capability-evolver

name	Capability Evolver
description	Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvement proposals. Use when the user or agent asks to analyze logs, diagnose failures, improve agent reliability, generate evolution proposals, or assess system health. Supports analyze, evolve, and status actions.
metadata	{"requires":{"env":["CLAW0X_API_KEY"]}}

Capability Evolver

Analyze agent runtime logs, detect patterns, compute health scores, and generate structured improvement proposals. Pure logic — no external AI dependency.

Pay-per-call pricing. $0.03 per successful analysis. Failed calls are free.

Quick Reference

When This Happens	Use Action	What You Get
Agent keeps failing	analyze	Error patterns + health score
Same error repeats	analyze	Root cause identification
Need improvement plan	evolve	Prioritized recommendations
System health check	status	Health score + summary
Post-deployment review	analyze	Regression detection
Fleet-wide diagnostics	analyze (batch)	Cross-agent patterns

Why deterministic? Reproducible results, no hallucination risk, sub-100ms processing, zero token costs.

---

5-Minute Quickstart

Step 1: Get API Key (30 seconds)

Sign up at claw0x.com → Dashboard → Create API Key

Step 2: Analyze Your First Logs (1 minute)

curl -X POST https://api.claw0x.com/v1/call \
  -H "Authorization: Bearer ck_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "skill": "capability-evolver",
    "input": {
      "action": "analyze",
      "logs": [
        {"timestamp": "2025-01-15T10:00:00Z", "level": "error", "message": "ETIMEDOUT", "context": "payment-api.ts"},
        {"timestamp": "2025-01-15T10:01:00Z", "level": "error", "message": "ETIMEDOUT", "context": "payment-api.ts"},
        {"timestamp": "2025-01-15T10:02:00Z", "level": "error", "message": "ETIMEDOUT", "context": "payment-api.ts"}
      ]
    }
  }'

Step 3: Get Actionable Insights (instant)

{
  "patterns": [
    {
      "type": "repeated_error",
      "severity": "high",
      "description": "ETIMEDOUT appeared 3 times in payment-api.ts",
      "affected_contexts": ["payment-api.ts"]
    }
  ],
  "health_score": 45,
  "recommendations": [
    "Add timeout configuration to payment-api.ts",
    "Implement retry logic with exponential backoff",
    "Monitor payment API response times"
  ]
}

Step 4: Generate Evolution Plan (2 minutes)

curl -X POST https://api.claw0x.com/v1/call \
  -H "Authorization: Bearer ck_live_..." \
  -d '{
    "skill": "capability-evolver",
    "input": {
      "action": "evolve",
      "logs": [...],
      "strategy": "harden"
    }
  }'

Done. You now have a prioritized improvement roadmap.

---

Real-World Use Cases

Scenario 1: Production Incident Response

Problem: Your agent crashed in production and you need to understand why

Solution:

1. Export last 1000 log entries
2. Run analyze action
3. Get error patterns and cascades
4. Identify root cause in minutes

Example:

const logs = await db.logs.findMany({ 
  where: { timestamp: { gte: incidentStart } },
  orderBy: { timestamp: 'asc' }
});

const analysis = await claw0x.call('capability-evolver', {
  action: 'analyze',
  logs: logs.map(l => ({
    timestamp: l.timestamp,
    level: l.level,
    message: l.message,
    context: l.context
  }))
});

// analysis.patterns shows: "auth-service.ts failed, then payment-api.ts failed"
// Root cause: auth service timeout cascaded to payment failures

Scenario 2: Continuous Improvement Pipeline

Problem: You want your agent to automatically improve based on production data

Solution:

1. Schedule daily log analysis
2. Generate evolution proposals
3. Auto-create GitHub issues for high-priority items
4. Track improvement over time

Example:

// Cron job: every day at 2am
async function dailyEvolution() {
  const logs = await getLast24HoursLogs();
  
  const evolution = await claw0x.call('capability-evolver', {
    action: 'evolve',
    logs,
    strategy: 'balanced'
  });
  
