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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

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Error Handling

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

Copy# ✅ 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

Copy# ✅ 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:

Copyimport 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

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

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Data Processing

Core Rule: Prefer Polars over Pandas for dataframe operations.

Exception mechanism: Add at file top:

Copy# polars-exception: MLflow requires Pandas DataFrames
import pandas as pd

See ml-data-pipeline-architecture for decision tree and benchmarks.

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Related Skills

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Reference Documentation

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