Deep-dive e-commerce review and VOC analysis skill that turns product reviews into actionable insights. Uses 22-dimension AI tagging to extract user personas, pain points, and real needs, then delivers a 15-chapter insight report and themed dashboards while surfacing optimization opportunities from negative reviews and winning traits from positive ones. Supports CSV as the primary input with optional SellerSprite enhancement and Feishu sync; not for keyword research or product-radar tasks.
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
Recommended based on tags and category
Reduces AIGC detection rates and AI-like tone in academic papers when using Claude Code and Codex. Based on detectors such as 知网, 万方, PaperPass, and PaperPure, it applies scanning, deterministic replacement, sentence-level restructuring, and Anti-AI auditing to cut templated AI traces while keeping academic register, facts, data, citations, and terminology consistent. Use when the user asks to lower AI tone, reduce AIGC rates, avoid AI detection, reduce false-positive AI similarity, make writing sound more human, or mentions 'remove AI tone', 'reduce similarity', or 'AIGC check'.
Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. (gstack)
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).