preprocessing-data-with-automated-pipelines

Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.

Quality

62%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./bundled/skills/preprocessing-data-with-automated-pipelines/SKILL.md

Quality

Content

65%

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

The body is a clean, well-structured positioning overview, but it functions as meta-guidance rather than executable instruction and fails to wire its existing bundle scripts and references into the navigation.

Suggestions

Link the existing bundle scripts inline (e.g., 'See scripts/pipeline.py for the reference implementation' and scripts/validate_data.py for the validation step) so Claude can reach executable detail.

Add an explicit validation checkpoint to the workflow (run validate_data.py after transform, fix and re-validate before proceeding) to satisfy the feedback-loop requirement for batch operations.

Replace the unchecked placeholder list in references/README.md with real referenced files that the body actually points to, or remove the orphaned references.

Dimension	Reasoning	Score
Conciseness	The body is a lean overview of positioning, when-to-use, boundaries, and outputs with no padding of concepts Claude already knows, matching the 'lean and efficient; every token earns its place' anchor.	3 / 3
Actionability	It gives some concrete shape (output deliverables, a clean/encode/transform/validate sequence) but provides no executable steps and never links to the bundle scripts that exist, falling short of fully actionable guidance.	2 / 3
Workflow Clarity	A loose clean→encode→transform→validate sequence is implied in 'Typical Outputs', but there are no explicit validation checkpoints or feedback loops, which caps workflow clarity at 2 for batch preprocessing operations.	2 / 3
Progressive Disclosure	Sections are well-organized, but the body references none of the existing bundle files (scripts/pipeline.py, validate_data.py, etc.) and references/README.md lists only unchecked, non-existent docs, so navigation to detailed materials is missing.	2 / 3
	Total	9 / 12 Passed

Description

60%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and action-oriented but stops short of providing explicit trigger guidance, capping completeness. It is a solid, concise capability statement rather than a fully self-describing trigger.

Suggestions

Add a 'Use when…' clause naming natural user triggers (e.g., 'Use when preparing, cleaning, or encoding ML training data, or building an ETL preprocessing pipeline').

Include common user phrasings like 'data cleaning', 'feature encoding', and 'ETL' alongside the technical terms to broaden trigger coverage.

Dimension	Reasoning	Score
Specificity	The description enumerates multiple concrete actions — 'design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating' — matching the anchor that lists several specific concrete actions.	3 / 3
Completeness	It clearly states what the skill does but lacks any 'Use when…' trigger clause, so per the judging guidelines completeness is capped at 2.	2 / 3
Trigger Term Quality	Terms like 'preprocessing pipelines' and 'cleaning, encoding, transforming' are somewhat natural but lean technical and omit common variations a user might say ('data cleaning', 'ETL', 'feature encoding'), matching the 'some relevant keywords but missing common variations' anchor.	2 / 3
Distinctiveness Conflict Risk	The 'ML input data' preprocessing niche is reasonably specific, but the description alone could still overlap with sibling ML pipeline skills, matching the 'somewhat specific but could still overlap' anchor rather than the clearly-distinct level 3.	2 / 3
	Total	9 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/preprocessing-data-with-automated-pipelines/SKILL.md
Commit: f627ab5

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.