Agent skill

Customer Feedback Analysis

by seb1n in seb1n/awesome-ai-agent-skills

Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports.

MITAuto-check passedSales & Support

Install Customer Feedback Analysis

skills CLI
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/customer-success/customer-feedback-analysis .claude/skills/customer-feedback-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
customer-feedback-analysis
GitHub stars
206
Token cost
~2.4k tokens
SKILL.md length
1,212 words
Files
1
Skills in repo
91
Repo updated
First seen
Licence
MIT

At a glance

Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports.

  • Works in 6 steps: Collect feedback data — Aggregate… → Clean and normalize — Deduplicate… → Extract themes from open-text responses… → …
  • The user requests customer feedback analysis
  • SKILL.md covers Workflow, Usage, Examples and Best Practices, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Customer Feedback Analysis is an agent skill from seb1n/awesome-ai-agent-skills. Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Sales & Support, covering Customer feedback analysis. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.

When your agent uses it

  • The user requests customer feedback analysis
  • Provides relevant inputs for this workflow

Example prompts

  • “/customer-feedback-analysis”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Collect feedback data — Aggregate feedback from all available sources: NPS survey responses (score + open text), CSAT ratings from support…
  2. Clean and normalize — Deduplicate responses from the same customer across channels. Standardize rating scales (convert 1-5 CSAT to 1-10…
  3. Extract themes from open-text responses — Apply topic modeling to cluster open-text feedback into coherent themes. Common theme categories…
  4. Calculate quantitative scores — Compute aggregate metrics: NPS (% Promoters minus % Detractors), CSAT average, and theme frequency…
  5. Identify trends — Compare current period metrics against previous periods (month-over-month, quarter-over-quarter). Flag themes with…
  6. Generate insight report — Produce a structured report with: executive summary (3-5 key takeaways), quantitative scorecard, theme breakdown…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Customer Feedback Analysis loads about 2.4k tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 1,212 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 1,212 words, ~2,361 tokens.

Download SKILL.mdSave it as .claude/skills/customer-feedback-analysis/SKILL.md (or your agent's skills folder).
name
customer-feedback-analysis
description
Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow.
license
MIT
metadata.author
community
metadata.version
1.0

Customer Feedback Analysis

Transform raw customer feedback from NPS surveys, CSAT responses, support interactions, and app store reviews into structured insights. This skill extracts recurring themes from open-text responses, calculates quantitative score distributions, identifies emerging trends over time, and produces reports that connect customer sentiment to specific product areas and business outcomes.

Workflow

  1. Collect feedback data — Aggregate feedback from all available sources: NPS survey responses (score + open text), CSAT ratings from support interactions, in-app feedback widgets, app store reviews, social media mentions, G2/Capterra reviews, and sales call notes. Tag each response with metadata: date, customer segment, plan tier, account tenure, and source channel. Ensure consistent schema across all sources.

  2. Clean and normalize — Deduplicate responses from the same customer across channels. Standardize rating scales (convert 1-5 CSAT to 1-10 for cross-comparison). Strip PII from open-text responses. Handle multilingual responses by detecting language and translating to English while preserving the original. Remove bot/spam responses using pattern detection (identical text, suspicious timing, single-word noise).

  3. Extract themes from open-text responses — Apply topic modeling to cluster open-text feedback into coherent themes. Common theme categories include: product reliability, ease of use, specific feature feedback, pricing/value perception, support quality, onboarding experience, and competitive comparison. Assign each response to one or more themes with a confidence score. Pull representative verbatim quotes for each theme.

  4. Calculate quantitative scores — Compute aggregate metrics: NPS (% Promoters minus % Detractors), CSAT average, and theme frequency distribution. Break scores down by segment (plan tier, industry, account size, tenure) to identify which cohorts are most and least satisfied. Calculate statistical significance for segment differences to avoid acting on noise.

