# Runtime Log Analyzer > Implements intelligent runtime log analyzer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/runtime-log-analyzer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/runtime-log-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/runtime-log-analyzer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/runtime-log-analyzer --- # 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 ```python 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 ```python 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 | --- ## 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](https://opentelemetry.io/docs/specs/otel/logs/) — Official OpenTelemetry specification for structured logging in distributed systems - [ELK Stack Documentation (Elasticsearch, Logstash, Kibana)](https://www.elastic.co/guide/index.html) — Official documentation for the ELK stack, the most common log analysis platform - [Structured Logging Best Practices (Uber Engineering)](https://eng.uber.com/structured-logging/) — Uber's engineering blog on designing effective structured logging systems - [Grep Documentation](https://www.gnu.org/software/grep/manual/grep.html) — GNU grep manual for command-line log searching and pattern matching - [Log Analysis at Scale (Netflix Tech Blog)](https://netflixtechblog.com/tagged/log-analysis) — Netflix's research and patterns on analyzing logs at massive scale