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aps-doc-golden

Expert documentation generation for golden layers. Detects SCD types, documents business rules, metric definitions, aggregation logic, and data quality scoring. Use when documenting golden layer tables.

48

Quality

50%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Risky

Do not use without reviewing

Optimize this skill with Tessl

npx tessl skill review --optimize ./aps-doc-skills/golden/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

14%

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

This skill suffers from extreme verbosity, embedding hundreds of lines of template scaffolding inline rather than referencing separate template files. While it provides useful structural guidance for golden layer documentation, it lacks a clear workflow for the actual analysis process and has no validation steps to ensure extracted information is accurate. The content would benefit enormously from being restructured into a concise SKILL.md overview with templates and examples in separate bundle files.

Suggestions

Extract the massive template sections (parent page, attributes page, behaviors page) into separate bundle files (e.g., TEMPLATE_PARENT.md, TEMPLATE_ATTRIBUTES.md, TEMPLATE_BEHAVIORS.md) and reference them from a concise overview in SKILL.md.

Add a clear numbered workflow for the analysis process: 1) Scan .dig files to discover tables, 2) Parse SQL files to extract attributes/logic, 3) Categorize into attribute vs behavior tables, 4) Validate extracted data against actual schema, 5) Generate documentation from templates.

Include concrete, executable code or tool-use examples for the discovery phase (e.g., specific Glob patterns to find .dig and .sql files, regex patterns or parsing approaches to extract COALESCE patterns and CASE statements).

Add validation checkpoints such as 'verify extracted attribute count matches SQL SELECT columns' or 'confirm all referenced source tables exist in enriched layer' before generating final documentation.

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~300+ lines, mostly consisting of massive template scaffolding that Claude could generate from a brief description. It explains obvious concepts (what golden layers are, what atomic replacement means, what MDM is) and includes extensive markdown templates that are essentially boilerplate rather than actionable guidance.

1 / 3

Actionability

The skill provides concrete template structures and specifies that real SQL/code must be extracted from the codebase, which is useful. However, it lacks executable code for the actual discovery process (e.g., how to parse .dig files, how to extract attributes from SQL), relying instead on vague instructions like 'Dynamically discover all attribute and behavior tables from actual code' without showing how.

2 / 3

Workflow Clarity

There is no clear multi-step workflow for the documentation generation process itself. The skill jumps from 'get codebase access' directly to massive templates without sequencing the analysis steps (e.g., first scan .dig files, then parse SQL, then categorize tables, then generate docs). No validation checkpoints exist for verifying extracted information accuracy before generating documentation.

1 / 3

Progressive Disclosure

The entire skill is a monolithic wall of text with massive inline templates that should be in separate reference files. The template sections alone (parent page, attributes page, behaviors page, child pages) could each be their own referenced file, dramatically improving readability. No bundle files are provided despite the clear need for them.

1 / 3

Total

5

/

12

Passed

Description

85%

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-structured description that clearly defines specific capabilities and includes an explicit 'Use when' trigger clause. Its main weakness is that the trigger terms are heavily domain-specific jargon ('golden layer', 'SCD types'), which may not match how all users naturally phrase their requests. Users unfamiliar with the 'golden layer' terminology but needing the same functionality might not trigger this skill.

Suggestions

Add natural language variations users might say, such as 'data warehouse documentation', 'curated layer docs', 'final/presentation layer tables', or 'data model documentation'.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: detects SCD types, documents business rules, metric definitions, aggregation logic, and data quality scoring. These are clearly defined capabilities.

3 / 3

Completeness

Clearly answers both what ('Detects SCD types, documents business rules, metric definitions, aggregation logic, and data quality scoring') and when ('Use when documenting golden layer tables') with an explicit trigger clause.

3 / 3

Trigger Term Quality

Includes domain-specific terms like 'golden layer', 'SCD types', 'business rules', 'metric definitions', and 'data quality scoring', but these are somewhat technical/jargon-heavy. Missing more natural user phrases like 'data warehouse documentation', 'dbt docs', or 'data model documentation' that users might actually say.

2 / 3

Distinctiveness Conflict Risk

The focus on 'golden layer' tables is a very specific niche within data engineering/warehousing. The combination of SCD detection, business rules, and data quality scoring for golden layers is highly distinctive and unlikely to conflict with other documentation or data skills.

3 / 3

Total

11

/

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.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
treasure-data/td-skills
Reviewed

Table of Contents

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