cc-skill-continuous-learning

Name: cc-skill-continuous-learning
Availability: InStock
Author: blockhead22

Extract reusable patterns from coding session transcripts into a local learning store.

Updated Aug 23, 2026

Search Tags:#pattern-detection #pattern-extraction #learned-skills #manual-review #coding-sessions #crt-memory #memory-grounding

Ready to run

npx skills add https://github.com/blockhead22/CRT-GroundCheck-SSE --skill cc-skill-continuous-learning-blockhead22

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cc-skill-continuous-learning
Source: https://github.com/blockhead22/CRT-GroundCheck-SSE/tree/main/.agents/skills/cc-skill-continuous-learning
Command: npx skills add https://github.com/blockhead22/CRT-GroundCheck-SSE --skill cc-skill-continuous-learning-blockhead22

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq.

What problem does it solve?

Automatically analyzes coding sessions to extract reusable patterns, debugging techniques, and project-specific knowledge, preserving lessons learned for future reuse.

Core Features & Use Cases

Automatically analyzes end-of-session transcripts to surface patterns like error resolution, debugging approaches, workarounds, and CRT memory concepts.
Saves concise session summaries to the local learning store for later review and approval.
Supports a configurable workflow with manual review to ensure only validated patterns are learned.

Quick Start

Run the stop-hook on your latest coding session to generate learnable patterns.

Frequently Asked Questions about cc-skill-continuous-learning

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

FAQPage Schema

How do I extract reusable coding patterns from session transcripts?▼

Extracting reusable coding patterns involves analyzing session transcripts to detect error resolutions, debugging approaches, and workarounds, then saving concise summaries for later review and approval.

What is CRT memory grounding in the context of learned coding skills?▼

CRT memory concepts in this context refer to preserving project-specific knowledge and debugging techniques extracted from coding sessions into a local learned-skills repository for future reuse.

How do I build a learned-skills repository from end-of-session analyses?▼

Build a learned-skills repository by running automated end-of-session analyses to surface reusable patterns, storing session summaries locally, and applying manual review guardrails to ensure only validated patterns are approved for learning.

Do I need jq to automate pattern detection from coding sessions?▼

Yes, jq is a required dependency for automating pattern detection from coding sessions, enabling the extraction workflow to parse and process session data into the local learning store.

Can I manually review extracted patterns before they are saved to the learning store?▼

Yes, the configurable workflow implements manual review guardrails, ensuring only approved patterns extracted from coding sessions are saved to the local learning store as learned skills.

What's the best way to preserve debugging workarounds across different coding projects?▼

Apply automated pattern extraction at the end of coding sessions across projects, saving approved debugging techniques and workarounds to a growing local learned-skills repository for future cross-project reuse.