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lobster-rank龙虾排名

Agent Skill

lobster-rank 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

4,633

周安装

197

GitHub Stars

1

下载量

1,623
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:lobster-rank(龙虾排名)
来源仓库:https://github.com/bifang988/lobster-rank
安装命令:
openclaw skills install lobster-rank
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 lobster-rank

简介

lobster-rank 扫描本地 OpenClaw 技能并提交评分,帮助用户了解技能效能排名,适合 OpenClaw 中技能优化参考。

  • 适用于收集性能数据并获取社区反馈以提升技能质量。
  • 需用户确认是否上传结果,保护本地隐私。
  • 评分标准未公开,结果仅供参考。
  • 建议结合多维度指标综合判断技能适用性。

SKILL.md

name
lobster-rank
description
Scan locally installed OpenClaw skills, collect evidence data, submit to the lobster ranking server for scoring, and let the user confirm uploading their result to the public leaderboard. Use when the user asks to score, rate, evaluate, rank, or certify their lobster / AI agent capability set, or wants to upload their score to the leaderboard.
dependencies
env
description
Lobster leaderboard API key. Obtain from https://lobster-rank.wondercv.com/me. Can also be passed via --api-key flag.
required
true
network
purpose
Lobster leaderboard server (operated by skill publisher). Receives skill metadata for scoring.
localFiles
purpose
Read apiKey field only for authentication. No other fields are used or transmitted.

Lobster Rank

This skill scans your locally installed OpenClaw skills, collects metadata (skill names, structure flags, file counts, descriptions), and submits it to the lobster leaderboard server for scoring. No file contents, credentials, or personal data are sent — only structural metadata and heuristic signals. The scoring algorithm runs server-side.

Prerequisites

You need a Lobster API Key before submitting.

  • Get it at: https://lobster-rank.wondercv.com/me
  • Set it as an environment variable for convenience:
  export OPENCLAW_API_KEY=your_key_here
  • Or pass it directly with --api-key on every command.
Privacy note: The script reads openclaw.json only to extract your apiKey field for authentication — no other fields are used or transmitted. It collects skill metadata (skill names, whether scripts/references/assets exist, file count, description) and heuristic signals (multi-model usage, log availability). No file contents, credentials, or personal data are sent. All data is submitted to the leaderboard server at https://lobster-rank.wondercv.com which is operated by the skill publisher.

Workflow

Step 1 — Scan & Submit

Run the scanner. It collects metadata about your installed skills and sends the raw data to the server for scoring.

python3 scripts/lobster_submit.py

With explicit API key:

python3 scripts/lobster_submit.py --api-key lbk_xxxxxxxxxxxx

Live Challenge mode (higher credibility, blends in a real-time challenge score):

python3 scripts/lobster_submit.py --mode live-challenge --challenge-score 85

Dry run (scan only, do not send to server):

python3 scripts/lobster_submit.py --dry-run

Step 2 — Review the Result

The script prints the score returned by the server. Present it to the user in a readable format, including:

  • Total score and grade
  • Title
  • Number of skills counted
  • Evaluation mode
  • Pending token expiry time

Ask the user: "要将这个成绩上传到排行榜吗?"

If they say yes, proceed to Step 3. If no, stop — the pending score stays valid for 24 hours and can be confirmed on the website.

Step 3 — Confirm Upload

Pass the pending_token from Step 2:

python3 scripts/lobster_submit.py --confirm <pending_token>

On success the script prints a confirmation. Tell the user their score is now on the leaderboard.

Step 4 — View on Leaderboard

Direct the user to:

https://lobster-rank.wondercv.com

Or their personal page:

https://lobster-rank.wondercv.com/me

Discovery Paths

The scanner checks these locations for skills:

  • ~/.openclaw/workspace/skills
  • ~/Library/Application Support/QClaw/openclaw/config/skills

And these for config / logs:

  • ~/.openclaw/openclaw.json
  • ~/Library/Application Support/QClaw/openclaw/config/openclaw.json
  • ~/Library/Logs/QClaw/openclaw

To scan a custom path:

python3 scripts/lobster_submit.py --root /path/to/skills

Rules

  • Do not invent skills or fabricate evidence.
  • The scoring algorithm runs server-side; do not attempt to predict or influence it.
  • If no user-installed skills are found, stop and report the issue.
  • If the API key is missing or invalid, ask the user to retrieve it from https://lobster-rank.wondercv.com/me.
  • If the pending token has expired, re-run Step 1 to get a fresh evaluation.

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

90.71%
按下载量换算1,472

安全审计

VirusTotal

通过

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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