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community-skill-radar社区技能雷达

Agent Skill

community-skill-radar 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

294

周安装

12

GitHub Stars

58

下载量

94
CodexClaudeCursorGemini CLI

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

unknown

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:community-skill-radar(社区技能雷达)
来源仓库:https://github.com/archieindian/openclaw-superpowers
仓库路径:skills/community-skill-radar
安装命令:
npx skills add https://github.com/archieindian/openclaw-superpowers --skill community-skill-radar
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/archieindian/openclaw-superpowers --skill community-skill-radar

简介

community-skill-radar 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 每三天自动扫描 Reddit 上关于 OpenClaw 痛点和技能缺口的帖子评论,识别高优先级提案。
  • 基于信号强度(点赞数、评论深度、重复频率)对提案进行评分,生成 PROPOSALS.md 文件供审阅决策。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Community Skill Radar

What it does

Your best skill ideas don't come from guessing — they come from what the community is actually struggling with. Community Skill Radar scans Reddit every 3 days for posts and comments mentioning OpenClaw pain points, feature requests, and skill gaps. It scores them by signal strength (upvotes, comment depth, recurrence) and writes a prioritized PROPOSALS.md in the repo root.

You review the proposals. You decide what to build. The radar just makes sure you never miss a signal.

When to invoke

  • Automatically, every 3 days (cron)
  • Manually when you want a fresh pulse-check on community needs
  • Before planning a new batch of skills

Subreddits searched

SubredditWhy
openclawPrimary OpenClaw community
LocalLLaMALocal AI users — many run OpenClaw
ClaudeAIClaude ecosystem — overlaps with OpenClaw users
MachineLearningBroader AI practitioners
AIAgentsAgent-specific discussions

Custom subreddits can be configured via --subreddits.

Signal scoring

Each candidate is scored on 5 dimensions:

SignalWeightSource
Upvotes2xPost/comment score
Comment depth1.5xNumber of replies — more discussion = stronger signal
Recurrence3xSame pain point appearing across multiple posts
Keyword density1xConcentration of problem/request keywords
Recency1.5xNewer posts score higher (7-day decay)

How to use

python3 radar.py --scan                     # Full scan, write PROPOSALS.md
python3 radar.py --scan --lookback 7        # Scan last 7 days (default: 3)
python3 radar.py --scan --subreddits openclaw,LocalLLaMA
python3 radar.py --scan --min-score 5.0     # Only proposals scoring ≥5.0
python3 radar.py --status                   # Last scan summary from state
python3 radar.py --history                  # Show past scan results
python3 radar.py --format json              # Machine-readable output

Cron wakeup behaviour

Every 3 days at 9am:

  1. Fetch recent posts from each configured subreddit via Reddit's public JSON API (no auth required)
  2. Filter for posts/comments containing OpenClaw-related keywords
  3. Extract pain points and feature request signals
  4. Score each candidate
  5. Deduplicate against previously seen proposals (stored in state)
  6. Write PROPOSALS.md to the repo root
  7. Print summary to stdout

PROPOSALS.md format

# Skill Proposals — Community Radar

*Last scanned: 2026-03-16 09:00 | 5 subreddits | 14 candidates*

## High Signal (score ≥ 8.0)

### 1. Skill auto-update mechanism (score: 12.4)
- **Source:** r/openclaw — "Anyone else manually pulling skill updates?"
- **Signal:** 47 upvotes, 23 comments, seen 3 times across 2 subreddits
- **Pain point:** No way to update installed skills without manual git pull
- **Potential skill:** `skill-auto-updater` — checks upstream repos for new versions

### 2. Context window usage dashboard (score: 9.1)
- **Source:** r/LocalLLaMA — "My openclaw agent keeps losing context mid-task"
- **Signal:** 31 upvotes, 18 comments
- **Pain point:** No visibility into how much context each skill consumes
- **Potential skill:** `context-usage-dashboard` — real-time token budget display

## Medium Signal (score 4.0–8.0)

...

## Previously Seen (already in state — not re-proposed)

...

Procedure

Step 1 — Let the cron run (or trigger manually)

python3 radar.py --scan

Step 2 — Review PROPOSALS.md

Open PROPOSALS.md in the repo root. High-signal proposals are the ones the community is loudest about.

Step 3 — Act on proposals you want to build

For each proposal you decide to build, either:

  • Ask your agent to create it: "Build a skill for <pain point> using create-skill"
  • Open a GitHub issue for the community

Step 4 — Mark proposals as actioned

python3 radar.py --mark-actioned "skill-auto-updater"

This moves the proposal to the "actioned" list in state so it won't be re-proposed on future scans.

State

Scan results, seen proposals, and actioned items stored in ~/.openclaw/skill-state/community-skill-radar/state.yaml.

Fields: last_scan_at, subreddits, proposals list, actioned list, scan_history.

Notes

  • Uses Reddit's public JSON API at reddit.com/<subreddit>/search.json. No authentication required. Rate-limited to 1 request per 2 seconds to respect Reddit's guidelines.
  • Does not post, comment, or interact with Reddit in any way — read-only scanning.
  • PROPOSALS.md is gitignored by default (local working document). Add to .gitignore if not already present.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

37.92%
按下载量换算36

Claude

29.85%
按下载量换算28

Cursor

19.59%
按下载量换算18

Gemini CLI

8.58%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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