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community-sentiment-dashboard

28.6
D

Reporting template for tracking sentiment, risks, and advocacy signals across social + community channels.

Marketplace
Also in: ci cd networking data analysis

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Installation for Agentic Skill

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skilz install gtmagents/gtm-agents/community-sentiment-dashboard
skilz install gtmagents/gtm-agents/community-sentiment-dashboard --agent opencode
skilz install gtmagents/gtm-agents/community-sentiment-dashboard --agent codex
skilz install gtmagents/gtm-agents/community-sentiment-dashboard --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding agents

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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/social-media-marketing/skills/community-sentiment-dashboard ~/.claude/skills/

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

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Agentic Skill Details

Repository
gtm-agents
Type
Non-Technical
Meta-Domain
media
Primary Domain
monitoring
Market Score
28.6

Agent Skill Grade

D
Score: 62/100 Click to see breakdown

Score Breakdown

Spec Compliance
11/15
PDA Architecture
17/30
Ease of Use
15/25
Writing Style
7/10
Utility
11/20
Modifiers: +1

Areas to Improve

  • No trigger phrases
  • Templates are mentioned but not provided in reference files
  • Description lacks specific trigger terms that users would search for

Recommendations

  • Focus on improving Pda (currently 17/30)
  • Focus on improving Utility (currently 11/20)
  • Address 3 high-severity issues first

Graded: 1/24/2026

Developer Feedback

I found your community-sentiment-dashboard skill and noticed it's tackling a really useful problem—real-time sentiment tracking across communities—but the implementation feels like it's caught between a few different architectural directions. With a 62/100, there's solid potential here, but I'd like to dig into what's holding it back.

Links:

The TL;DR

You're at 62/100, which lands you in D territory. This is based on Anthropic's five-pillar framework for agentic skills. Your Spec Compliance is actually solid (11/15), but Utility is dragging things down (11/20)—you've got the structure, just missing the substance.

What's Working Well

  • Valid frontmatter – Your YAML is clean and follows conventions. Hyphen-case naming is correct.
  • Clear problem domain – The five-component framework (Signal Sources, Sentiment Stack, Risk Board, Advocates, Actions) is a sensible way to slice sentiment tracking.
  • grep-friendly structure – The skill file is organized in a way that's easy to search and navigate.

The Big One: Missing Templates and Examples

Here's the thing: you mention templates but they don't actually exist. Your Templates section lists "Dashboard slide layout (KPI tiles + trend graphs + highlights/lowlights)" and "Escalation tracker" and "Advocate tracker," but users get nothing concrete to work with.

Why it matters: Without actual examples, users can't visualize what the output looks like or understand the exact shape of data they're building. That's a quick +8 point swing right there (hitting PDA layering, reference depth, and utility all at once).

The fix: Create reference files—references/dashboard-template.md with an actual slide layout showing sample KPIs, references/escalation-tracker.md with a real spreadsheet structure, references/example-dashboard.md with sample sentiment data. Don't overthink it; even a markdown table showing "5,234 mentions | 68% positive | 12 escalations" moves the needle.

Other Things Worth Fixing

  1. Trigger phrases are buried – Your description doesn't say "Use when asked to 'sentiment dashboard' or 'community listening report'." Users won't find this skill unless you're explicit. Rewrite the description to include actual trigger terms users would search for. (+2-3 points)

  2. Workflow steps are missing – You list components but no sequential workflow. Add numbered steps: 1. Configure signal sources (connect APIs), 2. Set up sentiment analysis stack, 3. Build risk board, 4. Identify advocates. (+3-5 points)

  3. Imperative voice inconsistency – Sections use noun phrases ("Signal Sources – social listening feeds") instead of verbs ("Configure Signal Sources – Connect social listening feeds, integrate community platforms"). Tighten the language. (+2 points)

Quick Wins

  • Add reference files with actual templates and example data (+8 points—biggest bang for your buck)
  • Rewrite the frontmatter description with explicit trigger phrases (+3 points)
  • Convert framework to numbered workflow steps with concrete actions (+4 points)
  • Show a sample dashboard output in the examples section (+3 points)

These four changes alone could push you to mid-70s.


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