Install
$ agentstack add skill-majiang213-openclaw-mas-continuous-learning ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Continuous Learning Skill
Automatically evaluates Claude Code sessions on end to extract reusable patterns that can be saved as learned skills.
When to Activate
- Setting up automatic pattern extraction from Claude Code sessions
- Configuring the Stop hook for session evaluation
- Reviewing or curating learned skills in
~/.claude/skills/learned/ - Adjusting extraction thresholds or pattern categories
- Comparing v1 (this) vs v2 (instinct-based) approaches
How It Works
This skill runs as a Stop hook at the end of each session:
- Session Evaluation: Checks if session has enough messages (default: 10+)
- Pattern Detection: Identifies extractable patterns from the session
- Skill Extraction: Saves useful patterns to
~/.claude/skills/learned/
Configuration
Edit config.json to customize:
{
"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:
{
"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 - Section on continuous learning
/learncommand - Manual pattern extraction mid-session
Comparison Notes (Research: Jan 2025)
vs 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
- Instinct-based learning - Smaller, atomic behaviors with confidence scoring
- Background observer - Haiku agent analyzing in parallel
- Confidence decay - Instincts lose confidence if contradicted
- Domain tagging - code-style, testing, git, debugging, etc.
- Evolution path - Cluster related instincts into skills/commands
See: docs/continuous-learning-v2-spec.md for full spec.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: majiang213
- Source: majiang213/OpenClaw-MAS
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.