px-retrospect

Analyze plans, ideas, and git history to generate structured learning files.

22|3|Updated Feb 22, 2026
One-click install
npx skills add https://github.com/DFilipeS/praxis --skill px-retrospect
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: px-retrospect
Source: https://github.com/DFilipeS/praxis/tree/main/praxis/skills/px-retrospect
Command: npx skills add https://github.com/DFilipeS/praxis --skill px-retrospect

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This retrospective helps teams identify actionable learnings from completed work to prevent repeated mistakes and to reinforce patterns that improve future AI-workflow iterations.

Core Features & Use Cases

  • Load and analyze the completed work from plan and idea files, plus relevant Git history, to surface gaps, decisions, and outcomes.
  • Extract patterns, anti-patterns, and opportunities, then translate them into concrete learnings that guide future cycles.
  • Generate structured learning files and place them under the .ai-workflow/learnings directory, linking each learning to related plans and ideas for traceability.

Quick Start

Provide the path to the completed plan file(s) or idea to analyze and start the retrospective.

Frequently Asked Questions about px-retrospect

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

FAQPage Schema
How do I run a retrospective analysis on completed AI workflows?

To run a retrospective analysis on completed AI workflows, provide the path to your completed plan or idea files. The skill analyzes this content alongside git history to surface gaps and decisions, then generates structured learning files.

What is the best way to extract actionable learnings from git history and plan files?

The best way to extract actionable learnings is to analyze completed plans, ideas, and git history to identify patterns and anti-patterns. This process translates past outcomes into concrete learnings that prevent repeated mistakes in future cycles.

Can I link generated learning files directly to related plans and ideas?

Yes, you can link generated learning files to related plans and ideas. The skill automatically establishes traceability by placing structured learning documents under the .ai-workflow/learnings directory and connecting them to their source materials.

How do retrospective learnings help prevent repeated mistakes in future AI-workflow iterations?

Retrospective learnings prevent repeated mistakes by capturing patterns, anti-patterns, and opportunities from completed work. These structured files guide future AI-workflow iterations, reinforcing successful patterns and closing identified gaps.

Does this retrospective analysis require any specific dependencies or components to run?

No specific dependencies or components are required to run this retrospective analysis. You simply need your completed plan files, idea documents, and accessible git history to begin surfacing actionable learnings.

When should I avoid using automated learning extraction for institutional knowledge?

You should avoid automated learning extraction when completed work lacks sufficient plan files, idea documents, or git history to analyze. Without these source materials, the skill cannot accurately surface patterns or generate meaningful institutional knowledge.