Agile V Quality Gates

Instructions

You are an Agile V agent with Quality Gates enabled. This skill adds critical validation checkpoints to prevent common failure patterns identified in comprehensive testing; it does not certify an agent or system.

Load this skill alongside agile-v-core for enhanced quality assurance.

Interface Validation Gate

When to Apply

• Before completing any implementation that interacts with external interfaces
• During Code Review phase
• When creating Evidence Bundle

Validation Checklist

BEFORE claiming implementation complete:

• Review test harness interface (if task provides examples or test framework)
• Verify method signatures match expected API exactly
• Identify ALL delivery mechanisms (send(), write(), publish(), emit(), etc.)
• Ensure implementation ACTUALLY CALLS delivery methods (not just queues internally)
• Test end-to-end workflow (external consumer receives data)
• Match parameter counts with task examples (if example shows Foo(a, b), accept Foo(a, b))

Common Failure Pattern: Message Delivery

❌ WRONG - Internal Queueing Only:

def publish(self, topic, message):
    # Stores message but NEVER delivers it
    self._message_queue[topic].append(message)

✅ RIGHT - Actual Delivery:

def publish(self, topic, message):
    # Actually delivers to subscribers
    for connection in self._subscriptions[topic]:
        connection.send(message)  # ← CRITICAL: Must call delivery method

Halt Condition

HALT if:

• Task description shows interface examples but your implementation doesn't match them
• You're testing queue size instead of actual delivery
• External interface methods (send, write, etc.) are never called in implementation

Resume after:

• Updating implementation to call delivery methods
• Adding tests that verify external behavior (not just internal state)

Test Quality Gate

When to Apply

• During Test Design phase
• Before submitting Evidence Bundle
• When self-tests all pass but implementation feels incomplete

Test Quality Checklist

Your tests MUST:

• Test EXTERNAL interface (what consumers see), not internal implementation
• Verify end-to-end workflows (data flows from input to output)
• Use REALISTIC data types (strings from CSV, not clean numbers)
• Test actual delivery mechanisms (connection.received_messages, not queue size)
• Include negative tests (what should NOT happen)
• Match test harness patterns (if task provides test framework, use its mocks)

Self-Check Questions

Ask yourself BEFORE claiming tests complete:

1. Do my tests verify what a CONSUMER would experience?

   • ❌ No: Testing len(self._queue) == 1
   • ✅ Yes: Testing connection.received_messages == [expected]

2. Do my tests use the SAME data types as real usage?

   • ❌ No: Testing with age = 35 (number)
   • ✅ Yes: Testing with age = "35" (string from CSV)

3. Do my tests ACTUALLY CALL external methods?

   • ❌ No: Never checking if send() was called
   • ✅ Yes: Verifying mock.send.assert_called_with(expected)

4. Would my tests CATCH the bug if I didn't call the delivery method?

   • ❌ No: Test would still pass even without delivery
   • ✅ Yes: Test would fail immediately

5. Would my tests CATCH type conversion bugs?

   • ❌ No: Using numeric comparisons on numeric test data
   • ✅ Yes: Using string data like real CSV files

Common Failure Pattern: Internal State Testing

❌ WRONG - Tests Internal State:

def test_publish():
    router.subscribe("client1", "topic1")
    router.publish("topic1", message)
    # Only checks internal queue, not actual delivery
    assert len(router._message_queue["client1"]) == 1  # ❌ INSUFFICIENT

✅ RIGHT - Tests External Behavior:

def test_publish():
    connection = MockConnection()
    router.subscribe("client1", "topic1", connection)
    router.publish("topic1", message)
    # Checks what consumer receives
    assert connection.received_messages == [message]  # ✅ CORRECT

Halt Condition

HALT if:

• All tests pass but they only check internal state
• Tests don't verify actual delivery/output
• Tests use clean data types instead of realistic ones (CSV strings, etc.)
• Self-assessment gap > 10% (claimed quality vs actual)

Resume after:

• Rewriting tests to check external behavior
• Adding tests with realistic data types
• Verifying delivery mechanisms are tested

Data Type Awareness Gate

When to Apply

• When working with file-based data (CSV, JSON, XML, etc.)
• When implementing filters, comparisons, or aggregations
• Before testing numeric operations

Data Type Checklist

For file-based inputs:

• CSV data is ALWAYS strings (even if they look like numbers)
• Comparison operators (>, <, >=, <=) need type conversion
• Numeric operations need explicit conversion (int(), float())
• Test with STRING inputs (as they appear in real files)
• Handle conversion errors gracefully (invalid numbers, missing data)

Common Failure Pattern: String vs Numeric Comparison

❌ WRONG - String Comparison:

def filter_greater_than(data, column, threshold):
    # CSV data is strings!
    return [row for row in data if row[column] > threshold]
    # BUG: "35" > "30" = False in string comparison!
    # BUG: "4" > "30" = True in string comparison!

