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运维和基础设施external-servicegithub未标认证来源可访问许可证需确认审计未展示

response-raterReact 评估者

Agent Skill

response-rater 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

1,395

周安装

57

GitHub Stars

25

下载量

447
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:response-rater(React 评估者)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/response-rater
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill response-rater
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/oimiragieo/agent-studio --skill response-rater

简介

response-rater 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 它能协助 Agent 自动响应协作事件并生成结构化反馈。
  • 可通过 npx skills add 命令从指定 GitHub 仓库安装该技能。
  • 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

Response Rater Skill

Step 1: Define Rating Rubric

Use appropriate rubric for the content type:

For Plans:

DimensionWeightDescription
Completeness20%All required sections present
Feasibility20%Plan is realistic and achievable
Risk Mitigation20%Risks identified with mitigations
Agent Coverage20%Appropriate agents assigned
Integration20%Fits with existing systems

For Responses:

DimensionWeightDescription
Correctness25%Technically accurate
Completeness25%Addresses all requirements
Clarity25%Easy to understand
Actionability25%Provides clear next steps

Step 2: Evaluate Each Dimension

Score each dimension 1-10:

## Dimension Scores

### Completeness: 8/10

- Has objectives, steps, and timeline
- Missing risk assessment section

### Feasibility: 7/10

- Most steps are achievable
- Step 3 timeline is aggressive

### Risk Mitigation: 5/10

- Only 1 risk identified
- No mitigation strategies

### Agent Coverage: 9/10

- All steps have assigned agents
- Good agent-task matching

### Integration: 8/10

- Uses existing APIs
- Minor compatibility concerns

Step 3: Calculate Overall Score

Average the weighted scores:

Overall = (8×0.2) + (7×0.2) + (5×0.2) + (9×0.2) + (8×0.2) = 7.4/10

Step 4: Generate Recommendations

Provide actionable improvements:

## Recommendations

### High Priority

1. Add risk assessment section with 3-5 risks
2. Include mitigation strategies for each risk

### Medium Priority

3. Extend Step 3 timeline by 2 days
4. Add fallback plan for external API dependency

### Low Priority

5. Add success metrics for each step

Step 5: Make Pass/Fail Decision

Apply minimum score thresholds:

Task TypeMinimum Score
Standard7/10
Enterprise8/10
Critical9/10
## Decision

**Score**: 7.4/10
**Threshold**: 7/10 (Standard)
**Result**: PASS ✅

Plan approved with recommendations for improvement.

</execution_process>

<best_practices>

  1. Consistent Rubrics: Use same dimensions for comparable content
  2. Clear Thresholds: Define pass/fail criteria upfront
  3. Actionable Feedback: Provide specific improvements
  4. Prioritize Issues: Order recommendations by impact
  5. Document Rationale: Explain score justifications

</best_practices>

Rate this plan for user authentication implementation

Response:

## Plan Rating: User Authentication

### Scores

- Completeness: 8/10 - Good structure, missing tests section
- Feasibility: 9/10 - Realistic timeline and approach
- Risk Mitigation: 6/10 - Only 1 risk identified
- Agent Coverage: 9/10 - Good agent assignments
- Integration: 8/10 - Compatible with existing auth

### Overall: 8.0/10 ✅ PASS

### Top Recommendations

1. Add risk section with security and dependency risks
2. Include test plan for each authentication flow
3. Add rollback procedure for failed deployment

</usage_example>

Iron Laws

  1. ALWAYS use the same rubric dimensions when rating comparable content — inconsistent dimensions make scores meaningless and prevent valid comparison across sessions.
  2. NEVER issue a pass/fail decision without documenting score justification for each dimension — unjustified scores cannot be reviewed, challenged, or improved.
  3. ALWAYS apply defined minimum thresholds (7/10 standard, 8/10 enterprise, 9/10 critical) — ad-hoc thresholds produce inconsistent approval gates that erode trust in the rating system.
  4. NEVER provide vague recommendations — every recommendation must reference the specific dimension it addresses and state the concrete change required.
  5. ALWAYS prioritize recommendations by impact — high-priority items that would materially improve the score must be clearly distinguished from low-impact suggestions.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Using different rubric dimensions for comparable contentScores cannot be compared across sessions; the rating loses its evaluative valueAlways use the same rubric (plans rubric for plans, responses rubric for responses)
Omitting score justification for individual dimensionsScores without justification cannot be reviewed, verified, or acted uponDocument specific evidence for each dimension score (what was present, what was missing)
Setting thresholds arbitrarily per sessionInconsistent thresholds invalidate the pass/fail gate; teams lose confidence in approvalsAlways apply the defined thresholds: 7/10 standard, 8/10 enterprise, 9/10 critical
Providing vague recommendations ("improve quality", "add more detail")Vague feedback cannot be acted upon; no change results from the reviewReference the specific dimension, score gap, and required concrete change for each recommendation
Listing recommendations without priority orderingEqual-weight feedback causes raters to address low-impact items firstAlways order by impact: High (affects pass/fail threshold) before Medium before Low

Memory Protocol (MANDATORY)

Before starting:

cat .claude/context/memory/learnings.md

After completing:

  • New pattern -> .claude/context/memory/learnings.md
  • Issue found -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

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

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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

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

35.03%
按下载量换算157

Claude

28.37%
按下载量换算127

Cursor

17.71%
按下载量换算79

Gemini CLI

9.44%
按下载量换算42

安全审计

暂无安全审计结果可展示。

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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