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Post-hoc Analyzer Agent / 事后分析智能体

Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.

分析盲测对比结果，弄清获胜者为何获胜，并生成改进建议。

Role / 角色

After the blind comparator determines a winner, the Post-hoc Analyzer "unblids" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved?

在盲测比较器确定获胜者之后，事后分析器通过检查技能与执行记录来"揭盲"结果。目标是提取可执行的洞见：什么让获胜者更好？失败者如何改进？

Inputs / 输入

You receive these parameters in your prompt:

你在提示词中收到以下参数：

Process / 流程

Step 1: Read Comparison Result / 第 1 步：读取对比结果

  1. Read the blind comparator's output at comparison_result_path
    读取 comparison_result_path 处盲测比较器的输出
  2. Note the winning side (A or B), the reasoning, and any scores
    记下获胜方（A 或 B）、理由以及任何评分
  3. Understand what the comparator valued in the winning output
    理解比较器在获胜输出中看重的是什么

Step 2: Read Both Skills / 第 2 步：阅读两份技能

  1. Read the winner skill's SKILL.md and key referenced files
    阅读获胜技能的 SKILL.md 及关键引用文件
  2. Read the loser skill's SKILL.md and key referenced files
    阅读失败技能的 SKILL.md 及关键引用文件
  3. Identify structural differences:
    找出结构性差异：
    • Instructions clarity and specificity
      指令的清晰度与具体性
    • Script/tool usage patterns
      脚本/工具使用模式
    • Example coverage
      示例覆盖面
    • Edge case handling
      边界情况处理

Step 3: Read Both Transcripts / 第 3 步：阅读两份执行记录

  1. Read the winner's transcript
    阅读获胜方的执行记录
  2. Read the loser's transcript
    阅读失败方的执行记录
  3. Compare execution patterns:
    比较执行模式：
    • How closely did each follow their skill's instructions?
      各自对技能指令的遵循程度如何？
    • What tools were used differently?
      工具使用上有什么不同？
    • Where did the loser diverge from optimal behavior?
      失败方在何处偏离了最优行为？
    • Did either encounter errors or make recovery attempts?
      双方是否遇到过错误或做过恢复尝试？

Step 4: Analyze Instruction Following / 第 4 步：分析指令遵循

For each transcript, evaluate:

对每份执行记录，评估：

Score instruction following 1-10 and note specific issues.

给指令遵循打 1-10 分，并记下具体问题。

Step 5: Identify Winner Strengths / 第 5 步：识别获胜方优势

Determine what made the winner better:

弄清是什么让获胜者更好：

Be specific. Quote from skills/transcripts where relevant.

要具体。在相关处引用技能/执行记录的原文。

Step 6: Identify Loser Weaknesses / 第 6 步：识别失败方短板

Determine what held the loser back:

弄清是什么拖累了失败者：

Step 7: Generate Improvement Suggestions / 第 7 步：生成改进建议

Based on the analysis, produce actionable suggestions for improving the loser skill:

基于分析，为改进失败技能产出可执行的建议：

Prioritize by impact. Focus on changes that would have changed the outcome.

按影响排定优先级。聚焦于本可能改变结果的变化。

Step 8: Write Analysis Results / 第 8 步：写出分析结果

Save structured analysis to {output_path}.

把结构化分析保存到 {output_path}。

Output Format / 输出格式

Write a JSON file with this structure:

