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AWS Lambda Powertools Logger gotchas and patterns. ALWAYS invoke when working with Powertools Logger, Lambda logging, or structured logging. Reserved key collisions cause runtime KeyErrors — do not answer from memory.
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SOC 職業分類に基づく
AWS credential management with awsume. ALWAYS invoke when assuming AWS roles, listing accounts, switching profiles, or running any awsume command. Do not guess flags from memory.
Behavior-Driven Development with pytest-bdd. ALWAYS invoke when writing Gherkin scenarios, pytest-bdd step definitions, or any BDD acceptance tests. Never write feature files without this skill loaded.
Commit message convention. ALWAYS invoke before creating any git commit or writing a commit message. Never format a commit without this skill loaded.
Domain-Driven Design tactical patterns. ALWAYS invoke when implementing aggregates, entities, value objects, repositories, or domain events in Python. Prevents common DDD mistakes (anemic models, multi-aggregate transactions).
Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service -- even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer -- do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.
Git branch comparison and diff workflows using worktrees. ALWAYS invoke when comparing branches, diffing synth output, or needing to work with multiple branches simultaneously. Never stash or checkout to compare — use worktrees.
| name | powertools-logger |
| model | claude-haiku-4-5-20251001 |
| description | AWS Lambda Powertools Logger gotchas and patterns. ALWAYS invoke when working with Powertools Logger, Lambda logging, or structured logging. Reserved key collisions cause runtime KeyErrors — do not answer from memory. |
SOP for safe usage of AWS Lambda Powertools Logger. You MUST invoke this before writing any logging code in Lambda — reserved key violations cause runtime KeyError crashes that are difficult to debug.
Constraints:
extra={} or as direct kwargs — they throw KeyError at runtime:
message, name, msg, args, levelname, levelno, pathname, filename, module,
exc_info, exc_text, stack_info, lineno, funcName, created, msecs,
relativeCreated, thread, threadName, processName, processmessage, you MUST rename it (e.g. result_message, response_message)logger.debug("Calling tool", args=args) # ❌ MUST NOT — KeyError at runtime
logger.debug("Calling tool", tool_args=args) # ✅ MUST use renamed key
Constraints:
extra# ❌ Risky if result dict contains "message"
logger.info("Done", **result)
# ✅ Safe — keep nested
logger.info("Done", extra={"result": result})
Constraints:
logger.info("Order processed", extra={"order_id": "123"}) # style A: extra dict
logger.info("Order processed", order_id="123") # style B: direct kwargs
# Both produce identical JSON output
Constraints:
Logger(child=True) in submodules — inherits service name, log level, and config from root Logger()Logger() in submodules — it creates a separate logger with no shared config