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continuous-learning

Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

Skill by vibeeval

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/vibeeval-continuous-learning

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About

Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

Use cases: agent-building

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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Runs as
Shell scripts

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/continuous-learning

What's inside

Skill name
continuous-learning
Description
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
License
MIT
Instructions
107 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (3)

  • SKILL.md
  • config.json
  • evaluate-session.sh

SKILL.md (first lines)

---
name: continuous-learning
description: Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
---

# Continuous Learning Skill

Automatically evaluates Claude Code sessions on end to extract reusable patterns that can be saved as learned skills.

## How It Works

This skill runs as a **Stop hook** at the end of each session:

1. **Session Evaluation**: Checks if session has enough messages (default: 10+)
2. **Pattern Detection**: Identifies extractable patterns from the session
3. **Skill Extraction**: Saves useful patterns to `~/.claude/skills/learned/`

## Configuration

Edit `config.json` to customize:

```json
{
  "min_session_length": 10,
  "extraction_threshold": "medium",
  "auto_approve": false,
  "learned_skills_path": "~/.claude/skills/learned/",
  "patterns_to_detect": [
    "error_resolution",
    "user_corrections",
    "workarounds",
    "debugging_techniques",
    "project_specific"
  ],
  "ignore_patterns": [
    "simple_typos",
    "one_time_fixes",
    "external_api_issues"
  ]
}
```

## Pattern Types

| Pattern | Description |
|---------|-------------|
| `error_resolution` | How specific errors were resolved |
| `user_corrections` | Patterns from user corrections |
| `workarounds` | Solutions to framework/library quirks |
| `debugging_techniques` | Effective debugging approaches |
| `project_specific` | Project-specific conventions |

## Hook Setup

Add to your `~/.claude/settings.json`:

```json
{
  "hooks": {
    "Stop": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
      }]
    }]
  }
}
```

## Why Stop Hook?

- **Lightweight**: Runs once at session end
- **Non-blocking**: Doesn't add latency to every message
- **Complete context**: Has access to full session transcript

## Related

- [The Longform Guide](https://x.com/affaanmustafa/status/2014040193557471352) - Section on continuous learning
- `/learn` command - Manual pattern extraction mid-session

---

## Comparison Notes (Research: Jan 2025)

### vs Homunculus (github.com/humanplane/homunculus)

Homunculus v2 takes a more sophisticated approach:

| Feature | Our Approach | Homunculus v2 |
|---------|--------------|---------------|
| Observation | Stop hook (end of session) | PreToolUse/PostToolUse hooks (100% reliable) |
| Analysis | Main context | Background agent (Haiku) |
| Granularity | Full skills | Atomic "instincts" |
| Confidence | None | 0.3-0.9 weighted |
| Evolution | Direct to skill | Instincts → cluster → skill/command/agent |
| Sharing | None | Export/import instincts |

**Key insight from homunculus:**
> "v1 relied on skills to observe. Skills are probabilistic—they fire ~50-80% of the time. v2 uses hooks for observation (100% reliable) and instincts as the atomic unit of learned behavior."

### Potential v2 Enhancements

1. **Instinct-based learning** - Smaller, atomic behaviors with confidence scoring
2. **Background observer** - Haiku agent analyzing in parallel
3. **Confidence decay** - Instincts lose confidence if contradicted
4. **Domain tagging** - code-style, testing, git, debugging, etc.
5. **Evolution path** - Cluster related instincts into skills/commands

See the continuous-learning-v2 spec for full details.

From vibeeval/vibecosystem, MIT.

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Stars
540
Forks
44
Open issues
5
Last commit (this folder)
Mar 23, 2026 (7 months ago)
Last release
Apr 12, 2026 (6 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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continuous-learning skill. Agent Mart. https://agentmart.dev/skills/vibeeval-continuous-learning (last updated 2026-10-24)