paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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 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
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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 | |