Support Data Analyzer
Analyze customer support data (Excel/CSV with PIT, Support Tickets, CSAT) to categorize issues, prioritize by impact (P0-P3), and generate strategic markdown reports with examples and trends.
npx skills add majiayu000/claude-skill-registry --skill support-data-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Overview This Skill analyzes customer support feedback from multiple data sources (PIT/Roadblocks, Support Tickets, CSAT surveys, Ideas Forum) to identify pain points, categorize by theme, prioritize by customer impact, and generate comprehensive strategic reports. **Supports two analysis modes:** 1. **Comprehensive Analysis**: Full categorization, prioritization, and strategic reporting (initial analysis) 2. **Targeted Deep-Dive**: Timeline analysis, theme investigation, resolution tracking (follow-up queries) ## When to Apply **Use this skill for analysis of structured support data in the expected format:** **Initial Analysis:** - User provides Excel/CSV files with PIT/Roadblocks, Support Tickets, or CSAT data columns - User asks to "analyze support data", "prioritize customer feedback", or "generate impact report" - User wants to understand top customer pain points from support channels - User needs to scope analysis to a specific time period (e.g., "last 60 days") **Expected data format:** Excel/CSV with columns like frustration level, MRR, ticket categories, CSAT scores **Follow-up Analysis:** - User asks about specific findings: "Has X issue dropped off?", "Show timeline f
- Overview
- When to Apply
- Inputs
- Outputs
- Instructions for Claude
- Step 1: Data Loading and Validation
- Step 2: Theme Categorization
- Step 3: Priority Assignment
- Step 4: Customer Example Selection
- Step 5: CSAT Analysis (if data provided)
- Step 6: Trend Analysis (if historical data provided)
- Step 7: Report Generation
- Follow-up Analysis Workflow (Deep-Dive Queries)
- Step 1: Understand the Context
pip3 install openpyxl pandas --break-system-packages --quiet
What does the Support Data Analyzer skill do?
Analyze customer support data (Excel/CSV with PIT, Support Tickets, CSAT) to categorize issues, prioritize by impact (P0-P3), and generate strategic markdown reports with examples and trends.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill support-data-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.