validation-report-generator

44 stars 10 forks
0
A

Generate structured 8-section validation reports with verdict (GOOD/BAD/NEEDS MAJOR WORK), strengths, critical flaws, blindspots, and concrete path forward. Use after strategic-cto-mentor has completed validation analysis and needs to produce final deliverable.

CommandsAgentsMarketplace
#claude-ai#path forward#MAJOR#cto-office#MAJOR WORK#cto#roadmap#claude-code

Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation for Agentic Skill

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skilz install alirezarezvani/claude-cto-team/validation-report-generator
skilz install alirezarezvani/claude-cto-team/validation-report-generator --agent opencode
skilz install alirezarezvani/claude-cto-team/validation-report-generator --agent codex
skilz install alirezarezvani/claude-cto-team/validation-report-generator --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding assistants

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Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/alirezarezvani/claude-cto-team
2. Copy the agent skill directory:
cp -r claude-cto-team/skills/validation-report-generator ~/.claude/skills/

Need detailed installation help? Check our platform-specific guides:

Related Agentic Skills

Agentic Skill Details

Stars
44
Forks
10
Type
Technical
Meta-Domain
data ai
Primary Domain
data analysis
Market Score
0

Agent Skill Grade

A
Score: 93/100 Click to see breakdown

Score Breakdown

Spec Compliance
11/15
PDA Architecture
25/30
Ease of Use
22/25
Writing Style
9/10
Utility
18/20
Modifiers: +8

Areas to Improve

  • Description needs trigger phrases
  • Missing TOC in main skill file
  • Redundant content between files

Recommendations

  • Address 1 high-severity issues first
  • Add trigger phrases to description for discoverability
  • Add table of contents for files over 100 lines

Graded: 2026-01-24

Developer Feedback

I was looking at your validation-report-generator and noticed how you've structured the progressive disclosure—most skills front-load their complexity, but you've managed to keep the token efficiency tight while still covering the edge cases. That's a solid A-grade approach.

Links:

The TL;DR

You're at 93/100, solidly in A territory. This is based on Anthropic's best practices for skill structure and usability. Your strongest area is Writing Style (9/10)—the voice stays consistent and instructional throughout. The weakness is Spec Compliance (11/15), mainly because your description field is missing trigger phrases that help users discover the skill.

What's Working Well

  • Layered architecture is chef's kiss: SKILL.md points cleanly to report-template.md and verdict-criteria.md without burying details. The reference depth is perfect at one level down.
  • Terminology consistency across files: You use "verdict," "validation," and "bulletproof" uniformly, which makes the skill feel cohesive and easy to follow.
  • Quality checklist before delivery: The 10-point verification list in the Quality Checklist section is excellent—it shows you thought about how users actually use this, not just the happy path.
  • Tone calibration guidance: Letting users adjust tone (confident vs. diplomatic) while keeping structure rigid is the right balance. It gives freedom without chaos.

The Big One: Add Trigger Phrases to Description

Your frontmatter description needs actual trigger keywords. Right now it reads:

description: Generate structured 8-section validation reports with verdict (GOOD/BAD/NEEDS MAJOR WORK)...

The system needs phrases like "Use when asked to..." so...

AI-Detected Topics

Extracted using NLP analysis

claude-ai path forward MAJOR cto-office MAJOR WORK cto roadmap claude-code ai-workflow-automation validation analysis

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