skill-zju-real-easel-skill-comment-insights · by Install
$ agentstack add skill-zju-real-easel-skill-comment-insights ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-zju-real-easel-skill-comment-insights) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
评论区量化分析（comment-insights）
> 对评论做量化洞察：情感分布、高频词/短语、需求与吐槽挖掘。走 > scripts/comment_insights.py（jieba + SnowNLP + 社媒情感词典）。
> 和 skill-community-ops 的分工：community-ops 是"怎么回评论 + 危机应对"（运营动作）； > 本 SKILL 是"评论区在说什么"（量化数据）。评论抓取见 skill-xhs-analyzer。
⚠️ 依赖
pip install jieba snownlp（首次 jieba 会构建词典缓存）。
输入
评论数据，三种格式：
.txt：每行一条评论.json：字符串数组，或对象数组（--column指定字段，默认 content）.csv：--column指定评论列（默认首列/content）
输出（outputs/主题名/）
- 报告 JSON：情感分布/占比 + 正负代表评论 + 高频词 + 高频短语 + 需求/吐槽/提问计数与例子
- 终端可读摘要
执行
脚本路径（相对项目根）：skills/openclaw/skill-comment-insights/scripts/comment_insights.py。
python /scripts/comment_insights.py analyze -i comments.txt --top 20 \
-o outputs/主题名/report.json
# CSV 指定列
python /scripts/comment_insights.py analyze -i comments.csv --column 评论内容结果怎么用
- 情感占比：负面偏高 → 结合负面代表评论定位问题；配合 skill-community-ops 做回应/危机。
- 高频词/短语：用户关注点与话题；可喂 chart-visualization/infographic 出词云图。
- 需求（demands）：求链接/求教程/求同款 → 直接转化为下一条选题（配合 community-ops 选题反哺）。
- 吐槽（complaints）：产品/服务问题信号 → 复盘改进（配合 skill-content-postmortem）。
规则
- 情感为 SnowNLP 基线 + 社媒词典修正的近似值，用于看趋势与占比，非逐条精判；
关键决策需人工核对代表评论，或让 LLM 对存疑评论精读。
- 高频词已做词性过滤（保留名/动/形）+ 停用词剔除，聚焦有信息量的词。
- 数据量小（<20 条）时占比参考意义有限，如实说明样本量。
- 只做分析不做回复；回复/危机用 skill-community-ops。
参考来源
沿用社媒评论分析常用组合：jieba 中文分词（#131 高频词）+ SnowNLP 情感（#130），并叠加 社媒情感词典（绝绝子/yyds/避雷/翻车等网络用语）修正 SnowNLP 在社交文本上的偏差。诉求挖掘 用规则匹配（求购/疑问/吐槽），确定可复现。
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: ZJU-REAL
- Source: ZJU-REAL/Easel
- License: Apache-2.0
- Homepage: https://zju-real.github.io/Easel/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.