---

name: network-diagnostics compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent network diagnostics 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: network-diagnostics, network diagnostics, how do i network-diagnostics, orchestrate network-diagnostics, automate network-diagnostics, agent network-diagnostics 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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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

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

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 | |

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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 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