What makes continue-learning notable?

1.5K downloads. Trust evidence available

How should continue-learning be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

continue-learning

Instinct-based learning system for OpenClaw. Analyzes sessions, detects patterns, creates atomic learnings with confidence scoring, and suggests optimizations for self-evolution. Works alongside agent-self-improvement for complete learning: internal session analysis + external user feedback. Use when: you want your AI agent to learn from its own behavior, improve over time, discover optimization opportunities, or build a self-improving automation system. Don't use when: static agent behavior is preferred.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.5k

Updated

Oct 10, 2026

Version

1.3.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.5K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.5K downloadsadoption · observed Oct 10, 2026
Latest release: 1.3.2release · observed Aug 20, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s177vajy8ffpjnym8evc1jcxr5877x59:continue-learning

Install using `clawhub skill install s177vajy8ffpjnym8evc1jcxr5877x59:continue-learning` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/adelpro/continue-learning before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-adelpro-continue-learning/snapshot"

Documentation

CLAWHUB

48,249 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: continue-learning
slug: continue-learning
version: 1.3.2
description: |
  Instinct-based learning system for OpenClaw. Analyzes sessions, detects patterns, creates atomic learnings with confidence scoring, and suggests optimizations for self-evolution.
  
  Works alongside agent-self-improvement for complete learning: internal session analysis + external user feedback.
  
  Use when: you want your AI agent to learn from its own behavior, improve over time, discover optimization opportunities, or build a self-improving automation system.
  
  Don't use when: static agent behavior is preferred.
triggers:
  - continuous learning
  - self improving agent
  - agent evolution
  - pattern detection
  - session analysis
  - ai learning
  - agent optimization
  - automation improvement
  - self evolution
metadata:
  openclaw:
    emoji: "🧠"
    requires:
      bins: ["node"]
---

# Continuous Learning for AI Agents

An instinct-based learning system that helps AI agents improve themselves through observation and pattern detection.

## What This Skill Does

- **Analyzes session history** - Reviews agent interactions and outputs
- **Detects patterns** - Identifies recurring behaviors, preferences, workflows
- **Creates instincts** - Atomic learnings with confidence scores
- **Suggests optimizations** - Based on observed behavior patterns
- **Enables self-evolution** - Converts insights into improvements

## When to Use

**Use when:**
- Building self-improving AI agents
- Want agent to learn from interactions
- Discovering optimization opportunities
- Creating adaptive automation
- Tracking behavioral patterns

**Skip when:**
- Static, unchanging behavior preferred
- No session history available
- Simple, deterministic workflows only

## Architecture

```
~/.openclaw/agents/ (session .jsonl files)
        │
        ▼
┌───────────────────────────────────────────┐
│ analyze.mjs                                │
│ • Reads session history                   │
│ • Extracts tool calls & errors             │
│ • Detects patterns                         │
└───────────────────────────────────────────┘
        │
        ▼
┌───────────────────────────────────────────┐
│ memory/learning/                           │
│ • instincts.jsonl (atomic learnings)       │
│ • patterns.json (aggregated)              │
│ • optimizations.json (suggestions)         │
└───────────────────────────────────────────┘
```

## External Feedback (Sub-Skill)

This skill works with **agent-self-improvement** (ClawHub) for external user feedback capture:

- **Internal Learning**: Session analysis (this skill)
- **External Learning**: User feedback via `SKILL:agent-self-improvement`

### Combined Usage

```
# Nightly: Internal analysis
SKILL:openclaw-continuous-learning --analyze

# After any output: Capture feedback
SKILL:agent-self-improvement --job <task> --feedback "<user response>"

# Daily: Generate combined improvements
SKILL:agent-self-improvement --improve all
```

### Feedback

README.md

# continue-learning

OpenClaw instinct-based learning system: session analysis, pattern detection, atomic learnings with confidence scoring, and optimization suggestions.

## Install

```bash
npx skills add adelpro/continue-learning
```

_meta.json

{
  "ownerId": "kn72cbvk9f5n48msm4t4sj3wyh80n2eb",
  "slug": "continue-learning",
  "version": "1.3.2",
  "publishedAt": 1787254873454
}

skill-card.md

## Description:

Instinct-based learning system for OpenClaw that analyzes sessions, detects patterns, creates atomic learnings with confidence scoring, and suggests optimizations for self-evolution.

This skill is ready for commercial/non-commercial use.

## Publisher:

[adelpro](https://clawhub.ai/user/adelpro)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and agent builders use this skill to analyze OpenClaw session history, identify recurring behavior and error patterns, and generate reviewed learning artifacts or optimization suggestions for self-improving agents.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill reads recent OpenClaw session logs and may process private interaction history.

Mitigation: Run analysis with an explicit --agent scope when possible, and use --all only when broad review is needed.

Risk: Session-derived snippets may retain sensitive details despite redaction.

Mitigation: Review stored learning contents periodically and run the prune command if sensitive data may have appeared in tool errors or session output.

Risk: The skill persists derived instincts, patterns, and optimization suggestions locally.

Mitigation: Use the documented retention limits and prune command to remove stored learning data before sharing or retiring a workspace.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/adelpro/skills/continue-learning)

## Skill Output:

**Output Type(s):** [text, JSON files, shell commands, guidance]

**Output Format:** [Console text plus local JSON and JSONL learning files]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Requires Node.js; analysis requires an explicit --agent scope or explicit --all selection.]

## Skill Version(s):

1.3.2 (source: server release metadata and SKILL.md frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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