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Agent Skill · 研究检索

gauntletscore(gauntletscore 文档)

gauntletscore 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。

gauntletscore 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 14,296。

总安装 8,678周安装 358GitHub Stars 公开资料未说明下载 2,835核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
5.1.5

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:gauntletscore(gauntletscore 文档)
来源仓库:https://github.com/wmehobbs/gauntletscore
安装命令:
openclaw skills install gauntletscore
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 gauntletscore

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

gauntletscore 用于验证人工智能输出的可信度,确保文档和代码质量。适用于在采取行动前对 AI 生成内容进行安全审查的场景。提供多维度信任评估和风险等级划分功能支持。安装命令为 openclaw skills install gauntletscore,需确认验证规则配置。

建议结合人工审核流程提高最终决策准确性。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install gauntletscore;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

gauntletscore 怎么安装?

在终端执行 openclaw skills install gauntletscore。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

gauntletscore 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

gauntletscore 需要什么权限?

权限级别是接触敏感数据。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。

gauntletscore 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

gauntletscore 用的人多吗?

公开数据是累计安装 8,678 次,近一周安装 358 次,下载 2,835 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

gauntletscore 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
gauntletscore

gauntletscore 文档 · 本页

8,678—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
gauntletscore
version
5.1.5
description
Trust verification for AI output — verify any document or code before you act on it
author
genstrata
license
proprietary
homepage
https://gauntletscore.com
api_base
https://api.gauntletscore.com
tags
metadata
clawdbot
config
requiredEnv
example
|

GauntletScore — Trust Verification for AI Output

Verify any AI-generated document or code before you trust it. Seven AI personas from five independent model providers independently analyze your content, verify every checkable claim against authoritative sources, and produce a cryptographically signed trust score.

What It Does

Submit a document or code and get:

  • Gauntlet Score (0-100) with letter grade (A-F)
  • Claim-by-claim verification against CourtListener (legal), eCFR (regulatory), PubMed (scientific), EDGAR (SEC), and computational math verification
  • Code safety analysis detecting reverse shells, credential theft, prompt manipulation, data exfiltration, and obfuscated payloads
  • Unanimous vote from 7 independent AI agents (PROCEED / PROCEED WITH CONDITIONS / DO NOT PROCEED)
  • Cryptographic certificate proving the score is genuine and untampered
  • Full debate transcript showing every agent's reasoning

Quick Start

Free tier includes 3 analyses per month. Get an API key at gauntletscore.com.

Verify a document by pasting content:

POST https://api.gauntletscore.com/v1/analyze
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

{
  "document": "Your document text here...",
  "topic": "Verify the claims in this document"
}

Verify a ClawHub skill by URL:

POST https://api.gauntletscore.com/v1/analyze
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

{
  "source_url": "https://clawhub.ai/skills/gauntlet-validate/SKILL.md",
  "topic": "Evaluate the safety of this ClawHub skill before installation"
}

Check results:

GET https://api.gauntletscore.com/v1/jobs/{job_id}
Authorization: Bearer YOUR_API_KEY

Results include score, grade, vote, verified claims, and a cryptographic certificate.

What It Catches

In documents:

  • Fabricated legal citations (hallucinated case law)
  • Misapplied regulations (wrong CFR section for the situation)
  • Mathematical errors (wrong totals, incorrect percentages)
  • Internal contradictions
  • Unsupported conclusions

In code:

  • Reverse shells and remote code execution
  • Credential theft and data exfiltration
  • Download-and-execute attacks (curl | bash)
  • Prompt manipulation attempts (instructions designed to override agent behavior)
  • Documentation-vs-behavior mismatches (code does things the README doesn't mention)
  • Dangerous operation combinations that pass individual checks

How It Works

Seven AI personas from five independent model providers independently analyze your submission:

  1. Round 0 — Each agent conducts independent research, verifying claims against authoritative databases
  2. Rounds 1-3 — Structured adversarial debate where agents challenge each other's findings
  3. Round 4 — Final positions and votes
  4. Knowledge Graph — Every verified and debunked claim is stored in a persistent knowledge graph. Subsequent analyses benefit from prior verifications, reducing cost and increasing accuracy over time.
  5. Bayesian Calibration — Confidence scores are computed using Bayesian inference across multiple evidence sources, not simple vote counting. The score reflects calibrated probability, not consensus.
  6. Scoring — Six-component rubric produces the Gauntlet Score
  7. Certification — Ed25519 cryptographic signature proves the result is genuine

All analysis is read-only. Submitted code is never executed. Documents are processed in memory and not stored.

Pricing

TierPriceCredits
Free$03 runs / month
Starter$295 analyses
Pro$7915 analyses
Business$14930 analyses
EnterpriseCustomUnlimited

One credit = one analysis, regardless of document length. No subscriptions. See gauntletscore.com/pricing for details. Sovereign Edition: sales@genstrata.com

Verify a Certificate

Anyone can verify a Gauntlet Score is genuine:

GET https://api.gauntletscore.com/v1/verify/{certificate_id}

No authentication required.

Links

About

GauntletScore is built by Genstrata, Inc. The Gauntlet's adversarial multi-agent verification architecture is patent-pending (USPTO #63/967,169).

For organizations that cannot send data to cloud services, the Sovereign Edition runs entirely on your hardware with zero data egress. Contact sales@genstrata.com.

平台分布

OpenClaw

85.99%
按下载量换算2,438

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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