AI Skill Report Card

Generating Skills

Creates Claude Code agent skill files from expertise descriptions. Use when someone describes their professional methodology or workflow that should be captured as a reusable skill.

B+78·Feb 12, 2026·Source: Web
15 / 15
YAML
--- name: analyzing-data description: Analyzes datasets to identify patterns and insights. Use when exploring new data or validating hypotheses. ---
Recommendation
Add more concrete input/output examples showing the actual skill file generation process with full YAML structure
15 / 15

Load your dataset and run initial exploration:

Python
import pandas as pd df = pd.read_csv('data.csv') print(df.info()) print(df.describe())
Recommendation
Include templates or frameworks for common skill types (analysis, writing, coding, etc.)
13 / 15
  • Load and inspect data structure
  • Check for missing values and outliers
  • Generate summary statistics
  • Create visualizations for key variables
  • Document findings and recommendations
Recommendation
Expand on edge cases like handling vague inputs or converting non-technical expertise into actionable workflows
13 / 15
  1. Parse Input - Extract the core expertise from the description
  2. Structure Content - Organize into the 5 required sections
  3. Create YAML Header - Generate gerund-form name and trigger-based description
  4. Write Quick Start - Provide immediate actionable example
  5. Document Workflow - Break down the process into clear steps with checklist
  6. Add Examples - Include concrete input/output pairs
  7. List Best Practices - Capture key guidelines
  8. Identify Pitfalls - Note common mistakes to avoid

Progress:

  • Extract methodology from input
  • Create YAML frontmatter with gerund name
  • Write Quick Start section with code/steps
  • Document step-by-step workflow
  • Add concrete examples
  • List best practices
  • Identify common pitfalls
  • Review for conciseness (~500 lines max)
15 / 20

Example 1: Input: "I'm a data scientist who specializes in exploratory data analysis. I always start by loading the data, checking data types, looking for missing values, then creating visualizations." Output: A skill named analyzing-data with Python code examples for pandas operations, visualization steps, and a systematic workflow checklist.

Example 2: Input: "I help companies write better proposals by structuring them with executive summary, problem statement, solution approach, timeline, and budget." Output: A skill named writing-proposals with template structure, example sections, and review criteria.

  • Use gerund form for skill names (verb-ing format)
  • Include trigger phrases in description ("Use when...")
  • Start with actionable Quick Start, no preamble
  • Provide concrete code examples or step templates
  • Keep explanations concise - assume Claude knows basics
  • Include progress checklists for complex workflows
  • Focus on specific methodology, not general advice
  • Aim for ~500 lines maximum length
  • Don't use first person ("I do...") or second person ("You should...")
  • Don't explain basic concepts Claude already knows
  • Don't create multiple options - provide the recommended approach
  • Don't ask clarifying questions if input is unclear - create minimal placeholder
  • Don't exceed 64 characters for skill names
  • Don't forget YAML frontmatter at the very beginning
  • Don't include preamble before the skill content
0
Grade B+AI Skill Framework
Scorecard
Criteria Breakdown
Quick Start
15/15
Workflow
13/15
Examples
15/20
Completeness
8/20
Format
15/15
Conciseness
12/15