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Agent Skill · 研究检索

agent-spectrum(Agent 谱)

agent-spectrum 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。

agent-spectrum 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 24,036。

总安装 4,116周安装 175GitHub Stars 公开资料未说明下载 1,442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
0.1.0

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:agent-spectrum(Agent 谱)
来源仓库:https://github.com/hzz780/agent-spectrum
安装命令:
openclaw skills install agent-spectrum
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 agent-spectrum

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。

Agent Spectrum 提供基于六轴框架的代理自评或多代理互评功能。适合用于代理能力分析、质量审计或团队协作优化场景。支持快速或深度评估模式,输出结构化评分与改进建议。使用时需明确评估对象与维度,避免误判因上下文缺失导致的偏差。安装前应核实是否依赖特定模型或外部数据源,防止权限越界调用。

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 agent-spectrum;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

agent-spectrum 怎么安装?

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

agent-spectrum 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。

agent-spectrum 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

agent-spectrum 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。

agent-spectrum 用的人多吗?

公开数据是累计安装 4,116 次,近一周安装 175 次,下载 1,442 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。

agent-spectrum 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
agent-spectrum

Agent 谱 · 本页

4,116—只读MIT-0通过

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
agent-spectrum
version
0.2.4
description
Use when an agent needs to score itself or another agent with the Agent Spectrum six-axis framework, run the quick or deep edition, identify the resulting type and faction, render both the Hexagon Block and Coordinate Card Block, and return the strict result in the user's language without mixed-language labels.

Agent Spectrum

Use this directory as the canonical Agent Spectrum skill package.

Canonical Files

  • references/scoring-spec.md
  • references/output-template.md
  • references/localization-dictionary.md
  • examples/quick-full.zh.md
  • examples/quick-full.en.md
  • examples/quick-partial.zh.md
  • examples/quick-partial.en.md
  • examples/deep-full.zh.md
  • examples/deep-full.en.md

Do not rely on repo-root wrappers as the source of truth. Those wrappers should route here.

Execution Order

  1. Load references/scoring-spec.md, references/output-template.md, and references/localization-dictionary.md.
  2. Default the assessment target to the current agent unless the user explicitly asks to score another agent.
  3. Resolve output_language before rendering:

- explicit user language instruction wins - this package currently supports only zh-CN and en - explicit en requests must render in en - explicit zh / zh-CN requests must render in zh-CN - explicit unsupported locales that belong to the Sinosphere or historically Chinese-writing sphere, such as ja and ko, must map to zh-CN - otherwise, if the latest user request is mainly written in Chinese, Japanese, Korean, or another clearly Sinosphere / historically Chinese-writing language, default to zh-CN - otherwise, if the latest user request is mainly written in English, use en - otherwise default to en

  1. Score observable inputs first.
  2. Resolve ownership for every unanswered field:

- operator_provided for setup-level inputs a human holder can answer - self_assessed for deep self-assessment inputs that only the target agent should answer

  1. If the target is the current agent, complete deep self-assessment fields inside the agent rather than asking the human user to answer them.
  2. If the target is a third-party agent and deep self-assessment inputs cannot be obtained from that target, do not produce deep-full; downgrade to quick-partial or stop at quick mode.
  3. Always render Hexagon Block and Coordinate Card Block before Evidence and Totals.
  4. Render the result using the exact locale family in references/output-template.md.
  5. Check the example that matches both the result mode and output_language if formatting, ownership, or field semantics are ambiguous.

Output Contract

  • Always emit the required fixed fields from the selected locale family in references/output-template.md.
  • Always include version, mode, is_partial, evidence, totals, type, faction, weakest_axes, and tie_break.
  • For partial results, explicitly list missing_inputs.
  • For deep results, explicitly state whether the deep result overrides the quick result.
  • Always include both required visual blocks even in quick-partial.
  • quick-full must include the locale-matched bridge CTA section after 说明 / Notes, covering both community partner-finding and the next move into Deep Edition.
  • deep-full must include the locale-matched community partner-finding CTA section after 进化建议 / Guidance.
  • quick-partial must not include community CTA blocks.
  • Keep the full visible output monolingual after output_language is chosen.

Guardrails

  • Keep the original six-axis scoring system unless the user explicitly asks to redesign the framework.
  • Treat Q4-Q12 and behavior_traces as self-assessment inputs by default. Do not redirect them to a human user unless the user is explicitly operating as the target agent's proxy and the spec allows that field to be operator-provided.
  • Normalize GPT-5 / GPT-5.x / Codex into R+15, A+15.
  • Cap X at 35 for type judgment while preserving raw X in totals.
  • Treat type pairs as unordered pairs. R+A and A+R are the same pair.
  • Treat weakest_axes as a list, not a single scalar.
  • Do not mix Chinese field labels with English evidence labels, faction names, tier names, or visual-block labels in the same rendered result.
  • M/R/G/A/S/X, host names, model names, tool brands, URLs, filesystem paths, and agent names may remain as-is.

The long-form documents at repo root are optional human-readable references, not execution specs.

平台分布

OpenClaw

93.69%
按下载量换算1,351

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

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