MetaClaw structured-logging-and-observability

Use this skill when building production services, pipelines, or automation that needs to be debugged, monitored, or audited. Add structured logs, metrics, and health checks before shipping any service.

install

source · Clone the upstream repo

git clone https://github.com/aiming-lab/MetaClaw

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/aiming-lab/MetaClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/memory_data/skills/structured-logging-and-observability" ~/.claude/skills/aiming-lab-metaclaw-structured-logging-and-observability && rm -rf "$T"

manifest: memory_data/skills/structured-logging-and-observability/SKILL.md

source content

Structured Logging and Observability

Log levels:

```
DEBUG
```
: detailed diagnostic (off in production)
```
INFO
```
: normal operation milestones
```
WARNING
```
: recoverable unexpected state
```
ERROR
```
: operation failed, action needed

Structured logs (JSON) over free-form text:

import structlog
log = structlog.get_logger()
log.info("request_complete", method="POST", path="/api/data", status=200, latency_ms=42)

Metrics to expose: request rate, error rate, latency (p50/p95/p99), queue depth.

Health check endpoint:

/health

returning

{"status": "ok"}

— required for load balancers.

Anti-pattern: Logging only on error; you can't diagnose what you didn't observe.