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Use when teams need to cluster complai...\n\nTags: latest:1.0.1\n\nVersion history:\n\nv1.0.1 | 2026-03-26T07:46:56.626Z | user\n\nUpgrade internals with clearer clustering, root-cause logic, and decision-ready outputs\n\nv1.0.0 | 2026-03-13T02:16:23.274Z | user\n\nInitial release\n\nArchive index:\n\nArchive v1.0.1: 4 files, 3885 bytes\n\nFiles: references/output-template.md (184b), skill-card.md (1839b), SKILL.md (4991b), _meta.json (134b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: review-analysis\ndescription: Analyze customer reviews, complaints, and feedback to find repeat patterns, likely root causes, and action priorities. Use when teams need to cluster complaints, separate product issues from messaging issues, identify purchase drivers or refund triggers, and turn messy review data into a concise decision-ready report.\n---\n\n# Review Analysis\n\nTurn messy reviews, complaints, and feedback into a short decision memo the team can actually act on.\n\nThis skill is not just for “summarizing reviews.”\n\nIts real job is to help answer:\n- **What are people repeatedly saying?**\n- **What problems are actually frequent vs just loud?**\n- **Is the issue in the product, the messaging, the offer, shipping, or support?**\n- **What should the team fix first?**\n- **What can marketing, product, ops, and support each learn from the feedback?**\n\n## Solves\n\nReview data is usually noisy and operationally useless in raw form:\n- hundreds of comments, but no pattern hierarchy;\n- teams confuse anecdotes with repeat problems;\n- product issues get mixed with bad expectation-setting;\n- strengths are underused because nobody clusters positive themes;\n- support, product, and growth teams all read the same reviews differently;\n- no one translates feedback into action priorities.\n\nGoal:\n**Turn unstructured feedback into pattern clusters, likely causes, and recommended next steps.**\n\n## Use when\n\nUse when the user needs structured insight from customer feedback rather than a raw summary.\n\nTypical cases:\n- summarizing product reviews from marketplaces or app stores;\n- clustering repeated complaints;\n- identifying refund / return drivers;\n- extracting product strengths and buyer-loved features;\n- separating product quality issues from messaging or expectation mismatch;\n- turning review data into FAQ, copy, product, or support actions;\n- preparing a concise report for product, ops, CX, or marketing teams.\n\n## Do not use when\n\nDo not use this skill when:\n- the user only wants sentiment labels with no explanation;\n- the task is broad social listening across the public web rather than a defined feedback set;\n- there is too little review data to identify meaningful patterns;\n- the user wants rigorous statistical causality rather than directional pattern analysis;\n- the task is support ticket workflow automation rather than insight extraction.\n\n## Inputs\n\nAsk for the minimum useful analysis set:\n- review source(s)\n- product / service name\n- review text or feedback sample\n- date range, if relevant\n- market / platform, if relevant\n- whether focus should be on complaints, positives, refunds, retention, or all feedback\n- any business question to prioritize\n\n## Workflow\n\n### 1. Define the review set\nClarify what is being analyzed:\n- marketplace reviews\n- app reviews\n- support complaints\n- refund / return notes\n- post-purchase survey responses\n- social comments collected into a feedback set\n\n### 2. Normalize and cluster the feedback\nGroup feedback into useful buckets, such as:\n- product quality / defects\n- expectation mismatch\n- shipping / logistics\n- service / support\n- pricing / value perception\n- feature gaps\n- usability / onboarding friction\n- trust / claim issues\n- delight drivers / positive strengths\n\n### 3. Identify repeat patterns\nFor each cluster, assess:\n- frequency\n- severity\n- confidence level\n- likely root cause\n- which team owns the problem\n\nAlways distinguish:\n- **repeat pattern vs loud anecdote**\n- **product issue vs messaging issue**\n- **true defect vs wrong customer expectation**\n\n### 4. Translate insight into action\nRecommend the next step clearly:\n- fix now\n- monitor\n- rewrite messaging\n- update FAQ\n- adjust offer or positioning\n- escalate to product / ops / support\n\n## Output format\n\nReturn a concise decision-ready report:\n\n1. **Top patterns**\n   - ranked by importance, not just by volume\n\n2. **Evidence snippets**\n   - short representative quotes or examples\n\n3. **Likely root cause**\n   - product / messaging / offer / shipping / support / unclear\n\n4. **Severity / urgency**\n   - high / medium / low, with short explanation\n\n5. **Recommended action**\n   - what should be done next and by whom\n\n6. **Optional positives worth amplifying**\n   - strengths to reuse in copy, PDPs, ads, or FAQs\n\n## Quality bar\n\nA strong analysis should:\n- separate signal from noise;\n- keep evidence snippets short and representative;\n- distinguish product issues from expectation-setting issues;\n- avoid pretending root cause certainty is higher than it is;\n- identify actionable implications, not just themes;\n- help a real operator decide what to do next.