  // Create issues for critical recommendations
  for (const rec of evolution.recommendations.filter(r => r.priority === 'critical')) {
    await github.issues.create({
      title: `[Auto] ${rec.category}: ${rec.description}`,
      body: `Risk: ${rec.risk}\nApproach: ${rec.approach}`,
      labels: ['auto-generated', 'reliability']
    });
  }
  
  // Track health score trend
  await metrics.record('agent_health_score', evolution.estimated_improvement);
}
// Result: Health score improved from 45 to 85 over 3 months

Scenario 3: Multi-Agent Fleet Management

Problem: Managing 50+ agent instances, need to identify systemic issues

Solution:

1. Aggregate logs from all agents
2. Batch analyze to find common patterns
3. Fix once, deploy to all agents
4. Reduce fleet-wide error rate

Example:

# Collect logs from all agents
all_logs = []
for agent_id in agent_fleet:
    logs = fetch_agent_logs(agent_id, last_24h)
    all_logs.extend(logs)

# Analyze fleet-wide
result = client.call("capability-evolver", {
    "action": "analyze",
    "logs": all_logs
})

# result.patterns shows: "40 of 50 agents failing on auth-service.ts"
# Fix auth-service.ts once, deploy to all agents
# Result: 80% reduction in fleet-wide errors

Scenario 4: Pre-Deployment Health Check

Problem: Want to ensure new deployment doesn't introduce regressions

Solution:

1. Analyze logs from staging environment
2. Compare health score to production baseline
3. Block deployment if health score drops
4. Catch regressions before production

Example:

# .github/workflows/deploy.yml
- name: Health Check
  run: |
    STAGING_LOGS=$(fetch-logs staging)
    
    RESULT=$(curl -X POST https://api.claw0x.com/v1/call \
      -H "Authorization: Bearer $CLAW0X_API_KEY" \
      -d "{\"skill\":\"capability-evolver\",\"input\":{\"action\":\"analyze\",\"logs\":$STAGING_LOGS}}")
    
    HEALTH_SCORE=$(echo $RESULT | jq -r '.health_score')
    BASELINE=75
    
    if [ $HEALTH_SCORE -lt $BASELINE ]; then
      echo "Health score $HEALTH_SCORE below baseline $BASELINE"
      exit 1
    fi
# Result: Zero regression-related incidents in 6 months

---

Integration Recipes

OpenClaw Agent

import { Claw0xClient } from '@claw0x/sdk';

const claw0x = new Claw0xClient(process.env.CLAW0X_API_KEY);

// Analyze logs after each run
agent.onComplete(async () => {
  const logs = agent.getRecentLogs();
  
  const analysis = await claw0x.call('capability-evolver', {
    action: 'analyze',
    logs
  });
  
  if (analysis.health_score < 70) {
    console.warn('⚠️ Health score low:', analysis.health_score);
    console.log('Recommendations:', analysis.recommendations);
  }
});

LangChain Agent

from claw0x import Claw0xClient
import os

client = Claw0xClient(api_key=os.getenv("CLAW0X_API_KEY"))

def analyze_agent_health(logs):
    result = client.call("capability-evolver", {
        "action": "analyze",
        "logs": logs
    })
    
    return {
        "health_score": result["health_score"],
        "patterns": result["patterns"],
        "recommendations": result["recommendations"]
    }

# Use in monitoring
health = analyze_agent_health(agent.logs)
if health["health_score"] < 70:
    alert_team(health)

Custom Monitoring Dashboard

// Real-time health monitoring
async function updateHealthDashboard() {
  const logs = await db.logs.findMany({
    where: { timestamp: { gte: Date.now() - 3600000 } } // last hour
  });
  
  const analysis = await fetch('https://api.claw0x.com/v1/call', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.CLAW0X_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      skill: 'capability-evolver',
      input: { action: 'analyze', logs }
    })
  });
  
  const result = await analysis.json();
  
