Survey Analyzer
SkillDev toolsAnalyze survey and feedback data with statistical distributions, cross-tabulations, significance testing, and open-ended theme extraction. Produces executive-ready survey reports with segment comparisons and action recommendations. Use when processing NPS, CSAT, employee engagement, or market research surveys.
Use Survey Analyzer in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Survey Analyzer skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by hoavdc/codexkit in skills/codexkit-survey-analyzer/SKILL.md and read by Ahel’s review.
When to Use
- After collecting NPS, CSAT, or employee engagement survey responses
- When analyzing market research or customer feedback data
- When leadership needs actionable insights from survey results
- When comparing satisfaction across segments (regions, teams, products)
Procedure
Step 1 — Data Overview
Summarize the survey:
- Total responses vs invitations sent → response rate
- Collection period
- Question types: Likert scale, multiple choice, ranking, open-ended
- Known biases: self-selection, non-response, recency
Step 2 — Quantitative Analysis
For each closed-ended question:
| Question | N | Mean | Median | Std Dev | Distribution Shape |
|---|---|---|---|---|---|
| [Q1 text] | [n] | [mean] | [median] | [sd] | Normal / Skewed L / Skewed R / Bimodal |
Calculate key indices:
- NPS: % Promoters (9–10) − % Detractors (0–6)
- CSAT: % Satisfied (4–5 on 5-point scale)
- Engagement: Overall index from engagement battery
Step 3 — Segment Comparison
Cross-tabulate by key segments:
| Segment | N | Score | vs Overall | Significant? |
|---|---|---|---|---|
| Region A | 120 | 72 | +4 | Yes (p<0.05) |
| Region B | 95 | 65 | −3 | No (p=0.12) |
Test significance:
- Chi-square for categorical × categorical
- t-test or ANOVA for continuous × categorical
- Flag small samples (<30) as unreliable
Step 4 — Open-Ended Theme Extraction
For free-text responses:
- Code responses into themes (max 8–10 themes)
- Count frequency of each theme
- Identify sentiment per theme (positive / neutral / negative)
| Theme | Frequency | % of Responses | Sentiment | Example Quote |
|---|---|---|---|---|
| Onboarding speed | 45 | 18% | Negative | "Took 3 weeks to get access" |
Step 5 — Insight Synthesis
Structure insights as:
- What: the finding (data-driven)
- So What: why it matters (impact)
- Now What: recommended action
Step 6 — Action Recommendations
| Priority | Insight | Action | Owner | Timeline |
|---|---|---|---|---|
| 1 | NPS dropped 8 pts in segment X | Investigate root cause | CX Lead | 2 weeks |
Inputs
| Input | Required | Format |
|---|---|---|
| Survey responses | Yes | CSV, spreadsheet, or summary tables |
| Question list | Yes | Questions with response types |
| Segments | Recommended | How to slice the data |
| Previous results | Recommended | For trend comparison |
Output
## Survey Analysis — [Survey Name]
### Overview
- **Period:** Q1 2024 | **Responses:** 532 / 1,200 (44% response rate)
- **NPS:** +32 (↓4 from Q4) | **CSAT:** 78% (stable)
### Key Findings
1. **Onboarding satisfaction dropped 12 points** — driven by Region B
- Root cause: New system rollout created delays
- Action: Expedite training for Region B ops team
2. **Support quality is the #1 driver of promoter scores**
- Correlation: 0.72 between support rating and NPS
- Action: Protect support team headcount in budget cycle
### Segment Comparison
[Cross-tabulation table]
### Open-Ended Themes
[Theme frequency table with example quotes]
### Recommended Actions
[Prioritized action table with owners]
Definition of Done
- Response rate calculated and bias noted
- All quantitative questions summarized with distributions
- Segment comparisons with significance testing
- Open-ended themes extracted and quantified
- What / So What / Now What insights provided
- Action recommendations prioritized with owners
Quality Criteria
- Data sources and assumptions are explicitly stated
- Calculations are reproducible from provided inputs
- Visualizations or tables have clear labels, units, and time ranges
- Caveats and confidence levels are documented for estimates
Verification (4C)
| Check | Question |
|---|---|
| Correctness | Are formulas, aggregations, and statistical methods applied correctly? |
| Completeness | Does the analysis cover all requested metrics and time ranges? |
| Context-fit | Are the chosen metrics relevant to the business question being answered? |
| Consequence | If this data were used for a decision today, what blind spots remain? |
Edge Cases
- Missing or incomplete data — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates.
- Outliers skewing results — Report with and without outliers. Document the decision to include or exclude.
- Changing data definitions mid-period — Split analysis at the change boundary and note the schema difference.
Changelog
- v1.0.0 — Initial release
Signals
- GitHub stars
- 25
- Forks
- 13
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
codexkit-survey-analyzer- Source
- github.com/hoavdc/codexkit