# Web Security Testing > Implements intelligent web security testing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/web-security-testing` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/web-security-testing` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/web-security-testing/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/web-security-testing --- # Web Security Testing Orchestrates intelligent skill selection and execution for web security testing 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_target_and_select_scanner( target_url: str, available_scanners: List[Dict], scan_depth: int = 2 ) -> Dict: """Analyze target characteristics and route to optimal security scanner. Evaluates target tech stack, authentication requirements, and historical vulnerability patterns to select the best scanning tool. Args: target_url: The web application endpoint to test available_scanners: List of configured scanner metadata scan_depth: How deep to crawl before scanning (1-3) Returns: Scanner configuration dict with routing metadata """ # Guard clause - Early Exit (Law 1) if not target_url or not urlparse(target_url).netloc: raise ValueError("Invalid target URL format") target_features = _extract_target_telemetry(target_url) auth_type = target_features.get("auth_type", "none") tech_stack = target_features.get("tech_stack", []) best_scanner = None best_score = 0.0 for scanner in available_scanners: score = 0.0 if scanner["type"] == "sast" and "static" in tech_stack: score += 0.4 elif scanner["type"] == "dast" and auth_type == "none": score += 0.5 elif scanner["type"] == "iaast" and auth_type in ["oauth", "saml"]: score += 0.6 if scanner.get("rate_limit") and target_features.get("concurrent_requests", 0) > scanner["rate_limit"]: score -= 0.2 if score > best_score: best_score = score best_scanner = scanner if best_score < 0.3: return {"fallback": True, "reason": "low_match", "target": target_url} # Atomic Predictability (Law 3) - Return new dict, don't mutate result = dict(best_scanner) result["config"] = { "target": target_url, "depth": scan_depth, "auth": auth_type, "timeout": 300 } result["confidence"] = best_score result["timestamp"] = time.time() return result ``` ### Pattern 2: Execution with Fallback ```python def execute_security_scan_with_fallback( scan_config: Dict, max_retries: int = 2 ) -> Dict: """Execute web security scan with resilience patterns for network/tool failures. Implements fail-fast validation and adaptive fallback for: - Rate limiting / WAF blocks - Scanner crashes or timeouts - False positive validation loops Args: scan_config: Output from analyze_target_and_select_scanner max_retries: Retry attempts before escalating Returns: Scan results with vulnerability findings and metadata """ # Guard clause - validate config (Early Exit) if not scan_config or "scanner" not in scan_config: raise ValueError("Invalid scan configuration provided") scanner_name = scan_config["scanner"] target = scan_config["config"]["target"] for attempt in range(max_retries + 1): try: # Execute domain-specific scan raw_results = _run_scanner_tool(scanner_name, scan_config["config"]) # Validate and deduplicate findings validated_findings = _deduplicate_vulnerabilities(raw_results) # Success - Atomic Predictability (Law 3) return { "success": True, "scanner": scanner_name, "findings_count": len(validated_findings), "findings": validated_findings, "attempts": attempt + 1, "latency_ms": _measure_execution_time() } except RateLimitError: # Fail Fast - Don't try to patch bad data (Law 4) if attempt < max_retries: time.sleep(2 ** attempt * 10) # Exponential backoff continue return _escalate_to_manual_review(target, "rate_limited") except ScannerCrashError: if attempt < max_retries: continue return _fallback_to_alternative_scanner(target, scan_config) # All retries exhausted - Fail Loud (Law 4) return { "success": False, "error": "scan_exhausted_retries", "target": target, "timestamp": time.time() } ``` ### 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. - [OWASP Top Ten Web Application Security Risks](https://owasp.org/www-project-top-ten/) - [OWASP Testing Guide v4](https://owasp.org/www-project-web-security-testing-guide/latest/) - [NIST SP 800-115 — Technical Guide to Information Security Testing](https://csrc.nist.gov/publications/detail/sp/800-115/final) - [Rapid7 Metasploit Framework Documentation](https://docs.rapid7.com/metasploit/) - [Burp Suite Professional User Guide](https://portswigger.net/burp/documentation) ## Related Skills | Skill | Purpose | |