Review Analysis
liangdabiao/amazon-sorftime-research-MCP-skill
对亚马逊商品评论进行深度分析,自动识别产品痛点、分析退货原因,生成改进建议和客服回复模板。Invoke when user uses /review-analysis command with a product ASIN.
Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports.
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "customer-feedback-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis into .claude/skills/customer-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-feedback-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/customer-success/customer-feedback-analysis .agents/skills/customer-feedback-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "customer-feedback-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis into .agents/skills/customer-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-feedback-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/customer-success/customer-feedback-analysis .cursor/skills/customer-feedback-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "customer-feedback-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis into .cursor/skills/customer-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-feedback-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/seb1n/awesome-ai-agent-skills.git --path customer-success/customer-feedback-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/customer-success/customer-feedback-analysis .gemini/skills/customer-feedback-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "customer-feedback-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis into .gemini/skills/customer-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-feedback-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/customer-success/customer-feedback-analysis .github/skills/customer-feedback-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "customer-feedback-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis into .github/skills/customer-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-feedback-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills customer-feedback-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/customer-success/customer-feedback-analysis .opencode/skills/customer-feedback-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "customer-feedback-analysis" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis into .opencode/skills/customer-feedback-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-feedback-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
customer-feedback-analysisAnalyze 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. 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.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 1,212 words, ~2,361 tokens.
.claude/skills/customer-feedback-analysis/SKILL.md (or your agent's skills folder).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.
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.
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).
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.
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.
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.
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.
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.Input: 850 NPS survey responses from Q4, segmented by plan tier.
Output:
Executive Summary:
Score Distribution:
| Segment | Promoters (9-10) | Passives (7-8) | Detractors (0-6) | NPS | Responses |
|---|---|---|---|---|---|
| Enterprise | 68% | 16% | 16% | +52 | 180 |
| Pro | 52% | 27% | 21% | +31 | 420 |
| Starter | 38% | 38% | 24% | +14 | 250 |
| Overall | 51% | 28% | 21% | +32 | 850 |
Theme Breakdown (Detractor Comments, n=179):
| Theme | Frequency | Change vs Q3 | Representative Quotes |
|---|---|---|---|
| Slow reporting | 38% | +16pp | "Dashboards take 20+ seconds to load with large datasets. This is killing our team's productivity." |
| Pricing concerns | 24% | -3pp | "The price jump from Pro to Enterprise is too steep. We need the features but can't justify 3x the cost." |
| Missing integrations | 18% | -5pp | "Still no native Salesforce integration. We've been asking for over a year." |
| Mobile experience | 12% | NEW | "I can't review dashboards on my phone during commute. Competitors have solid mobile apps." |
| Complex setup | 8% | -8pp | "Initial configuration took our team 3 weeks. Onboarding docs are outdated." |
Recommended Actions:
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:
Feature Request Extraction (from 3★ and above reviews):
| Feature Request | Mentions | Platform | Sample Quote |
|---|---|---|---|
| Offline mode | 87 | Both | "I travel a lot and need to access my data without WiFi." |
| Dark mode | 64 | Both | "Using this at night is blinding. Please add dark mode." |
| Widget support | 43 | iOS | "Would love a home screen widget to see my daily stats." |
| Export to PDF | 38 | Both | "I need to share reports with people who don't have accounts." |
| Android stability | 112 | Android | "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.
© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in customer-success/customer-feedback-analysis of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Customer Feedback Analysis this skillseb1n/awesome-ai-agent-skills | 206 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Review Analysisliangdabiao/amazon-sorftime-research-MCP-skill | 946 | 1 repos | ~2.5k | Automated safety check: Pass | None | |
| Bggg Data Amazonbinggandata/bggg-skills | 603 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Zsxqunnoo/zsxq-skill | 304 | — | ~3.8k | Automated safety check: Pass | MIT | |
| Roadtrip NavigatorWaybox-AI/roadtrip-skill | 126 | — | ~3.4k | Automated safety check: Pass | MIT | |
| Always Compareai-analyst-lab/ai-analyst | 304 | — | ~1.4k | Automated safety check: Pass | MIT |
liangdabiao/amazon-sorftime-research-MCP-skill
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Categories
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.
Customer Feedback Analysis fits situations like: the user requests customer feedback analysis; provides relevant inputs for this workflow.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Customer Feedback Analysis is instructions for the agent only.
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.
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.
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.
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.
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.
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.