Agent skill
impl-standards
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.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/product/impl-standards-terrylica-cc-skills
SKILL.md
Implementation Standards
Apply these standards during implementation to ensure consistent, maintainable code.
When to Use This Skill
- During
/itp:goPhase 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 |
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.
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.
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")
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 &
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 |
Reference Documentation
- Error Handling - Raise + propagate patterns
- Constants Management - Magic number abstraction
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