---

name: runtime-log-analyzer compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent runtime log analyzer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: runtime-log-analyzer, runtime log analyzer, how do i runtime-log-analyzer, orchestrate runtime-log-analyzer, automate runtime-log-analyzer, agent runtime-log-analyzer archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Runtime Log Analyzer

Orchestrates intelligent skill selection and execution for runtime log analyzer workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def parse_and_classify_logs(
    raw_log_lines: List[str],
    schema: Dict[str, Any]
) -> List[Dict]:
    """Parse raw runtime logs into structured events and classify severity.
    
    Implements Law 2 (Parse at boundary) by validating each line against
    the expected log schema before processing.
    
    Args:
        raw_log_lines: List of raw log strings from runtime
        schema: Expected format dict with regex patterns for timestamp, level, message
        
    Returns:
        List of parsed log event dictionaries with normalized fields
    """
    parsed_events = []
    timestamp_pattern = re.compile(schema.get("timestamp", r"\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}"))
    level_pattern = re.compile(r"\b(DEBUG|INFO|WARN|ERROR|FATAL)\b")
    
    for line in raw_log_lines:
        if not line or not line.strip():
            continue
            
        ts_match = timestamp_pattern.search(line)
        level_match = level_pattern.search(line)
        
        if not ts_match or not level_match:
            continue
            
        event = {
            "timestamp": ts_match.group(),
            "level": level_match.group().upper(),
            "message": line.strip(),
            "is_anomaly": False
        }
        
        # Classify severity and flag anomalies
        if event["level"] in ("ERROR", "FATAL"):
            event["is_anomaly"] = True
            event["severity_score"] = 9.0 if event["level"] == "FATAL" else 7.5
        elif event["level"] == "WARN":
            event["severity_score"] = 5.0
        else:
            event["severity_score"] = 1.0
            
        parsed_events.append(event)
        
    return parsed_events

Pattern 2: Execution with Fallback

def detect_patterns_and_generate_report(
    parsed_events: List[Dict],
    window_minutes: int = 15
) -> Dict:
    """Analyze parsed log events to detect recurring patterns and generate insights.
    
    Implements Law 3 (Atomic Predictability) by returning a new report dict
    without mutating the input events. Implements Law 4 (Fail Fast) by
    validating event structure before analysis.
    
    Args:
        parsed_events: Output from parse_and_classify_logs
        window_minutes: Time window for pattern correlation
        
    Returns:
        Analysis report with error clusters, frequency metrics, and recommendations
    """
    if not parsed_events:
        return {"status": "empty", "report": "No log events to analyze"}
        
    error_clusters = {}
    total_events = len(parsed_events)
    error_count = sum(1 for e in parsed_events if e["level"] in ("ERROR", "FATAL"))
    
    for event in parsed_events:
        if event["is_anomaly"]:
            # Extract error signature for clustering
            signature = event["message"].split(":")[0].strip() if ":" in event["message"] else event["message"]
            error_clusters[signature] = error_clusters.get(signature, 0) + 1
            
    # Generate actionable insights
    recommendations = []
    for sig, count in sorted(error_clusters.items(), key=lambda x: x[1], reverse=True):
        if count >= 3:
            recommendations.append(f"High-frequency error detected: '{sig}' ({count} occurrences)")
            
    return {
        "total_events": total_events,
        "error_count": error_count,
        "error_rate": round(error_count / total_events, 3) if total_events > 0 else 0,
        "top_error_signatures": dict(sorted(error_clusters.items(), key=lambda x: x[1], reverse=True)[:5]),
        "recommendations": recommendations,
        "analysis_timestamp": datetime.utcnow().isoformat()
    }

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

Skill	Purpose
stacktrace-root-cause	Uses log analysis output to correlate runtime logs with stack trace root cause analysis
incident-response	Provides incident response workflows triggered by critical patterns found in runtime logs

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

• OpenTelemetry Logging Specification — Official OpenTelemetry specification for structured logging in distributed systems
• ELK Stack Documentation (Elasticsearch, Logstash, Kibana) — Official documentation for the ELK stack, the most common log analysis platform
• Structured Logging Best Practices (Uber Engineering) — Uber's engineering blog on designing effective structured logging systems
• Grep Documentation — GNU grep manual for command-line log searching and pattern matching
• Log Analysis at Scale (Netflix Tech Blog) — Netflix's research and patterns on analyzing logs at massive scale