Container Inspector

Container Inspector

Orchestrates intelligent skill selection and execution for container inspector 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 analyze_container_manifest(
    container_id: str,
    inspection_targets: List[str],
    min_security_score: float = 0.8
) -> Dict:
    """Analyze container manifest and runtime state for inspection readiness.
    
    Extracts critical inspection vectors:
    - Image layer composition and base OS vulnerabilities
    - Runtime resource limits (CPU, memory, PID)
    - Network exposure and privileged flags
    - Health check configuration and restart policies
    
    Args:
        container_id: Target container ID or image reference
        inspection_targets: List of inspection vectors to prioritize
        min_security_score: Minimum acceptable security posture score
        
    Returns:
        Inspection readiness dict with extracted features and risk flags
    """
    # Guard clause - validate container reference (Law 1)
    if not container_id or not re.match(r'^[a-zA-Z0-9._-]+$', container_id):
        raise ValueError(f"Invalid container reference: {container_id}")
    
    # Parse runtime state - Make Illegal States Unrepresentable (Law 2)
    runtime_state = _fetch_container_state(container_id)
    if runtime_state.get("Status") == "exited":
        return {"ready": False, "reason": "Container is stopped", "state": runtime_state}
        
    inspection_features = {
        "has_healthcheck": bool(runtime_state.get("Config", {}).get("Healthcheck")),
        "privileged": runtime_state.get("HostConfig", {}).get("Privileged", False),
        "network_mode": runtime_state.get("HostConfig", {}).get("NetworkMode"),
        "resource_limits": runtime_state.get("HostConfig", {}).get("Memory"),
        "base_image": runtime_state.get("Config", {}).get("Image")
    }
    
    # Calculate inspection readiness score
    risk_penalty = 0.0
    if inspection_features["privileged"]:
        risk_penalty += 0.4
    if inspection_features["network_mode"] == "host":
        risk_penalty += 0.3
        
    readiness_score = max(0.0, 1.0 - risk_penalty)
    
    if readiness_score < min_security_score:
        return {"ready": False, "score": readiness_score, "flags": ["high_risk_config"]}
        
    # Atomic Predictability (Law 3) - Return new dict
    return {
        "ready": True,
        "score": readiness_score,
        "features": dict(inspection_features),
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def run_inspection_pipeline(
    target: str,
    scan_depth: str = "full",
    max_retries: int = 2
) -> Dict:
    """Execute container inspection pipeline with resilience fallbacks.
    
    Implements Fail Fast, Fail Loud (Law 4) for inspection operations:
    - Invalid container states halt immediately
    - Network timeouts for CVE databases trigger offline fallback
    - No silent partial scans or corrupted manifests
    
    Fallback chain:
    1. Retry live inspection with adjusted timeout
    2. Fall back to cached manifest/layer analysis
    3. Defer to manual audit for critical security gaps
    
    Args:
        target: Container ID, image name, or archive path
        scan_depth: 'quick', 'full', or 'deep' inspection level
        max_retries: Maximum retry attempts for transient failures
        
    Returns:
        Inspection report with findings, confidence, and execution metadata
    """
    # Guard clause - validate target (Early Exit)
    if not target or not _is_valid_container_reference(target):
        raise InspectionError(f"Invalid inspection target: {target}")
        
    # Parse context - Ensure trusted state (Law 2)
    validated_target = _resolve_target_path(target)
    
    for attempt in range(max_retries + 1):
        try:
            if scan_depth == "quick":
                report = _run_quick_inspect(validated_target)
            else:
                report = _run_full_security_scan(validated_target)
                
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "target": validated_target,
                "depth": scan_depth,
                "findings": report,
                "attempts": attempt + 1,
                "latency_ms": _measure_execution_time()
            }
            
        except NetworkTimeoutError as e:
            # CVE DB unreachable - try offline fallback
            if attempt == max_retries:
                return _execute_offline_manifest_scan(validated_target)
            continue
            
        except CorruptedLayerError as e:
            # Fail Fast - Don't patch corrupted data (Law 4)
            raise InspectionError(f"Corrupted layer in {validated_target}: {str(e)}") from e
            
    # All retries exhausted - Fail Loud (Law 4)
    raise InspectionError(f"Inspection failed for {validated_target} 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

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.

• Docker Security Best Practices
• Container Scanning with Trivy
• CIS Docker Benchmark
• Open Container Initiative (OCI) Specs
• Kubernetes Pod Security Standards

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