# E2e Testing > Implements intelligent e2e testing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/e2e-testing` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/e2e-testing` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/e2e-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/e2e-testing --- # E2E Testing Orchestrates intelligent skill selection and execution for e2e 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 orchestrate_e2e_suite( test_manifest: List[Dict], environment_state: Dict, browser_pool: List[Dict] ) -> List[Dict]: """Orchestrate E2E test execution by routing tests to optimal browser/environment combos. Evaluates test requirements against available browser contexts and environment health. Applies flakiness history to schedule unstable tests during low-traffic windows. Args: test_manifest: List of test specs with required browsers, auth types, and endpoints environment_state: Current state of staging/prod-like environments browser_pool: Available browser contexts with version and capability metadata Returns: Ordered execution plan mapping tests to specific browser contexts """ execution_plan = [] for test in test_manifest: required_browser = test.get("target_browser", "chromium") requires_auth = test.get("auth_type") == "oauth2" is_flaky = test.get("historical_flakiness", 0) > 0.3 # Filter available browsers by capability and version compatible_browsers = [ b for b in browser_pool if b["engine"] == required_browser and b["version"] >= test.get("min_version", "110") ] if not compatible_browsers: raise ValueError(f"No compatible {required_browser} context for test {test['id']}") # Select browser with lowest current load, prioritizing stable contexts for flaky tests selected_context = min( compatible_browsers, key=lambda b: (b["current_load"], 0 if not is_flaky else 1) ) execution_plan.append({ "test_id": test["id"], "browser_context": selected_context["id"], "auth_setup": _configure_auth_session(requires_auth, environment_state["auth_endpoint"]), "network_mock": _apply_api_mocks(test.get("mock_endpoints", [])), "timeout_ms": test.get("timeout", 30000) }) return execution_plan ``` ### Pattern 2: Execution with Fallback ```python def execute_test_with_resilience( test_plan: Dict, test_runner: object, diagnostic_store: object ) -> Dict: """Execute a single E2E test with built-in resilience and diagnostic fallback. Implements retry logic for transient network/UI issues, captures screenshots/video on failure, and falls back to lightweight smoke assertions if full DOM interaction fails. Args: test_plan: Execution plan generated by orchestrate_e2e_suite test_runner: Initialized Playwright/Cypress runner instance diagnostic_store: Storage backend for screenshots, traces, and logs Returns: Test result dict with status, duration, and diagnostic artifacts """ max_retries = 2 last_error = None page = None for attempt in range(max_retries + 1): try: context = test_runner.new_context( base_url=test_plan["base_url"], storage_state=test_plan["auth_setup"], extra_http_headers=test_plan.get("headers", {}) ) page = context.new_page() for mock in test_plan["network_mock"]: page.route(mock["pattern"], mock["handler"]) result = test_runner.run_steps(page, test_plan["steps"]) diagnostic_store.save_trace(page, test_plan["test_id"], attempt) return { "status": "passed", "test_id": test_plan["test_id"], "duration_ms": result["elapsed"], "attempts": attempt + 1, "artifacts": diagnostic_store.get_latest(test_plan["test_id"]) } except (TimeoutError, NetworkError, AssertionError) as e: last_error = e if page: diagnostic_store.capture_screenshot(page, test_plan["test_id"], attempt) diagnostic_store.capture_console_logs(page, test_plan["test_id"]) if attempt < max_retries: time.sleep(2 ** attempt) continue fallback_result = _run_lightweight_smoke_check(test_plan["base_url"], test_plan["critical_paths"]) return { "status": "flaky" if fallback_result["passed"] else "failed", "test_id": test_plan["test_id"], "error": str(last_error), "fallback_status": fallback_result["status"], "artifacts": diagnostic_store.get_latest(test_plan["test_id"]) } ``` ### 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 ## Related Skills | Skill | Purpose | |