power-bi-model-design-review
Comprehensive Power BI data model design review prompt for evaluating model architecture, relationships, and optimization opportunities.
48
22%
Does it follow best practices?
Impact
89%
1.08xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/power-bi-development/skills/power-bi-model-design-review/SKILL.mdQuality
Discovery
32%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 identifies the Power BI domain and broadly names review areas (architecture, relationships, optimization), but lacks concrete specific actions and entirely omits a 'Use when...' clause. It reads more like a title than a functional description that would help Claude distinguish this skill from others in a large skill library.
Suggestions
Add an explicit 'Use when...' clause with trigger scenarios, e.g., 'Use when the user asks to review a Power BI data model, check relationships, optimize DAX performance, or evaluate star schema design.'
List more specific concrete actions such as 'evaluate star schema compliance, review relationship cardinality, identify redundant columns, assess DAX measure performance, and check row-level security configuration.'
Include natural user terms and file types like 'Power BI Desktop', '.pbix', 'DAX', 'measures', 'calculated columns', 'slow reports', and 'data refresh' to improve trigger term coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Power BI data model design review) and some actions (evaluating model architecture, relationships, optimization opportunities), but these are fairly high-level and not concrete specific actions like 'check star schema compliance' or 'identify missing indexes'. | 2 / 3 |
Completeness | Describes what it does (Power BI data model design review) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per rubric guidelines, missing 'Use when' caps completeness at 2, and the 'what' is also only moderately clear, warranting a 1. | 1 / 3 |
Trigger Term Quality | Includes relevant keywords like 'Power BI', 'data model', 'relationships', and 'optimization', but misses common user variations like 'DAX', 'measures', 'star schema', 'performance', 'slow report', or '.pbix'. | 2 / 3 |
Distinctiveness Conflict Risk | Power BI is a specific enough domain to reduce conflicts with generic data skills, but 'data model design review' and 'optimization opportunities' could overlap with general database design or other BI tool skills. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
12%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is an extremely verbose, abstract checklist document that reads like a consulting framework rather than an actionable skill for Claude. It contains no executable code, no concrete examples, and no specific DAX patterns or Power BI techniques that Claude wouldn't already know. The content would benefit enormously from being condensed to ~50 lines of genuinely novel guidance with concrete examples, splitting detailed checklists into referenced files.
Suggestions
Replace abstract checklists with concrete, executable examples — e.g., show specific DAX patterns for common anti-patterns, actual M code for data type optimization, or before/after relationship diagrams.
Reduce the content by 80%+ by removing well-known Power BI concepts Claude already understands (star schema basics, what RLS is, etc.) and focus only on non-obvious decision criteria and specific techniques.
Split the detailed checklists, output templates, and specialized review types into separate referenced files (e.g., CHECKLISTS.md, TEMPLATES.md) and keep SKILL.md as a concise overview with navigation.
Add concrete validation steps with specific tools/approaches — e.g., 'Run DAX Studio metrics to check model size' or 'Use Best Practice Analyzer with these specific rules' — instead of abstract checkbox items.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~300+ lines, mostly consisting of checklists and templates that restate well-known Power BI best practices Claude already knows. The content reads like a textbook rather than a skill that adds novel, specific knowledge. Massive redundancy across sections (e.g., performance considerations appear in multiple phases). | 1 / 3 |
Actionability | Despite its length, the skill contains zero executable code, no concrete DAX examples, no specific commands, and no real before/after examples. Everything is abstract checklists and templates with placeholder text like '[Critical issues impacting functionality/performance]'. It describes what to review but never shows how to actually do it. | 1 / 3 |
Workflow Clarity | There is a phased structure (Phase 1-3) and time-boxed checklists (30-min vs 4-8 hour), which provides some sequencing. However, there are no validation checkpoints, no feedback loops for error recovery, and no concrete decision criteria for when to proceed between phases. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of text with no references to external files. All content is inline despite being far too long for a single skill file. The specialized review types, detailed checklists, and output templates could easily be split into separate referenced documents. | 1 / 3 |
Total | 5 / 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
- github/awesome-copilot
- Commit
4020587
- Reviewed
Table of Contents
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.