retro

Aggregate git logs, memory files, and task metrics to evaluate agent performance.

1|Updated Apr 18, 2026
One-click install
npx skills add https://github.com/CC90210/CMO-Agent --skill retro-cc90210
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: retro
Source: https://github.com/CC90210/CMO-Agent/tree/main/skills/retro
Command: npx skills add https://github.com/CC90210/CMO-Agent --skill retro-cc90210

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of operational drift and lack of accountability by providing a structured, data-driven retrospective of all agent activities, ensuring that patterns of failure are identified and corrected before they compound.

Core Features & Use Cases

  • Multi-Agent Analysis: Aggregates git activity, memory logs, and task completion rates across all active agents.
  • Operational Scoring: Evaluates performance across four critical dimensions: Shipping Velocity, Code Quality, Memory Health, and Agent Coordination.
  • Improvement Pipeline: Automatically generates actionable tasks and updates system rules to prevent recurring mistakes.

Quick Start

Trigger the weekly retrospective analysis by typing retro in the chat interface to generate your performance report and improvement plan.

Frequently Asked Questions about retro

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate a weekly performance audit for multiple AI agents?

Automate a weekly performance audit by triggering the retrospective analysis to aggregate git logs, memory files, and task completion metrics across all active agents. It evaluates operational health and generates structured improvement actions.

What is the best way to run a data-driven retrospective on agent operational health?

Run a data-driven retrospective by analyzing weekly agent performance across shipping velocity, code quality, memory maintenance, and inter-agent coordination. This identifies failure patterns and updates system-wide rules to prevent recurring technical mistakes.

How do I analyze git logs and memory files to measure agent productivity?

Analyze git logs and memory files by aggregating activity across multiple repositories to evaluate agent productivity. The process audits task completion rates and memory health to ensure accountability and correct operational drift.

Can I automatically generate improvement tasks from a multi-agent performance analysis?

You can automatically generate improvement tasks from a multi-agent performance analysis. The retrospective evaluates aggregated metrics to create an improvement pipeline that updates system rules and prevents compounding process-related mistakes.

Does the weekly retrospective analysis work across multiple repositories?

The weekly retrospective analysis works across multiple repositories. It aggregates git activity, memory logs, and task completion metrics from various active agents to provide a comprehensive operational scoring and accountability check.