Claude Code Continuous Learning Skill

Overview

Every Claude Code session starts fresh—no memory of past discoveries. The Continuous Learning Skill solves this by extracting non-obvious knowledge (debugging techniques, workarounds, project patterns) and saving it as new skills that load automatically in future sessions.

The architecture exploits Claude Code's native skill retrieval system. At startup, Claude loads skill names and descriptions (~100 tokens each). When your current context semantically matches a description, the full skill loads. This skill writes to that retrieval system, not just reads from it.

Key Features

Automatic extraction: Evaluates each session for knowledge worth preserving
Quality gates: Only extracts reusable, non-trivial, verified solutions
Searchable descriptions: Generated descriptions optimized for future semantic matching
Retrospective mode: Run /retrospective at session end to catch missed learnings

Architecture

flowchart LR
    Problem[Problem] --> Investigate[Investigation]
    Investigate --> Solution[Solution]
    Solution --> Eval{Worth
preserving?}
    Eval -->|Yes| Extract[Extract Skill]
    Eval -->|No| Done[End]
    Extract --> Library[(Skills Library)]
    Library -->|Future sessions| Match[Semantic Matching]
    Match --> Load[Auto-load Skill]

Code Snippets

Installation

git clone https://github.com/blader/claude-code-continuous-learning-skill.git \
  ~/.claude/skills/continuous-learning

Hook Setup

Create an activation hook that injects evaluation reminders:

mkdir -p ~/.claude/hooks
cp ~/.claude/skills/continuous-learning/scripts/continuous-learning-activator.sh \
   ~/.claude/hooks/
chmod +x ~/.claude/hooks/continuous-learning-activator.sh

Add to ~/.claude/settings.json:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/hooks/continuous-learning-activator.sh"
          }
        ]
      }
    ]
  }
}

Quality Gates

The system applies strict criteria before extraction:

Gate	Question
Reusable	Will this help with future tasks?
Non-trivial	Did this require discovery, not docs lookup?
Specific	Can you describe exact triggers and solutions?
Verified	Has the solution been tested and confirmed?

Extraction Triggers

Non-obvious debugging requiring >10 minutes investigation
Error resolution where root cause wasn't apparent
Workaround discovery through experimentation
Configuration insights differing from standard patterns

Academic Foundations

The approach draws from AI agent research:

Voyager (Wang et al., 2023): Game-playing agents building skill libraries
CASCADE (2024): Meta-skills—skills for acquiring skills
SEAgent (2025): Agents learning software environments through trial and error
Reflexion (Shinn et al., 2023): Self-reflection improving agent performance

Connections

claude-code-skills - Official Skills documentation that this tool extends with automatic generation
introducing-agent-skills-in-vs-code - Same portable skill concept applied to VS Code's agent ecosystem