Imported from hiyenwong/ai_collection (collection/skills/self-challenge/SKILL.md). Install upstream with npx skills add hiyenwong/ai_collection --skill self-challenge. Copyright stays with the author.

Self-Challenge Mechanism

Description

A self-evolution skill based on Agent0 paper (arXiv:2511.16043). Uses a dual-agent competition model where Curriculum Agent designs challenges and Executor Agent attempts them, driving continuous capability expansion.

Activation Keywords

• 自我挑战
• self challenge
• 能力测试
• capability test
• 自我进化测试
• self evolution test
• 挑战任务
• challenge task
• agent0 挑战

Recommended Model

• sonnet4.5 (Recommended for complex reasoning and challenge design)

Tools Used

• exec: Execute commands and scripts
• read: Read existing skills, documentation, and resources
• write: Create challenge results and new skills
• memory_search: Find relevant knowledge for challenges
• sessions_spawn: Spawn sub-agents for execution

Usage Patterns

Start Challenge

开始自我挑战

Specific Domain Challenge

挑战我的 [domain] 能力

Weekly Challenge

本周自我挑战

Instructions for Agents

Overview

The self-challenge mechanism uses dual-agent architecture:

┌─────────────────────────────────────────────────────────┐
│            Self-Challenge Architecture                   │
├─────────────────────────────────────────────────────────┤
│                                                          │
│   ┌─────────────────┐       ┌─────────────────┐         │
│   │   Curriculum    │       │    Executor     │         │
│   │     Agent       │──────▶│     Agent       │         │
│   │  (设计挑战)      │       │   (执行挑战)    │         │
│   └─────────────────┘       └─────────────────┘         │
│          │                          │                    │
│          │                          │                    │
│          ▼                          ▼                    │
│   ┌─────────────────────────────────────────────┐       │
│   │              Review & Learn                  │       │
│   │         (回顾总结，提取经验)                   │       │
│   └─────────────────────────────────────────────┘       │
│                     │                                    │
│                     ▼                                    │
│   ┌─────────────────────────────────────────────┐       │
│   │           Knowledge Update                   │       │
│   │   (更新 skills, MEMORY.md, 工作流程)          │       │
│   └─────────────────────────────────────────────┘       │
│                                                          │
└─────────────────────────────────────────────────────────┘

Phase 1: Curriculum Agent (设计挑战)

Role: Design challenges that are slightly above current capabilities

Challenge Selection Criteria:

1. Capability Gap: Identify weak areas from recent performance
2. Difficulty: Should be achievable but challenging
3. Learning Value: Must provide actionable insights
4. Relevance: Align with user needs and goals

Challenge Types:

Challenge Design Process:

1. Analyze Recent Performance

   def analyze_capability_gaps():
       # Review recent self-reflection reports
       recent_reviews = read_recent_reviews()
       
       # Identify weak areas
       weak_areas = []
       for review in recent_reviews:
           if review.rating < 4:
               weak_areas.append(review.area)
       
       # Check for missing skills
       existing_skills = list_skills()
       recommended_skills = get_recommended_skills()
       missing_skills = set(recommended_skills) - set(existing_skills)
       
       return {
           'weak_areas': weak_areas,
           'missing_skills': missing_skills,
           'improvement_opportunities': identify_opportunities()
       }

2. Select Challenge Domain

   def select_challenge_domain(gaps):
       # Prioritize by impact
       priorities = [
           ('weak_area', gaps.weak_areas),
           ('missing_skill', gaps.missing_skills),
           ('opportunity', gaps.improvement_opportunities)
       ]
       
       for priority_type, items in priorities:
           if items:
               return {
                   'type': priority_type,
                   'domain': items[0],
                   'difficulty': 'medium'
               }