  5. Identify trends — Compare current period metrics against previous periods (month-over-month, quarter-over-quarter). Flag themes with significant volume changes (up or down 20%+ from baseline). Detect emerging themes that appear for the first time or cross a frequency threshold. Correlate sentiment shifts with product releases, pricing changes, or market events.

  6. Generate insight report — Produce a structured report with: executive summary (3-5 key takeaways), quantitative scorecard, theme breakdown with representative quotes, trend analysis, segment comparison, and recommended actions. Each recommendation should be tied to a specific theme and prioritized by frequency and business impact.

Usage

Provide feedback data (CSV, JSON, or describe the dataset) and specify the analysis scope. The agent returns a structured insight report.

Analyze our Q4 NPS survey results (850 responses). Break down scores by plan
tier, extract the top themes from detractor comments, and identify any new
themes that weren't present in Q3.

Examples

Example 1: NPS survey analysis

Input: 850 NPS survey responses from Q4, segmented by plan tier.

Output:

Executive Summary:

  • Overall NPS: +32 (up from +28 in Q3, statistically significant at p<0.05)
  • Enterprise NPS: +52 | Pro NPS: +31 | Starter NPS: +14
  • Top detractor theme: "Reporting is too slow" (38% of detractor comments, up from 22% in Q3)
  • New emerging theme: "Need better mobile experience" (appeared in 12% of Q4 responses, absent in Q3)
  • Promoter loyalty driver: "Customer support is exceptional" (45% of promoter comments)

Score Distribution:

SegmentPromoters (9-10)Passives (7-8)Detractors (0-6)NPSResponses
Enterprise68%16%16%+52180
Pro52%27%21%+31420
Starter38%38%24%+14250
Overall51%28%21%+32850

Theme Breakdown (Detractor Comments, n=179):

ThemeFrequencyChange vs Q3Representative Quotes
Slow reporting38%+16pp"Dashboards take 20+ seconds to load with large datasets. This is killing our team's productivity."
Pricing concerns24%-3pp"The price jump from Pro to Enterprise is too steep. We need the features but can't justify 3x the cost."
Missing integrations18%-5pp"Still no native Salesforce integration. We've been asking for over a year."
Mobile experience12%NEW"I can't review dashboards on my phone during commute. Competitors have solid mobile apps."
Complex setup8%-8pp"Initial configuration took our team 3 weeks. Onboarding docs are outdated."

Recommended Actions:

  1. P0 — Reporting performance: Invest in query optimization and caching. 38% of detractors cite this, and it's growing fast. Estimated NPS impact: +5-8 points if resolved.
  2. P1 — Mobile experience: Commission a competitive analysis of mobile offerings. 12% is a new theme trending upward — get ahead of it before Q1 NPS.
  3. P2 — Pricing tier gap: Introduce a "Pro Plus" tier between Pro and Enterprise to capture accounts that need select enterprise features without the full price tag.
Show full SKILL.md (527 more words)Show less
Example 2: App store review sentiment analysis

Input: 1,200 app store reviews from the past 6 months (iOS and Android combined).

Output:

Overall Sentiment: 3.8★ average (iOS: 4.1★, Android: 3.4★)

Sentiment by Star Rating:

  • 5★ (34%): "Love the design," "Fast and reliable," "Best in category"
  • 4★ (22%): "Great but needs offline mode," "Almost perfect"
  • 3★ (18%): "Decent but buggy on Android," "OK for basic use"
  • 2★ (14%): "Crashes frequently," "Too expensive for what it offers"
  • 1★ (12%): "Lost my data," "Customer support unresponsive," "App doesn't open"

Feature Request Extraction (from 3★ and above reviews):

Feature RequestMentionsPlatformSample Quote
Offline mode87Both"I travel a lot and need to access my data without WiFi."
Dark mode64Both"Using this at night is blinding. Please add dark mode."
Widget support43iOS"Would love a home screen widget to see my daily stats."
Export to PDF38Both"I need to share reports with people who don't have accounts."
Android stability112Android"Crashes every time I try to edit a dashboard. Pixel 8, Android 14."