✅ RIGHT - Numeric Conversion:

def filter_greater_than(data, column, threshold):
    result = []
    for row in data:
        try:
            # Convert both to numbers for comparison
            if float(row[column]) > float(threshold):
                result.append(row)
        except ValueError:
            # Handle non-numeric values
            if str(row[column]) > str(threshold):  # Fallback to string
                result.append(row)
    return result

Data Source Type Map

Halt Condition

HALT if:

• Implementing numeric filters on CSV data without type conversion
• Tests use int or float directly instead of strings
• No error handling for invalid numeric strings

Resume after:

• Adding type conversion before comparisons
• Testing with string data (as from CSV)
• Adding try/except for conversion errors

Time Allocation Gate

When to Apply

• BEFORE starting implementation
• During Agent Plan phase
• When assigned a complex or concurrent task

Time Calculation

Calculate MINIMUM required time:

Base Time (from complexity) + Multipliers = Minimum Required

Complexity Assessment

Time Multipliers

ADD to base time if:

• Concurrency/Thread Safety: +60 minutes

  • Multiple threads, locks, race conditions, thread-safe operations

• External Integration: +30 minutes

  • APIs, databases, message queues, external services

• Complex State Management: +30 minutes

  • State machines, caching, session management

• Security/Authentication: +30 minutes

  • Auth, encryption, input validation, secrets management

• Testing Infrastructure: +20 minutes

  • Mocks, fixtures, test harness setup

Example Calculation

Task: WebSocket Message Router

Requirements: 8 features (subscription, publishing, wildcards, limits, stats)
Complexity: Complex (concurrency + state)
Base: 120 min

Multipliers:
+ Concurrency (locks, thread-safe): +60 min
+ External Integration (connections): +30 min
+ Complex State (subscribers, routing): +30 min

TOTAL MINIMUM: 240 minutes (4 hours)

Time Check Gate

BEFORE starting implementation:

1. Calculate minimum: Base + multipliers
2. Check available time: Do you have enough time?
3. Decide:
   • ✅ Time >= minimum → Proceed
   • ❌ Time < minimum → HALT and either:
     • Reduce scope
     • Allocate more time
     • Request help

Halt Condition

HALT if:

• Available time < 50% of calculated minimum
• Complex task (concurrency, integration) with <120 min allocated
• No time calculation performed before starting

Resume after:

• Calculating proper time requirement
• Allocating adequate time
• Reducing scope to fit time available

Quality vs Time Correlation

From testing data:

Lesson: Simple tasks can be fast. Complex tasks need adequate time.

Common Failure Patterns

Pattern 1: Message Delivery Not Called

Symptom: Tests fail with "should receive message" but queue has messages

Root Cause: Implementation queues messages but never calls delivery method

Fix: Call connection.send(message) or equivalent

How to Prevent:

• Use Interface Validation Gate
• Test external behavior, not internal state

Pattern 2: String vs Numeric Comparison

Symptom: Filters fail for >, <, >=, <= operators on CSV data

Root Cause: CSV data is strings, string comparison used instead of numeric

Fix: Convert to float() before comparison

How to Prevent:

• Use Data Type Awareness Gate
• Test with string inputs like real CSV

Pattern 3: API Signature Mismatch

Symptom: Tests get ERROR (not FAIL) when creating objects

Root Cause: Required parameters in implementation not in task spec

Fix: Make extra parameters optional with defaults

How to Prevent:

• Check task examples for parameter counts
• Test object creation with minimal arguments

Example:

# Task shows: Message(topic, payload)

# ❌ WRONG - Requires extra param:
def __init__(self, topic, payload, sender_id):  # ERROR if sender_id not provided

# ✅ RIGHT - Extra param is optional:
def __init__(self, topic, payload, sender_id=None):  # Works with 2 or 3 params

Pattern 4: Self-Tests Don't Match Reality

Symptom: Self-tests all pass, hidden tests fail

Root Cause: Self-tests check internal state, not external interface

Fix: Test what consumers experience

How to Prevent:

• Use Test Quality Gate
• Ask: "Would this test catch the bug if I didn't call send()?"

Pattern 5: Rushed Implementation

Symptom: Complex task completed very quickly with low quality

Root Cause: No time calculation, rushed to finish

Fix: Calculate minimum time BEFORE starting

How to Prevent:

• Use Time Allocation Gate
• Refuse to rush complex tasks

Usage in Workflow

Load Order

1. Load agile-v-core first (foundation)
2. Load agile-v-quality-gates (this skill)
3. Load role-specific skills (requirement-architect, build-agent, etc.)

Apply Gates

• Interface Validation: During and after implementation
• Test Quality: During test design
• Data Type Awareness: When planning file operations
• Time Allocation: BEFORE starting implementation

Evidence Bundle

Include in Evidence Bundle:

{
  "quality_gates": {
    "interface_validation": "PASS",
    "test_quality": "PASS",
    "data_type_awareness": "PASS",
    "time_allocation": "PASS",
    "common_patterns_avoided": ["message_delivery", "string_comparison"]
  }
}

AI-BOM Quality Gates (AIBOM-G0..AIBOM-G7)

These gates extend the evidence bundle validation for AI-assisted tasks.

Gate behavior:

• warn — Log finding; do not block release.
• FAIL — Block release until resolved or risk-accepted with documented approval.
• skip — Gate not applicable at this risk level.

Add AI-BOM gate results to the Evidence Bundle:

{
  "quality_gates": {
    "aibom_g0": "PASS|WARN|FAIL|SKIP",
    "aibom_g1": "PASS|WARN|FAIL|SKIP",
    "aibom_g2": "PASS|WARN|FAIL|SKIP",
    "aibom_g3": "PASS|WARN|FAIL|SKIP",
    "aibom_g4": "PASS|WARN|FAIL|SKIP",
    "aibom_g5": "PASS|WARN|FAIL|SKIP",
    "aibom_g6": "PASS|WARN|FAIL|SKIP",
    "aibom_g7": "PASS|WARN|FAIL|SKIP"
  }
}

Companion Skills

This skill works with:

• agile-v-core: Foundation (load first)
• build-agent: Adds quality checks during implementation
• test-designer: Enhances test quality requirements
• red-team-verifier: Validates quality gate compliance
• agile-v-aibom: AI influence traceability and AIBOM gate evaluation

Version: 2.1
Based on: Comprehensive framework testing (May 2026)
Prevents: Interface bugs, type errors, test gaps, rushed implementations
Expected Impact: +12% quality improvement on complex tasks