3. Design Specific Challenge

   ## Challenge: [Name]
   
   ### Domain
   [Capability area being tested]
   
   ### Difficulty
   [Easy/Medium/Hard]
   
   ### Objective
   [Clear, measurable goal]
   
   ### Constraints
   - Time limit: [duration]
   - Tools allowed: [list]
   - Success criteria: [measurable]
   
   ### Expected Learning
   - [What will be learned]
   - [How it improves capabilities]
   
   ### Resources
   - [Available references]
   - [Similar challenges solved]

Phase 2: Executor Agent (执行挑战)

Role: Attempt to complete the designed challenge

Execution Process:

1. Understand Challenge

   • Read challenge specification
   • Identify required resources
   • Plan approach

2. Execute

   • Follow systematic approach
   • Document each step
   • Track time and resources

3. Document Results

   ## Challenge Execution Log
   
   ### Start Time
   [timestamp]
   
   ### Approach
   1. [Step 1]
   2. [Step 2]
   3. [Step 3]
   
   ### Obstacles Encountered
   | Obstacle | Attempted Solution | Result |
   |----------|-------------------|--------|
   | [issue] | [solution] | [outcome] |
   
   ### End Time
   [timestamp]
   
   ### Outcome
   [Success/Partial Success/Failure]
   
   ### Key Learnings
   - [Learning 1]
   - [Learning 2]

Phase 3: Review & Learn (回顾总结)

Role: Analyze results and extract actionable knowledge

Review Process:

1. Outcome Analysis

   def analyze_outcome(challenge, execution_log):
       success_rate = calculate_success_rate(execution_log)
       time_efficiency = calculate_time_efficiency(challenge, execution_log)
       resource_efficiency = calculate_resource_efficiency(execution_log)
       
       return {
           'success_rate': success_rate,
           'time_efficiency': time_efficiency,
           'resource_efficiency': resource_efficiency,
           'overall_score': (success_rate + time_efficiency + resource_efficiency) / 3
       }

2. Capability Assessment

   ## Capability Assessment
   
   ### Before Challenge
   | Capability | Level |
   |------------|-------|
   | [skill 1] | [1-5] |
   | [skill 2] | [1-5] |
   
   ### After Challenge
   | Capability | Level | Change |
   |------------|-------|--------|
   | [skill 1] | [1-5] | [↑/↓/=] |
   | [skill 2] | [1-5] | [↑/↓/=] |

3. Knowledge Extraction

   • What worked well?
   • What didn't work?
   • What new skills were developed?
   • What should be documented?

4. Update Knowledge Base

   • Update MEMORY.md with learnings
   • Create or update skills
   • Add to learned-skills.md
   • Schedule follow-up challenges

Challenge Templates

Template 1: Tool Mastery

## Challenge: Master [Tool Name]

### Objective
Use [tool] to complete [specific task] with [success criteria]

### Constraints
- Time: 30 minutes
- Resources: Official docs, examples
- Success: Task completed without errors

### Steps
1. Read documentation
2. Set up environment
3. Execute basic commands
4. Complete target task
5. Document learnings

Template 2: Integration Challenge

## Challenge: Integrate [Tools]

### Objective
Create a workflow that combines [tool1], [tool2], [tool3]

### Constraints
- Time: 60 minutes
- Must use all 3 tools
- Must solve real problem
- Success: Working workflow documented

### Steps
1. Identify integration points
2. Design workflow
3. Implement and test
4. Document and share

Template 3: Problem Solving

## Challenge: Solve [Problem]

### Objective
Debug and fix [failing system/error]

### Constraints
- Time: 45 minutes
- Must identify root cause
- Must implement fix
- Success: System working correctly

### Steps
1. Reproduce issue
2. Analyze logs/errors
3. Identify root cause
4. Implement fix
5. Verify solution
6. Document process

Context Files

~/.openclaw/workspace/memory/self-reflection/*.md

Recent self-reflection reports for identifying capability gaps.

~/.openclaw/workspace/knowledge/skills/learned-skills.md

Skills to be updated after challenges.

~/.openclaw/workspace/MEMORY.md

Long-term memory for storing challenge learnings.

~/.openclaw/workspace/memory/challenge-log.json

Log of all challenges executed.