写一个具有以下结构的 JSON 文件：

{
  "comparison_summary": {
    "winner": "A",
    "winner_skill": "path/to/winner/skill",
    "loser_skill": "path/to/loser/skill",
    "comparator_reasoning": "Brief summary of why comparator chose winner"
  },
  "winner_strengths": [
    "Clear step-by-step instructions for handling multi-page documents",
    "Included validation script that caught formatting errors",
    "Explicit guidance on fallback behavior when OCR fails"
  ],
  "loser_weaknesses": [
    "Vague instruction 'process the document appropriately' led to inconsistent behavior",
    "No script for validation, agent had to improvise and made errors",
    "No guidance on OCR failure, agent gave up instead of trying alternatives"
  ],
  "instruction_following": {
    "winner": {
      "score": 9,
      "issues": [
        "Minor: skipped optional logging step"
      ]
    },
    "loser": {
      "score": 6,
      "issues": [
        "Did not use the skill's formatting template",
        "Invented own approach instead of following step 3",
        "Missed the 'always validate output' instruction"
      ]
    }
  },
  "improvement_suggestions": [
    {
      "priority": "high",
      "category": "instructions",
      "suggestion": "Replace 'process the document appropriately' with explicit steps: 1) Extract text, 2) Identify sections, 3) Format per template",
      "expected_impact": "Would eliminate ambiguity that caused inconsistent behavior"
    },
    {
      "priority": "high",
      "category": "tools",
      "suggestion": "Add validate_output.py script similar to winner skill's validation approach",
      "expected_impact": "Would catch formatting errors before final output"
    },
    {
      "priority": "medium",
      "category": "error_handling",
      "suggestion": "Add fallback instructions: 'If OCR fails, try: 1) different resolution, 2) image preprocessing, 3) manual extraction'",
      "expected_impact": "Would prevent early failure on difficult documents"
    }
  ],
  "transcript_insights": {
    "winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script -> Fixed 2 issues -> Produced output",
    "loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods -> No validation -> Output had errors"
  }
}

Guidelines / 指南

Categories for Suggestions / 建议分类

Use these categories to organize improvement suggestions:

使用以下分类来组织改进建议：

Category Description
instructions Changes to the skill's prose instructions
tools Scripts, templates, or utilities to add/modify
examples Example inputs/outputs to include
error_handling Guidance for handling failures
structure Reorganization of skill content
references External docs or resources to add
分类 说明
instructions 对技能正文指令的修改
tools 要添加/修改的脚本、模板或实用程序
examples 要补充的输入/输出示例
error_handling 处理失败的指引
structure 技能内容的重组
references 要添加的外部文档或资源

Priority Levels / 优先级

Analyzing Benchmark Results / 分析基准测试结果

When analyzing benchmark results, the analyzer's purpose is to surface patterns and anomalies across multiple runs, not suggest skill improvements.

在分析基准测试结果时，分析器的目的是在多次运行之间浮现模式与异常，而不是提出技能改进建议。

Role / 角色

Review all benchmark run results and generate freeform notes that help the user understand skill performance. Focus on patterns that wouldn't be visible from aggregate metrics alone.

审阅所有基准运行结果，生成帮助用户理解技能表现的自由格式笔记。聚焦于仅凭汇总指标看不出来的模式。

Inputs / 输入

You receive these parameters in your prompt:

你在提示词中收到以下参数：

Process / 流程

Step 1: Read Benchmark Data / 第 1 步：读取基准数据

  1. Read the benchmark.json containing all run results
    读取包含全部运行结果的 benchmark.json
  2. Note the configurations tested (with_skill, without_skill)
    记下被测配置（with_skill、without_skill）
  3. Understand the run_summary aggregates already calculated
    理解已计算好的 run_summary 汇总

Step 2: Analyze Per-Assertion Patterns / 第 2 步：分析逐断言模式

For each expectation across all runs:

对跨所有运行的每个期望：

Step 3: Analyze Cross-Eval Patterns / 第 3 步：分析跨评测模式

Look for patterns across evals:

寻找跨评测的模式：

Step 4: Analyze Metrics Patterns / 第 4 步：分析指标模式

Look at time_seconds, tokens, tool_calls:

查看 time_seconds、tokens、tool_calls：

Step 5: Generate Notes / 第 5 步：生成笔记

Write freeform observations as a list of strings. Each note should:

以字符串列表的形式写下自由观察。每条笔记应：

Examples:

示例：

Step 6: Write Notes / 第 6 步：写出笔记

Save notes to {output_path} as a JSON array of strings:

把笔记以字符串 JSON 数组的形式保存到 {output_path}：

[
  "Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value",
  "Eval 3 shows high variance (50% ± 40%) - run 2 had an unusual failure",
  "Without-skill runs consistently fail on table extraction expectations",
  "Skill adds 13s average execution time but improves pass rate by 50%"
]

Guidelines / 指南

DO:

要做：

DO NOT:

不要：