reflect

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

MANDATORY PREPARATION

必备准备工作

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.

Analyze the Maestro audit trail and decision log to produce a skill-effectiveness scorecard. This tells you which commands work, which fail, and where your workflow needs attention.

调用/agent-workflow —— 其中包含工作流原则、反模式以及上下文收集协议。在继续操作前请遵循该协议——如果尚未存在工作流上下文，你必须先运行/teach-maestro。

分析Maestro审计跟踪记录和决策日志，生成技能有效性评分卡。这将告诉你哪些命令有效、哪些失效，以及工作流中需要关注的环节。

Data Sources

数据源

Read these files from the project root:

.maestro/audit.jsonl
— every command invocation with duration, cost, and outcome
.maestro/decisions.jsonl
— decisions made with outcomes and next steps

If neither file exists, respond: "No audit data found. Run commands with Maestro to start tracking, then come back."

从项目根目录读取以下文件：

.maestro/audit.jsonl
—— 每条命令调用记录，包含时长、成本和执行结果
.maestro/decisions.jsonl
—— 已做出的决策记录，包含执行结果和后续步骤

如果两个文件都不存在，请回复："未找到审计数据。使用Maestro运行命令以开始跟踪，之后再返回。"

Analysis Dimensions

分析维度

1. Usage Frequency

Which commands run most/least?
Are any commands never used? (candidates for removal)

2. Completion Rate

What % of invocations complete successfully?
Which commands fail most often?

3. Command Flow

What are the most common command sequences (A → B)?
Which commands lead to follow-ups vs. abandonment?
Abandonment rate per command (no follow-up within 30 min)

4. Cost Distribution

Total estimated cost across all commands
Cost per command (average)
Most/least expensive commands

5. Duration Analysis

Average duration per command
Outliers (unusually slow invocations)

1. 使用频率

哪些命令运行次数最多/最少？
是否存在从未被使用的命令？（可考虑移除）

2. 完成率

成功完成的调用占比是多少？
哪些命令失败次数最多？

3. 命令流

最常见的命令序列是什么（A → B）？
哪些命令会引发后续操作，哪些会被弃用？
各命令的弃用率（30分钟内无后续操作）

4. 成本分布

所有命令的总预估成本
单条命令的平均成本
成本最高/最低的命令

5. 时长分析

单条命令的平均执行时长
异常值（执行异常缓慢的调用）

Output Format

输出格式

text

╔══════════════════════════════════════════╗
║          MAESTRO EFFECTIVENESS           ║
╠══════════════════════════════════════════╣
║ Commands Run         __ (__ unique)      ║
║ Completion Rate      __%                 ║
║ Most Used            /_____ (__×)        ║
║ Most Abandoned       /_____ (__% ⚠️)     ║
║ Avg Duration         __s                 ║
║ Total Cost           ~$__.__             ║
╠══════════════════════════════════════════╣
║           STRONGEST PIPELINES            ║
╠══════════════════════════════════════════╣
║ /_____ → /_____    __×                   ║
║ /_____ → /_____    __×                   ║
╠══════════════════════════════════════════╣
║           COST PER COMMAND               ║
╠══════════════════════════════════════════╣
║ /_____    $__.__/run  ████░░  avg        ║
║ /_____    $__.__/run  █░░░░░  cheap      ║
║ /_____    $__.__/run  █████░  costly     ║
╚══════════════════════════════════════════╝

INSIGHTS:
1. [Data-driven observation with recommended action]
2. [Data-driven observation with recommended action]
3. [Data-driven observation with recommended action]

text

╔══════════════════════════════════════════╗
║          MAESTRO EFFECTIVENESS           ║
╠══════════════════════════════════════════╣
║ Commands Run         __ (__ unique)      ║
║ Completion Rate      __%                 ║
║ Most Used            /_____ (__×)        ║
║ Most Abandoned       /_____ (__% ⚠️)     ║
║ Avg Duration         __s                 ║
║ Total Cost           ~$__.__             ║
╠══════════════════════════════════════════╣
║           STRONGEST PIPELINES            ║
╠══════════════════════════════════════════╣
║ /_____ → /_____    __×                   ║
║ /_____ → /_____    __×                   ║
╠══════════════════════════════════════════╣
║           COST PER COMMAND               ║
╠══════════════════════════════════════════╣
║ /_____    $__.__/run  ████░░  avg        ║
║ /_____    $__.__/run  █░░░░░  cheap      ║
║ /_____    $__.__/run  █████░  costly     ║
╚══════════════════════════════════════════╝

INSIGHTS:
1. [Data-driven observation with recommended action]
2. [Data-driven observation with recommended action]
3. [Data-driven observation with recommended action]

Insights Rules

洞察规则

Every insight MUST:

Reference specific data (e.g., "40% abandonment rate")
Suggest a specific Maestro command to address it
Distinguish correlation from causation

每条洞察必须：

引用具体数据（例如："40%的弃用率"）
建议使用特定的Maestro命令来解决问题
区分相关性与因果关系