Critical Finding: Android rating (3.4★) drags overall score down. 72% of 1-2★ reviews are from Android users. Top complaint is crash on dashboard edit (Samsung and Pixel devices, Android 14+). Fixing this single bug could lift Android rating by an estimated 0.4 stars.

Best Practices

  • Analyze detractor and promoter comments separately — blending them into a single theme analysis dilutes the signal from each group.
  • Always report confidence intervals and sample sizes alongside NPS scores. A segment NPS of +60 from 15 responses is not actionable.
  • Use verbatim quotes in reports to leadership — raw customer voice is more persuasive than statistical summaries and prevents misinterpretation of theme labels.
  • Close the feedback loop by responding to detractors within 48 hours of survey completion. Customers who receive follow-up after negative feedback are 2x more likely to improve their score next cycle.
  • Track theme frequency over time rather than reacting to a single snapshot. A theme trending upward over 3 quarters is a structural issue; a one-quarter spike may be a temporary reaction to a specific release.
  • Separate "feature absence" complaints from "feature broken" complaints in your theme taxonomy — they require different organizational responses (product roadmap vs. engineering fix).

Edge Cases

  • Low response rates (<15%) — Results may suffer from non-response bias where only the most satisfied and most dissatisfied customers respond. Note the response rate prominently and recommend increasing it before drawing segment-level conclusions.
  • Survey fatigue — Accounts surveyed more than once per quarter show declining response rates and more negative scores. Cap survey frequency and rotate which customers are surveyed.
  • Sarcastic or ironic responses — "Oh great, another update that breaks everything, love it! 10/10" reads as positive to naive sentiment analysis. Flag responses where sentiment score and NPS score are contradictory for manual review.
  • Feedback influenced by recent outage — A single incident can dominate an entire survey period. Segment responses by pre/post incident and report both views. Consider extending the survey window to dilute the recency effect.
  • Competitor mentions in feedback — Responses like "I'm switching to CompetitorX" contain valuable competitive intelligence. Extract competitor mentions into a separate analysis and route to product strategy.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in customer-success/customer-feedback-analysis of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5

Compare with similar skills

Customer Feedback Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Customer Feedback Analysis

What does Customer Feedback Analysis do?

Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Customer Feedback Analysis is an agent skill from seb1n/awesome-ai-agent-skills. Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports.

When should I use Customer Feedback Analysis?

Customer Feedback Analysis fits situations like: the user requests customer feedback analysis; provides relevant inputs for this workflow.

How do I install Customer Feedback Analysis in Claude Code?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a claude-code`. Or copy the skill folder (customer-success/customer-feedback-analysis in seb1n/awesome-ai-agent-skills) into .claude/skills/customer-feedback-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Customer Feedback Analysis in Codex?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a codex`. Or copy the skill folder (customer-success/customer-feedback-analysis in seb1n/awesome-ai-agent-skills) into .agents/skills/customer-feedback-analysis in your project. Codex loads it when a task matches its description.

Can I use Customer Feedback Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customer-feedback-analysis, .gemini/skills/customer-feedback-analysis, .github/skills/customer-feedback-analysis and .opencode/skills/customer-feedback-analysis in your project.

What does Customer Feedback Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Customer Feedback Analysis is instructions for the agent only.

Does Customer Feedback Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Customer Feedback Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Customer Feedback Analysis use?

Customer Feedback Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Customer Feedback Analysis use?

About 2.4k tokens (SKILL.md is roughly 9.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Customer Feedback Analysis?

Skills that share tags, products or a category with Customer Feedback Analysis: Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 946 stars), Bggg Data Amazon (binggandata/bggg-skills, 603 stars), Zsxq (unnoo/zsxq-skill, 304 stars) and Roadtrip Navigator (Waybox-AI/roadtrip-skill, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Customer Feedback Analysis?

seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on August 9, 2026.

Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.