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name: impl-standards description: Core engineering standards during implementation. Use when implementing features, writing production code, or when user mentions error handling, constants management, progress logging, or code quality standards.

Implementation Standards

Apply these standards during implementation to ensure consistent, maintainable code.

When to Use This Skill

• During /itp:go Phase 1
• When writing new production code
• User mentions "error handling", "constants", "magic numbers", "progress logging"
• Before release to verify code quality

Quick Reference

Standard	Rule
Errors	Raise + propagate; no fallback/default/retry/silent
Constants	Abstract magic numbers into semantic, version-agnostic dynamic constants
Dependencies	Prefer OSS libs over custom code; no backward-compatibility needed
Progress	Operations >1min: log status every 15-60s
Logs	logs/{adr-id}-YYYYMMDD_HHMMSS.log (nohup)
Metadata	Optional: catalog-info.yaml for service discovery

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Error Handling

Core Rule: Raise + propagate; no fallback/default/retry/silent

# ✅ Correct - raise with context
def fetch_data(url: str) -> dict:
    response = requests.get(url)
    if response.status_code != 200:
        raise APIError(f"Failed to fetch {url}: {response.status_code}")
    return response.json()

# ❌ Wrong - silent catch
try:
    result = fetch_data()
except Exception:
    pass  # Error hidden

See Error Handling Reference for detailed patterns.

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Constants Management

Core Rule: Abstract magic numbers into semantic constants

# ✅ Correct - named constant
DEFAULT_API_TIMEOUT_SECONDS = 30
response = requests.get(url, timeout=DEFAULT_API_TIMEOUT_SECONDS)

# ❌ Wrong - magic number
response = requests.get(url, timeout=30)

See Constants Management Reference for patterns.

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Progress Logging

For operations taking more than 1 minute, log status every 15-60 seconds:

import logging
from datetime import datetime

logger = logging.getLogger(__name__)

def long_operation(items: list) -> None:
    total = len(items)
    last_log = datetime.now()

    for i, item in enumerate(items):
        process(item)

        # Log every 30 seconds
        if (datetime.now() - last_log).seconds >= 30:
            logger.info(f"Progress: {i+1}/{total} ({100*(i+1)//total}%)")
            last_log = datetime.now()

    logger.info(f"Completed: {total} items processed")

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Log File Convention

Save logs to: logs/{adr-id}-YYYYMMDD_HHMMSS.log

# Running with nohup
nohup python script.py > logs/2025-12-01-my-feature-20251201_143022.log 2>&1 &

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Related Skills

Skill	Purpose
adr-code-traceability	Add ADR references to code
code-hardcode-audit	Detect hardcoded values before release
semantic-release	Version management and release automation

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Reference Documentation

• Error Handling - Raise + propagate patterns
• Constants Management - Magic number abstraction