  // Update dashboard
  dashboard.update({
    healthScore: result.health_score,
    errorRate: result.summary.error_count / result.summary.total_logs,
    topPatterns: result.patterns.(, )
  });
}

(updateHealthDashboard, ); 

Evolution Strategy Comparison

// Compare different evolution strategies
const logs = await getProductionLogs();

const strategies = ['balanced', 'innovate', 'harden', 'repair-only'];

const results = await Promise.all(
  strategies.map(strategy =>
    claw0x.call('capability-evolver', {
      action: 'evolve',
      logs,
      strategy
    })
  )
);

// Compare estimated improvements
for (let i = 0; i < strategies.length; i++) {
  console.log(`${strategies[i]}: ${results[i].estimated_improvement}`);
}

// Choose best strategy for current situation
const best = results.reduce((a, b) => 
  parseFloat(a.estimated_improvement) > parseFloat(b.estimated_improvement) ? a : b
);

---

How It Works — Under the Hood

Capability Evolver is a deterministic analysis engine that processes structured log data and produces actionable diagnostics. No LLM is involved �?the analysis is rule-based, which means results are reproducible and fast.

Analysis Engine

The core engine processes log entries through several analysis passes:

1. Pattern detection �?logs are grouped by context (file/module) and level (error/warn/info/debug). The engine looks for:

   • Repeated errors �?the same error message appearing multiple times indicates a systemic issue, not a transient failure
   • Error cascades �?errors in module A followed by errors in module B within a short time window suggest a dependency chain failure
   • Regression signals �?errors that appear after a period of clean logs suggest a recent change broke something
   • Inefficiency patterns �?excessive warn-level logs or repeated retries indicate performance issues

2. Health scoring �?a system health score (0�?00) is computed based on:

   • Error rate (errors / total logs)
   • Error diversity (unique error messages / total errors)
   • Warn-to-error ratio
   • Time distribution (clustered errors score worse than spread-out errors)

3. Recommendation generation �?based on detected patterns, the engine generates specific, actionable recommendations. These aren't generic advice �?they reference the actual files, error messages, and patterns found in your logs.

Evolution Strategies

When using the evolve action, you can choose a strategy that shapes the recommendations:

Strategy	Focus	Best For
auto	Balanced based on health score	Default �?let the engine decide
balanced	Equal weight to reliability and features	Stable systems with moderate issues
innovate	Prioritize new capabilities	Healthy systems ready to grow
harden	Prioritize reliability and error reduction	Systems with frequent failures
repair-only	Fix critical issues only	Systems in crisis

Evolution Proposals

The evolve action produces structured improvement proposals with:

• A unique evolution_id for tracking
• Prioritized recommendations with category labels (reliability, performance, architecture)
• Risk assessment (how risky is each proposed change)
• Estimated improvement (projected health score after implementing recommendations)

Why Deterministic (Not LLM)?

• Reproducible �?same logs always produce the same analysis. Critical for debugging and auditing.
• Fast �?sub-100ms processing. No API call to an AI provider.
• No hallucination risk �?the engine only reports patterns it actually found in the data.
• Cost-effective �?pure computation, no token costs.

The tradeoff: the engine can't understand semantic meaning in log messages the way an LLM could. It relies on structural patterns (frequency, timing, severity) rather than understanding what the error message means in context.

Prerequisites

Requires a Claw0x API key. Sign up at claw0x.com and create a key in your dashboard. Set it as an environment variable:

export CLAW0X_API_KEY="your-api-key-here"

When to Use

• User says "analyze these logs", "what's failing", "improve my agent", "check system health"
• Agent pipeline needs automated diagnostics after a run
• User wants structured recommendations for fixing recurring errors
• Building a self-healing agent that adapts based on its own failure patterns

