learn

Extract patterns and propose amendments to constitution.md and prompt.md after feature completion.

96|5|Updated Jan 10, 2026
One-click install
npx skills add https://github.com/ArvorCo/Relentless --skill learn-arvorco
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: learn
Source: https://github.com/ArvorCo/Relentless/tree/main/.claude/skills/learn
Command: npx skills add https://github.com/ArvorCo/Relentless --skill learn-arvorco

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, and includes scripts (resource) components.

What problem does it solve?

This Skill captures and distills learnings from completed features to drive improvements in governance and prompting. It closes the loop after feature execution by formalizing lessons into actionable amendments.

Core Features & Use Cases

  • Learnings extraction: Identify patterns, costs, failures, and errors from a finished feature run.
  • Proposal generation: Convert learnings into constitutional or prompt-based amendments for future iterations.
  • Approval workflow: Save learning logs and surface proposals for human review before applying changes.

Quick Start

  • Run the extraction: bash .claude/skills/learn/scripts/extract-learnings.sh relentless/features/<feature-name>
  • Review and propagate approved proposals through constitution.md and prompt.md.

Frequently Asked Questions about learn

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

FAQPage Schema
How do I extract learnings from completed features to improve governance prompts?

To extract learnings from completed features, run the provided bash script on your feature directory to analyze progress.txt and prd.json, distilling patterns and failures into actionable amendment proposals for your governance files.

What is the process for turning feature run failures into constitution.md amendments?

Turning feature run failures into constitution.md amendments involves analyzing completed feature data to identify error patterns, then generating formal proposals that append governance rules to prevent similar issues in future iterations.

Do I need jq installed to run the learnings extraction script?

Yes, you need jq installed to run the learnings extraction script, as it is a required dependency for parsing the structured feature data needed to generate governance and prompt proposals.

How do I apply approved proposals to prompt.md after feature completion?

To apply approved proposals to prompt.md after feature completion, review the generated learning logs and manually propagate the accepted changes into your prompt.md file to refine future runs.

When should I run the learnings extraction workflow in my development cycle?

You should run the learnings extraction workflow immediately after feature completion, as it requires finished feature data to accurately capture costs and errors for iterative system improvement.