\n\n## What “better” looks like\n\nGood output should make it obvious:\n- what the main complaints are;\n- what the hidden strengths are;\n- which issues are operational vs messaging-driven;\n- what deserves immediate action;\n- what can be used to improve copy, FAQ, product decisions, or CX.\n\n## Resources\n\nRead `references/output-template.md` for the standard report layout.\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn70fv0ehp50emedet9tx3fekd82pw3b\",\n  \"slug\": \"review-analysis\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1774511216626\n}\n\nFile v1.0.1:references/output-template.md\n\n# Review Analysis Output Template\n\n## Top patterns\n1. \n2. \n3. \n\n## Evidence snippets\n- \n- \n\n## Likely root causes\n- \n\n## Recommended actions\n- Fix now:\n- Monitor:\n- Messaging changes:\n\nFile v1.0.1:skill-card.md\n\n## Description:\n\nAnalyzes customer reviews, complaints, and feedback to identify repeated patterns, likely root causes, and prioritized actions.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[leooooooow](https://clawhub.ai/user/leooooooow)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal product, operations, customer experience, and marketing teams use this skill to turn defined review or feedback sets into ranked patterns, likely root causes, and next actions.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Review datasets may contain sensitive personal data.\n\nMitigation: Avoid providing sensitive personal data unless the user intends the agent to process it as part of the review analysis.\n\nRisk: Root-cause findings are directional pattern analysis rather than rigorous statistical causality.\n\nMitigation: Use the skill's confidence, frequency, severity, and evidence snippets to review recommendations before acting on them.\n\n## Reference(s):\n\n- [Review Analysis Output Template](references/output-template.md)\n- [ClawHub Skill Page](https://clawhub.ai/leooooooow/skills/review-analysis)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance]\n\n**Output Format:** [Markdown decision report]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Includes ranked patterns, short evidence snippets, likely root causes, severity or urgency, recommended actions, and optional positives worth amplifying.]\n\n## Skill Version(s):\n\n1.0.1 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.0: 3 files, 1341 bytes\n\nFiles: references/output-template.md (184b), SKILL.md (1388b), _meta.json (134b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: review-analysis\ndescription: Analyze customer reviews, complaints, and feedback to find patterns, root causes, and action priorities. Use when summarizing reviews for products or services, clustering repeat complaints, extracting product strengths/weaknesses, identifying refund drivers, or turning messy feedback into a concise decision-ready report.\n---\n\n# Review Analysis\n\nTurn messy feedback into a short decision memo.\n\n## Workflow\n\n1. Define the review set.\n   - marketplace reviews\n   - app reviews\n   - support complaints\n   - refund / return notes\n\n2. Group the feedback.\n   - product quality\n   - expectation mismatch\n   - shipping / service\n   - feature gaps\n   - trust / claim issues\n\n3. Identify patterns.\n   - frequency\n   - severity\n   - likely root cause\n   - whether the issue belongs to product, content, offer, or operations\n\n4. Recommend actions.\n   - fix now\n   - monitor\n   - rewrite messaging\n   - escalate to product / ops\n\n## Output format\n\nReturn:\n- top patterns\n- evidence snippets\n- likely root cause\n- severity / urgency\n- recommended actions\n\n## Quality bar\n\n- Separate loud anecdotes from repeat patterns.\n- Keep evidence snippets short and representative.\n- Distinguish product issues from expectation-setting issues.\n- Avoid over-claiming root cause certainty.\n\n## Resources\n\nRead `references/output-template.md` for the standard report layout.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn70fv0ehp50emedet9tx3fekd82pw3b\",\n  \"slug\": \"review-analysis\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1773368183274\n}\n\nFile v1.0.0:references/output-template.md\n\n# Review Analysis Output Template\n\n## Top patterns\n1. \n2. \n3. \n\n## Evidence snippets\n- \n- \n\n## Likely root causes\n- \n\n## Recommended actions\n- Fix now:\n- Monitor:\n- Messaging changes:","readmeExcerpt":"Skill: Review Analysis Owner: leooooooow Summary: Analyze customer reviews, complaints, and feedback to find repeat patterns, likely root causes, and action priorities. Use when teams need to cluster complai... Tags: latest:1.0.1 Version history: v1.0.1 | 2026-03-26T07:46:56.626Z | user Upgrade internals with clearer clustering, root-cause logic, and decision-ready outputs v1.0.0 | 2026-03-13T02:16:23.274Z | user Ini","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: review-analysis\ndescription: Analyze customer reviews, complaints, and feedback to find repeat patterns, likely root causes, and action priorities. Use when teams need to cluster complaints, separate product issues from messaging issues, identify purchase drivers or refund triggers, and turn messy review data into a concise decision-ready report.