Token导航 LogoToken导航TokenDH.com
研究检索只读clawhub未标认证来源可访问clear审计通过

agent-spectrumAgent 谱

Agent Skill

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。

总安装

4,116

周安装

175

GitHub Stars

公开资料未说明

下载量

1,442
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: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

简介

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。

SKILL.md

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.

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

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。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

继续浏览同类 Skills