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Skillzwave

visualization-patterns

31 stars 7 forks
28
C

Use when designing dashboards, reports, and narratives for GTM stakeholders.

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Installation for Agentic Skill

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skilz install gtmagents/gtm-agents/visualization-patterns
skilz install gtmagents/gtm-agents/visualization-patterns --agent opencode
skilz install gtmagents/gtm-agents/visualization-patterns --agent codex
skilz install gtmagents/gtm-agents/visualization-patterns --agent gemini

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

1. Clone the repository:
git clone https://github.com/gtmagents/gtm-agents
2. Copy the agent skill directory:
cp -r gtm-agents/plugins/analytics-pipeline-orchestration/skills/visualization-patterns ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Owner
gtmagents (GitHub)
Repository
gtm-agents
Stars
31
Forks
7
Type
Non-Technical
Meta-Domain
data ai
Primary Domain
data analysis
Market Score
28
Agentic Skill Grades →

Agent Skill Grade

C
Score: 71/100 Click to see breakdown

Score Breakdown

Spec Compliance
12/15
PDA Architecture
20/30
Ease of Use
18/25
Writing Style
8/10
Utility
12/20
Modifiers: +1

Areas to Improve

  • Missing Reference Files for Templates
  • Insufficient Workflow Detail
  • No Validation or Feedback Loop

Recommendations

  • Address 2 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 took a look at your visualization-patterns skill and noticed you're tackling a pretty common pain point—helping developers translate data into clear, visual representations without getting lost in implementation details. The structure's solid (71/100), though there's some room to tighten up how you're guiding users through the progression of complexity.

Links:

The TL;DR

You're at 71/100, C grade—solid fundamentals but needs some depth work. This evaluation is based on Anthropic's best practices for skill design. Your strongest area is Spec Compliance (12/15)—clean YAML frontmatter, proper naming conventions. Weakest area is Utility (12/20)—you've got the framework sketched out, but it needs concrete examples and validation loops to actually help someone build a dashboard.

What's Working Well

  • Spec compliance is tight – Valid YAML, proper hyphen-case naming, all required fields in place
  • Clear trigger phrases – "dashboard", "reports", "narratives" are good GTM-specific keywords that'll help discoverability
  • Consistent terminology – KPI, dashboard, visualization language stays consistent throughout, which matters when users are trying to follow along
  • Audience-first framing – Starting with "Audience & Story" is smart; you're not jumping straight into technical implementation

The Big One: Missing Reference Files and Templates

This is what's holding you back most. Your skill lists templates ("Dashboard wireframe grid with KPI slots", "Metric dictionary", "Adoption checklist") but doesn't actually provide them. Right now, a Claude agent reading this has to invent those templates from scratch, which defeats the whole purpose of a skill.

**...

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