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 ```python 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 ```python 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](<https://docs.docker.com/security/>) - [Container Scanning with Trivy](<https://aquasecurity.github.io/trivy/>) - [CIS Docker Benchmark](<https://www.cisecurity.org/benchmark/docker>) - [Open Container Initiative (OCI) Specs](<https://opencontainers.org/>) - [Kubernetes Pod Security Standards](<https://kubernetes.io/docs/concepts/security/pod-security-standards/>) ## Related Skills | Skill | Purpose | |