Input

Field	Type	Required	Description
input.action	string	yes	"analyze", "evolve", or "status"
input.logs	array	yes (for analyze/evolve)	Array of log entries
input.logs[].timestamp	string	yes	ISO timestamp
input.logs[].level	string	yes	"error", "warn", "info", or "debug"
input.logs[].message	string	yes	Log message
input.logs[].context	string	no	File or module name
input.strategy	string	no	"auto", "balanced", "innovate", "harden", "repair-only"
input.target_file	string	no	Focus analysis on a specific file

Output (Analyze)

Field	Type	Description
patterns	array	Detected error/regression/inefficiency patterns with severity
health_score	number	System health 0�?00
recommendations	string[]	Actionable improvement suggestions
summary	object	Counts: total_logs, error_count, warn_count, unique_patterns

Output (Evolve)

Field	Type	Description
evolution_id	string	Unique proposal ID
strategy	string	Effective strategy used
recommendations	array	Prioritized improvements with category and approach
risk_assessment	object	Risk level and contributing factors
estimated_improvement	string	Projected health score improvement

Error Codes

• 400 — Invalid action or missing logs array
• 401 — Invalid or missing API key
• 500 — Processing failed (not billed)

Pricing

$0.03 per successful call. Failed calls and 5xx errors are never charged.

---

Deterministic vs LLM Analysis: Which is Right for You?

Feature	LLM-Based (GPT-4, Claude)	Claw0x (Deterministic)
Setup Time	5-10 min (prompt engineering)	2 minutes (get API key)
Processing Speed	5-30 seconds	Sub-100ms
Reproducibility	❌ Varies per run	✅ Same logs = same results
Hallucination Risk	⚠️ Can invent patterns	✅ Only reports real patterns
Cost	$0.10-0.50 per analysis	$0.03 per analysis
Semantic Understanding	✅ Understands context	❌ Pattern-based only
Audit Trail	❌ Hard to explain	✅ Rule-based, explainable

When to Use LLM-Based

• Need semantic understanding of log messages
• Want natural language explanations
• Logs contain unstructured text
• Willing to trade speed for insight depth

When to Use Claw0x (Deterministic)

• Need reproducible results for compliance
• Want sub-second processing
• Building automated pipelines
• Require explainable AI for audits
• Processing millions of logs
• Cost-sensitive applications

---

How It Fits Into Your Agent Lifecycle

┌─────────────────────────────────────────────────────────────┐
│                  Agent Development Lifecycle                 │
└─────────────────────────────────────────────────────────────┘
                            │
                            ├─ Development
                            │  • Write agent code
                            │  • Local testing
                            │
                            ├─ Staging Deployment
                            │  POST /v1/call
                            │  {action: "analyze", logs: staging_logs}
                            │  → Health check before production
                            │
                            ├─ Production Monitoring
                            │  POST /v1/call (every hour)
                            │  {action: "analyze", logs: recent_logs}
                            │  → Real-time health tracking
                            │
                            ├─ Incident Response
                            │  POST /v1/call
                            │  {action: "analyze", logs: incident_logs}
                            │  → Root cause analysis
                            │
                            └─ Continuous Improvement
                               POST /v1/call (daily)
                               {action: "evolve", strategy: "balanced"}
                               → Auto-generate improvement tasks

Integration Points

1. Pre-Deployment — Health check before releasing
2. Real-Time Monitoring — Continuous health tracking
3. Incident Response — Fast root cause analysis
4. Daily Reviews — Automated improvement proposals
5. Fleet Management — Cross-agent pattern detection

---

Why Use This Via Claw0x?

Unified Infrastructure

• One API key for all skills — no per-provider auth
• Atomic billing — pay per successful call, $0 on failure
• Security scanned — OSV.dev integration for all skills

Agent-Optimized

• Deterministic analysis — reproducible, auditable results
• Fast processing — sub-100ms, suitable for real-time monitoring
• Structured output — JSON format, easy to integrate
• Evolution strategies — tailored recommendations based on context

Production-Ready

• 99.9% uptime — reliable infrastructure
• Scales to millions — handle enterprise-scale log analysis
• Cloud-native — works in Lambda, Cloud Run, containers
• Zero maintenance — no model updates or dependencies