Add structlog logging and retry decorators to Python pipeline scripts.
npx skills add https://github.com/fstr21/sportsbot --skill pipeline-logger-skill
Please help me install this Agent Skill. Skill: Pipeline Logger Skill Source: https://github.com/fstr21/sportsbot/tree/main/.claude/skills/pipeline-logger Command: npx skills add https://github.com/fstr21/sportsbot --skill pipeline-logger-skill
Manual print() statements make debugging complex data pipelines a nightmare, and API calls often lack robust retry mechanisms, leading to brittle data captures. This Skill standardizes logging and adds resilience, saving you hours of manual debugging.
print() statements with structlog calls, creating parseable JSON logs for easier analysis.@conservative_retry decorators to API functions, ensuring robust error handling and reducing data loss from transient failures.python .claude/skills/pipeline-logger/scripts/add_logging.py pipelines/nhl/capture/01_fetch_nhl_odds.py
High-intent search queries and answers about installing and using this skill.
Structured logging replaces print() with logger.info/warning/error calls that output JSON logs, making debugging easier. This Skill automatically converts print statements to structlog calls in your Python scripts, preserving logic while enabling parseable log analysis.
Yes. This Skill inserts @conservative_retry decorators on API functions to handle transient failures automatically. Your existing pipeline logic stays intact while gaining robust error handling and reduced data loss from temporary failures.
A backup (.bak) copy is created before modification, and all original behavior is preserved. Only logging and retry decorators are added, so your script's core functionality and output remain unchanged.
Yes. The Skill applies to Python pipeline scripts across NFL, NHL, NCAAF, and Soccer pipelines—capture, package, derive, and fetch scripts. It standardizes logging and retries across your entire sports data infrastructure.
JSON logs are written to logs/{script_name}/YYYY-MM-DD.jsonl, organizing structured output by script and date. This enables time-based log analysis and easier troubleshooting of pipeline execution.
The Skill requires structlog for JSON logging and tenacity for retry logic. Both are open-source Python libraries that integrate seamlessly into your existing pipeline code.
pradyumnasagarAutomate inter-skill data transfer and provenance capture via JSON Material Passports.
timbuchingerCraft efficient Grafana Loki LogQL queries for debugging logs.
korallisImplement monitoring, logging, metrics, and tracing with Grafana and Sentry.
bobmatnycParse, serialize, validate, and transform JSON data in Python and JavaScript.