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skill-analytics技能分析

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

5,880

周安装

250

GitHub Stars

1

下载量

2,060
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-analytics(技能分析)
来源仓库:https://github.com/tommot2/skill-analytics
安装命令:
openclaw skills install skill-analytics
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install skill-analytics

简介

用于辅助数据整理、表格处理和指标计算,适合让 Agent 清洗字段、汇总数据或生成统计说明。

  • 适用于 CSV/Excel 分析、异常发现、图表准备等研究检索类任务。
  • 使用时需确认数据来源、字段含义和时间范围,避免将样本当作全量事实。
  • 涉及敏感数据或批量写回时,应先确认权限和脱敏边界。
  • skill-analytics 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
skill-analytics
description
Monitor ClawHub skill performance with file-based state tracking. Fetches public stats via web_fetch, tracks recommendations and their outcomes, avoids repetitive suggestions. Day-of-week rotation for varied analysis focus. Use when: (1) cron fires daily analytics, (2) user asks about skill performance, (3) adoption strategy or growth advice, (4) 'how is my skill doing', 'skill stats'. Homepage: https://clawhub.ai/skills/skill-analytics

Skill Analytics v2.0

Install: clawhub install skill-analytics

ClawHub skill portfolio monitoring with state tracking. Remembers what it recommended.

Language

Detect from user's message language. Default: English.

State Files

All state stored in memory/skill-analytics/:

FilePurpose
state.jsonRotation day, last run, recommendation IDs
recommendations.mdActive recommendations with status
ideas-tried.mdTopics already covered (avoid repeats)

Create directory and files on first run if they don't exist.

Day-of-Week Rotation

Use day-of-week (Monday=1, Sunday=7) from state.json (not current calendar day — track continuously):

DayFocus
1Adoption Funnel
2Competitive Analysis
3Content & Copy
4Feature Gap
5Monetization
6Cross-Promotion
7Wildcard

After each run: increment day in state.json, wrap at 7.

Anti-Repetition Protocol

Before generating recommendations:

  1. Read memory/skill-analytics/recommendations.md
  2. Read memory/skill-analytics/ideas-tried.md
  3. Skip any recommendation already listed as "Pending" or already tried
  4. Only generate NEW recommendations
  5. If no new insights exist: say "No new recommendations this run. Previous {N} are still pending."

Recommendation Format

| # | Recommendation | Date | Status | Result |
|---|---------------|------|--------|--------|
| 1 | **Short title** — one-line action | YYYY-MM-DD | Pending | - |

After user marks as done: change Status to "✅ Completed" with Result.

Data Collection

Use built-in tools only (web_fetch, web_search):

web_fetch https://clawhub.ai/tommot2/{slug}
web_search "clawhub {skill category}"

Extract: downloads, installs, stars, version count.

No CLI tools, no npm packages, no credentials.

Output Format

## 📊 Skill Analytics — {date}

### Dashboard
| Skill | DL | ⭐ | Versions |
|-------|---:|:--:|:--------:|
| ... | ... | ... | ... |

### Focus: {day focus}
{2-3 paragraphs of actual analysis. Concrete numbers.}

### New Recommendations
1. **{title}** — {one-line action}
   - Effekt: {estimated}
   - Innsats: {Lav/Middels/Høy}

### Previous Status
- Pending: {N} recommendations
- Completed: {N} recommendations
- Skipped (repeats): {N}

### Next Run
Focus: Day {N+1} — {focus}

Phase Indicator

Based on total installs across portfolio:

PhaseInstallsFocus
🌱 Seed0-10Visibility
🌿 Grow10-100Conversion
🌳 Scale100-1000Monetization
🏢 Sustain1000+Retention

Quick Commands

User saysAction
"skill stats"Quick dashboard
"skill analytics"Full analysis
"fulført #3"Mark recommendation #3 as completed
"alle anbefalinger"Show all with status

Guidelines for Agent

  1. Always read state before running — check recommendations and ideas-tried
  2. Write state after running — update state.json, add new recommendations
  3. Never repeat — check ideas-tried.md before suggesting
  4. Use built-in tools only — web_fetch and web_search
  5. No personal data in searches — only public ClawHub data
  6. Keep output concise — max 40 lines per report
  7. Language follows user

What This Skill Does NOT Do

  • Does NOT read MEMORY.md, SOUL.md, or other workspace files
  • Does NOT access credentials or private data
  • Does NOT use external CLI tools or npm packages
  • Does NOT modify any files outside memory/skill-analytics/

More by TommoT2

  • context-brief — Persistent context survival across sessions
  • setup-doctor — Diagnose and fix OpenClaw setup issues
  • tommo-skill-guard — Security scanner for installed skills

Install the full suite:

clawhub install skill-analytics context-brief setup-doctor tommo-skill-guard

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

94.56%
按下载量换算1,948

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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