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ml-cv-specialist

44 stars 10 forks
0
A

Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.

CommandsAgentsMarketplace
#Pipeline Architecture#claude-ai#inference architecture#Object Detection#cto-office#model#cto#Deep expertise

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Installation for Agentic Skill

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skilz install alirezarezvani/claude-cto-team/ml-cv-specialist
skilz install alirezarezvani/claude-cto-team/ml-cv-specialist --agent opencode
skilz install alirezarezvani/claude-cto-team/ml-cv-specialist --agent codex
skilz install alirezarezvani/claude-cto-team/ml-cv-specialist --agent gemini

First time? Install Skilz: pip install skilz

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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/ml-cv-specialist ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

Claude Desktop Guide Claude Code Guide Troubleshooting

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Agentic Skill Details

Owner
alirezarezvani (GitHub)
Repository
claude-cto-team
Stars
44
Forks
10
Type
Technical
Meta-Domain
data ai
Primary Domain
machine learning
Market Score
0
Agentic Skill Grades →

Agent Skill Grade

A
Score: 90/100 Click to see breakdown

Score Breakdown

Spec Compliance
12/15
PDA Architecture
25/30
Ease of Use
22/25
Writing Style
9/10
Utility
18/20
Modifiers: +4

Areas to Improve

  • Missing Reference File
  • Missing Navigation TOC
  • Second-Person Voice

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've been digging through computer vision skills lately, and your approach to grounding ML/CV concepts in practical Claude workflows is refreshing—most skills either go too theoretical or skip the implementation entirely, but you managed to thread that needle pretty well.

Links:

TL;DR

You're at 90/100, solid A-grade territory. This is based on Anthropic's best practices for skill design. Your strongest area is Writing Style (9/10)—the content is dense and appropriately technical without fluff. The weakest link is Spec Compliance (12/15), which is fixable with a couple of specific additions.

What's Working Well

  • Layered architecture is chef's kiss. SKILL.md gives the overview, model-catalog.md handles the deep benchmarks—clean separation that respects reader attention. The progressive disclosure structure is exactly what Claude needs.
  • Your decision trees actually work. The "API vs. Self-Hosted" framework and "I need to classify images" tables make this actionable. Not just theoretical—someone can actually use this to pick a model.
  • Trigger phrases hit the mark. "Designing machine learning systems" and "computer vision pipelines" are exactly what people search for. You nailed the discoverability language.
  • Objectivity throughout. Zero marketing fluff, all specifications and trade-offs. The cost/latency/accuracy tables are the kind of thing that actually moves decisions forward.

The Big One

Missing reference file is breaking your PDA structure. Line 382 references inference-patterns.md that doesn't exist. This isn't just a dead link—it signals incomplete architecture to anyone reading the layered structure.

Fix: Either create the file with the promised architectu...

AI-Detected Topics

Extracted using NLP analysis

Pipeline Architecture claude-ai inference architecture Object Detection cto-office model cto Deep expertise roadmap inference

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