{
  "challenges": [
    {
      "id": "challenge-001",
      "date": "2026-03-05",
      "domain": "tool-mastery",
      "difficulty": "medium",
      "outcome": "success",
      "score": 0.85,
      "learnings": ["..."]
    }
  ]
}

Scheduling

Recommended Schedule

• Weekly Challenge: Every Monday 10:00 AM
• Duration: 30-60 minutes
• Follow-up: Review results within 24 hours

Cron Configuration

openclaw cron add \
  --name "self-challenge" \
  --description "Weekly self-challenge for capability expansion" \
  --cron "0 10 * * 1" \
  --tz "Asia/Shanghai" \
  --system-event "执行自我挑战任务"

Error Handling

Challenge Too Easy

If challenge completed in < 50% of allotted time:
  1. Increase difficulty for next challenge
  2. Add additional constraints
  3. Expand scope

Challenge Too Hard

If challenge failed completely:
  1. Break down into smaller challenges
  2. Provide more resources
  3. Adjust difficulty level
  4. Reschedule with better preparation

Resource Unavailable

If required resources are unavailable:
  1. Find alternative resources
  2. Modify challenge to use available resources
  3. Postpone and acquire resources

Examples

Example 1: Tool Mastery Challenge

User: "开始自我挑战"

Agent (Curriculum):
## Challenge: Master openclaw cron Commands

### Domain
Tool Mastery - OpenClaw CLI

### Difficulty
Medium

### Objective
Create, list, and manage 3 different cron jobs using openclaw CLI

### Constraints
- Time: 30 minutes
- Resources: openclaw docs, --help
- Success: 3 jobs created, listed, one executed

### Expected Learning
- Understanding of openclaw cron system
- Job scheduling best practices
- Error handling for cron jobs

---

Agent (Executor):
## Execution Log

### Start: 2026-03-05 11:00
### Approach:
1. Read openclaw cron --help
2. Create first job (daily)
3. Create second job (weekly)
4. Create third job (hourly)
5. List all jobs
6. Run one job immediately

### Obstacles:
| Obstacle | Solution | Result |
|----------|----------|--------|
| Unknown syntax | Used --help | Success |
| Timezone issue | Set tz explicitly | Success |

### End: 2026-03-05 11:25
### Outcome: Success (25 minutes)

### Key Learnings:
1. openclaw cron add syntax
2. Cron expression format
3. Timezone configuration importance

---

Agent (Review):
## Review Results

### Scores:
- Success Rate: 100%
- Time Efficiency: 83% (25/30 min)
- Overall: 92%

### Capability Change:
| Capability | Before | After |
|------------|--------|-------|
| openclaw cron | 2 | 4 ↑ |

### Knowledge Updates:
- Updated: learned-skills.md (new skill entry)
- Created: knowledge/topics/optimization/cron-jobs.md

### Next Challenge Suggestion:
Automate cron job monitoring and alerting

Best Practices

1. Progressive Difficulty: Start easy, increase difficulty over time
2. Document Everything: Log all attempts, successes, and failures
3. Real Problems: Design challenges around actual user needs
4. Time Bound: Always set reasonable time limits
5. Review Promptly: Analyze results within 24 hours
6. Iterate: Use learnings to improve future challenges

Limitations

• Requires honest self-assessment
• Challenge design quality affects learning value
• May not cover all capability gaps
• Time investment needed for proper execution
• Benefits accumulate over time with consistency

Success Metrics

Resources

• Source Paper: https://arxiv.org/abs/2511.16043
• Self-Reflection Reports: ~/.openclaw/workspace/memory/self-reflection/
• Challenge Log: ~/.openclaw/workspace/memory/challenge-log.json

Related Skills

• ice-review: Review challenges using ICE method
• memory-retrieval: Find relevant knowledge for challenges
• skill-extractor: Extract skills from challenge learnings

Notes

• Self-challenge is most effective when scheduled regularly
• Mix different challenge types for balanced development
• Celebrate successes to maintain motivation
• Don't fear failures - they provide learning opportunities
• Track progress over time to see improvement