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retrospective

Analyze completed AI factory session artifacts to identify inefficiencies and propose enhancements.

130|8|Updated Jan 23, 2026
Search Tags:#workflow#analysis#ai#retrospective#improvement#factory
One-click install
npx skills add https://github.com/sandgardenhq/sgai --skill retrospective-sandgardenhq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retrospective
Source: https://github.com/sandgardenhq/sgai/tree/main/cmd/sgai/skel/.sgai/skills/retrospective
Command: npx skills add https://github.com/sandgardenhq/sgai --skill retrospective-sandgardenhq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill analyzes completed AI‑factory sessions to uncover inefficiencies, quality gaps, and knowledge deficits, turning raw artifacts into concrete improvement actions.

Core Features & Use Cases

  • Artifact Mining: Reads state.json, session logs, GOAL.md, and other artifacts to build a comprehensive picture of what happened.
  • Pattern Detection: Identifies efficiency bottlenecks, quality issues, knowledge gaps, and process omissions across agents.
  • Actionable Proposals: Generates concrete suggestions for new skills, agent‑prompt tweaks, and AGENTS.md updates, and records them in SGAI_NOTES.md.
  • Use Case: After a workflow finishes, run this skill to automatically produce a retrospective report that guides future sessions toward higher speed and reliability.

Quick Start

Run the retrospective skill immediately after a session ends to receive improvement suggestions.

Frequently Asked Questions about retrospective

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

FAQPage Schema
How do I analyze completed AI factory sessions for inefficiencies and quality gaps?▼

You can analyze completed AI factory sessions by mining artifacts like state.json, session logs, and GOAL.md to detect efficiency bottlenecks and generate concrete improvement proposals in SGAI_NOTES.md.

What is the best way to turn raw workflow artifacts into actionable improvement plans?▼

Turning raw workflow artifacts into improvement plans involves detecting process omissions and knowledge gaps across agents, then recording structured observations and prompt tweak suggestions in SGAI_NOTES.md.

When do I need to run a retrospective on my AI agent workflow?▼

You need to run a retrospective immediately after a workflow finishes to automatically produce a report that identifies quality issues and guides future sessions toward higher speed and reliability.

Can I use this retrospective analysis to generate suggestions for new skills and AGENTS.md updates?▼

Yes, retrospective analysis can generate concrete suggestions for new skills, agent-prompt tweaks, and AGENTS.md updates by building a comprehensive picture of what happened during the session from its artifacts.

Do I need state.json and session logs to perform a post-completion workflow analysis?▼

Yes, performing post-completion workflow analysis requires reading state.json and session logs to identify efficiency bottlenecks, quality issues, and process omissions across agents for actionable enhancement proposals.

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