# Network Diagnostics > Implements intelligent network diagnostics with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/network-diagnostics` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/network-diagnostics` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/network-diagnostics/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/network-diagnostics --- # Network Diagnostics Orchestrates intelligent skill selection and execution for network diagnostics 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 select_diagnostic_tool( target: str, available_tools: List[Dict], min_latency_threshold: float = 0.5 ) -> Optional[Dict]: """Select optimal network diagnostic tool based on target type and constraints. Applies Law 2 (Parse at boundary) by validating target and tool metadata. Applies Law 1 (Early Exit) for invalid inputs or missing tools. Returns immutable tool selection with confidence scoring. Args: target: IP address or hostname to diagnose available_tools: List of diagnostic tool metadata (e.g., ping, traceroute, nmap) min_latency_threshold: Maximum acceptable baseline latency in seconds Returns: Selected tool dictionary or None if no tool meets threshold """ # Law 1: Early exit on invalid input if not target or not isinstance(target, str): raise ValueError("Target must be a non-empty string") if not available_tools: raise ValueError("No diagnostic tools available for selection") # Law 2: Parse and validate at boundary is_ipv6 = ":" in target target_features = { "is_ip": _is_ip(target), "is_ipv6": is_ipv6, "target": target } best_tool = None best_score = 0.0 for tool in available_tools: # Domain-specific scoring: match tool capabilities to target features score = _calculate_diagnostic_score(target_features, tool) if score > best_score and score >= min_latency_threshold: best_score = score best_tool = tool if best_tool is None: return None # Law 3: Atomic Predictability - Return new dict, don't mutate result = dict(best_tool) result["selection_confidence"] = best_score result["selection_timestamp"] = time.time() return result ``` ### Pattern 2: Execution with Fallback ```python def execute_diagnostic_pipeline( tool: Dict, target: str, fallback_chain: List[Dict], max_retries: int = 2 ) -> Dict: """Execute network diagnostics with adaptive fallback chain. Implements Law 4 (Fail Fast, Fail Loud): - Invalid states halt immediately with descriptive errors - No silent failures or partial results Fallback chain: 1. Retry with adjusted parameters (e.g., increase timeout) 2. Try alternative diagnostic tool from fallback_chain 3. Defer to manual inspection (for critical infrastructure) Args: tool: Selected diagnostic tool metadata target: Hostname or IP to diagnose fallback_chain: List of alternative tools/methods max_retries: Maximum retry attempts before fallback Returns: Execution result with metadata (success, timing, confidence) """ # Law 1: Guard clause - validate tool if not tool or not tool.get("executable"): raise ValueError(f"Invalid diagnostic tool: {tool.get('name', 'unknown')}") # Law 2: Parse context - Ensure trusted state validated_target = _validate_target_format(target) for attempt in range(max_retries + 1): try: # Domain-specific execution: run actual diagnostic command result = _run_diagnostic_command(tool["executable"], validated_target, tool.get("args", [])) # Law 3: Atomic Predictability return { "success": True, "tool_executed": tool["name"], "result": result, "attempts": attempt + 1, "latency_ms": _calculate_latency() } except subprocess.TimeoutExpired: # Transient error - try fallback if attempt == max_retries: return _apply_diagnostic_fallback(tool, validated_target, fallback_chain) except PermissionError as e: # Fail Fast - Don't try to patch bad permissions (Law 4) raise ValueError(f"Permission denied for {tool['name']}: {str(e)}") from e # All retries exhausted - Fail Loud (Law 4) raise RuntimeError(f"Failed to execute {tool['name']} after {max_retries + 1} attempts") ``` ### 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 | | --- --- ## 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 skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [TCP/IP Networking (RFC 793)]() - [DNS Resolution Troubleshooting (RFC 1034/1035)]() - [HTTP/2 and HTTP/3 Protocol Specifications]() - [Wireshark Network Protocol Analysis]() - [Linux netstat/ss/traceroute Commands]()