\n---\n\n# Review Analysis\n\nTurn messy reviews, complaints, and feedback into a short decision memo the team can actually act on.\n\nThis skill is not just for “summarizing reviews.”\n\nIts real job is to help answer:\n- **What are people repeatedly saying?**\n- **What problems are actually frequent vs just loud?**\n- **Is the issue in the product, the messaging, the offer, shipping, or support?**\n- **What should the team fix first?**\n- **What can marketing, product, ops, and support each learn from the feedback?**\n\n## Solves\n\nReview data is usually noisy and operationally useless in raw form:\n- hundreds of comments, but no pattern hierarchy;\n- teams confuse anecdotes with repeat problems;\n- product issues get mixed with bad expectation-setting;\n- strengths are underused because nobody clusters positive themes;\n- support, product, and growth teams all read the same reviews differently;\n- no one translates feedback into action priorities.\n\nGoal:\n**Turn unstructured feedback into pattern clusters, likely causes, and recommended next steps.**\n\n## Use when\n\nUse when the user needs structured insight from customer feedback rather than a raw summary.\n\nTypical cases:\n- summarizing product reviews from marketplaces or app stores;\n- clustering repeated complaints;\n- identifying refund / return drivers;\n- extracting product strengths and buyer-loved features;\n- separating product quality issues from messaging or expectation mismatch;\n- turning review data into FAQ, copy, product, or support actions;\n- preparing a concise report for product, ops, CX, or marketing teams.\n\n## Do not use when\n\nDo not use this skill when:\n- the user only wants sentiment labels with no explanation;\n- the task is broad social listening across the public web rather than a defined feedback set;\n- there is too little review data to identify meaningful patterns;\n- the user wants rigorous statistical causality rather than directional pattern analysis;\n- the task is support ticket workflow automation rather than insight extraction.\n\n## Inputs\n\nAsk for the minimum useful analysis set:\n- review source(s)\n- product / service name\n- review text or feedback sample\n- date range, if relevant\n- market / platform, if relevant\n- whether focus should be on complaints, positives, refunds, retention, or all feedback\n- any business question to prioritize\n\n## Workflow\n\n### 1. Define the review set\nClarify what is being analyzed:\n- marketplace reviews\n- app reviews\n- support complaints\n- refund / return notes\n- post-purchase survey responses\n- social comments collected into a feedback set\n\n### 2. Normalize and cluster the feedback\nGroup feedback into useful bu"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn70fv0ehp50emedet9tx3fekd82pw3b\",\n  \"slug\": \"review-analysis\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1774511216626\n}"},{"path":"references/output-template.md","content":"# Review Analysis Output Template\n\n## Top patterns\n1. \n2. \n3. \n\n## Evidence snippets\n- \n- \n\n## Likely root causes\n- \n\n## Recommended actions\n- Fix now:\n- Monitor:\n- Messaging changes:"},{"path":"skill-card.md","content":"## Description:\n\nAnalyzes customer reviews, complaints, and feedback to identify repeated patterns, likely root causes, and prioritized actions.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[leooooooow](https://clawhub.ai/user/leooooooow)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal product, operations, customer experience, and marketing teams use this skill to turn defined review or feedback sets into ranked patterns, likely root causes, and next actions.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Review datasets may contain sensitive personal data.\n\nMitigation: Avoid providing sensitive personal data unless the user intends the agent to process it as part of the review analysis.\n\nRisk: Root-cause findings are directional pattern analysis rather than rigorous statistical causality.\n\nMitigation: Use the skill's confidence, frequency, severity, and evidence snippets to review recommendations before acting on them.\n\n## Reference(s):\n\n- [Review Analysis Output Template](references/output-template.md)\n- [ClawHub Skill Page](https://clawhub.ai/leooooooow/skills/review-analysis)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance]\n\n**Output Format:** [Markdown decision report]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Includes ranked patterns, short evidence snippets, likely root causes, severity or urgency, recommended actions, and optional positives worth amplifying.]\n\n## Skill Version(s):\n\n1.0.1 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Analyze customer reviews, complaints, and feedback to find repeat patterns, likely root causes, and action priorities. Use when teams need to cluster complai... Skill: Review Analysis Owner: leooooooow Summary: Analyze customer reviews, complaints, and feedback to find repeat patterns, likely root causes, and action priorities. Use when teams need to cluster complai... 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