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SKILL.md

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name reflect
description Analyze command history to identify which skills work, which fail, and where to improve.
argument-hint [time period]
category analysis
version 2.0.0
user-invocable true

MANDATORY PREPARATION

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.

Data Sources

Read these files from the project root:

  1. .maestro/audit.jsonl — every command invocation with duration, cost, and outcome
  2. .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."

Analysis Dimensions

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)

Output Format

╔══════════════════════════════════════════╗
║          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

Every insight MUST:

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

Reflection Checklist

  • All 5 analysis dimensions covered
  • Scorecard generated with real data
  • Insights are data-driven, not speculative
  • Cost estimates labeled as approximate (~)
  • Recommended actions reference specific Maestro commands

Recommended Next Step

After reflecting, run /streamline to remove unused commands, or /refine on the most-abandoned command to improve its prompt quality.

NEVER:

  • Require audit data to exist — degrade gracefully
  • Invent metrics beyond what the logs contain
  • Show cost data without the "estimate" disclaimer (~)
  • Make judgments without evidence (say "100% completion rate" not "works great")
  • Compare across projects — reflect is project-scoped