progressive Disclosure	References to REFERENCE.md are well-signaled and one level deep, but no bundle files were provided to verify the reference exists. The inline content is appropriately scoped as an overview, though the NDJSON validator could arguably live in a referenced file rather than inline.	2 / 3
	Total	11 / 12 Passed

Description

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 covers specific capabilities, includes an explicit 'Use when' clause with natural trigger terms, and occupies a distinct niche. The description uses proper third-person voice and avoids vague language. The only minor consideration is that it could potentially list a couple more trigger variations (e.g., 'data pipeline', 'extract transform load'), but the current coverage is solid.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'transforms, validates, loads data in ETL pipelines' plus mentions scrapers, NDJSON feeds, and importing 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 building scrapers, validating NDJSON feeds, or importing data into CMS/DB targets).	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'ETL', 'scrapers', 'NDJSON', 'feeds', 'CMS', 'DB', 'importing data', 'validating'. These cover common terms a user working with data pipelines would naturally use.	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' are highly specific and unlikely to conflict with general data processing or document skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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: 7a69a05

Reviewed: about 1 month ago

Table of Contents

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