automated-learning

Analyze automated task outcomes and generate routing patches for continuous improvement.

3|Updated Jun 12, 2026

Search Tags:#continuous improvement #task analysis #skill maintenance #automated learning #run log analysis

Ready to run

npx skills add https://github.com/weebcoder101/dreamcode --skill automated-learning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: automated-learning
Source: https://github.com/weebcoder101/dreamcode/tree/main/.dreamcode/skills/automated-learning
Command: npx skills add https://github.com/weebcoder101/dreamcode --skill automated-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill addresses the need for continuous improvement in automated processes by capturing learnings from each execution, generating routing patches, and maintaining skill registry hygiene.

Core Features & Use Cases

Post-Run Analysis: Captures success and failure signals to improve future runs.
Routing Patches: Creates plain English patches for routing adjustments.
Skill Registry Hygiene: Ensures skill configuration integrity.
Use Case: After running a complex task, the skill generates a learning note detailing what went well, what failed, and what to change, which can be pasted into the run log for continuous process improvement.

Quick Start

Run the automated-learning skill after each execution to capture insights and improve future tasks.

Frequently Asked Questions about automated-learning

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

FAQPage Schema

How do I analyze automated task outcomes for continuous improvement?▼

Continuous improvement of automated tasks requires capturing success and failure signals from post-run analysis. This skill processes execution outcomes to generate learning notes detailing what went well, what failed, and necessary routing adjustments for future runs.

How do I generate routing patches after an automated run fails?▼

Routing patches are generated by analyzing failed run logs to identify necessary workflow adjustments. This skill creates plain English patches for routing configurations based on captured execution signals, ensuring future automated tasks route correctly.

How does run log analysis work for skill maintenance?▼

Run log analysis for skill maintenance works by capturing insights from each task execution to inform continuous improvement. The skill identifies what went well and what failed, producing actionable learning notes that can be pasted into the run log.

Can I use post-run analysis to verify skill configuration integrity?▼

Post-run analysis can verify skill configuration integrity by maintaining skill registry hygiene. This skill ensures your configurations remain valid after execution, capturing learnings from both successful and failed runs to prevent future misconfigurations.

When do I need to run post-task analysis on my automated workflows?▼

Post-task analysis is needed after executing complex automated tasks to capture execution insights. Running this skill after each task allows you to document what went well, what failed, and required changes, enabling continuous process improvement.

What is the best way to capture learning notes from failed automated tasks?▼

The best way to capture learning notes from failed automated tasks is through structured post-run analysis. This skill captures failure signals, generates detailed notes on what to change, and produces routing patches to improve future executions.