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observability-patterns

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UpdatedMarch 4, 2026 at 08:37

Use this skill when implementing logging, monitoring, timing, or diagnostics. Triggers on: logging setup, performance profiling, timing, stderr output, or mentions of "logging", "monitoring", "timing", "profiling", "observability", or "diagnostics".

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CodeHalwell

CodeHalwell/DoclingOptimisation

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Related occupationsSOC

Based on SOC occupation classification

Software DevelopersComputer and Mathematical Occupations·SOC 15-1252

SKILL.md

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name	observability-patterns
description	Use this skill when implementing logging, monitoring, timing, or diagnostics. Triggers on: logging setup, performance profiling, timing, stderr output, or mentions of "logging", "monitoring", "timing", "profiling", "observability", or "diagnostics".

Observability Patterns — DoclingOptimisation

Current Logging

Python logging module with custom format (%(message)s)
All logs go to stderr (stdout reserved for markdown output)
Timing helper _step() logs elapsed time since process start
Pipeline profiling enabled: settings.debug.profile_pipeline_timings = True

Logging Standards

Use logging.getLogger("docling-processor") for the main logger
Include elapsed time in all step messages
Log CPU detection results, accelerator config, batch sizes
Log input file metadata (name, size)
Log conversion timing (convert duration, export duration, total)

For Azure Deployment

Container App Job logs go to Azure Log Analytics
Consider structured JSON logging for machine-parseable output
Track: conversion time, file size, thread count, model used
Set up alerts for: job failures, timeouts, excessive memory usage

Health Monitoring

Container App Jobs don't need health check endpoints
Monitor job execution status via Azure Container Apps API
Track job duration trends to detect performance regressions

More from this repository

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change-history

CodeHalwell/DoclingOptimisation

Maintain a structured JSON history of project changes in .history/changes.json. Use this skill after making any medium-to-large changes to the codebase. This includes: new files, API changes, schema changes, dependency changes, config changes, refactors, Dockerfile changes, and significant bug fixes. Do NOT log formatting-only changes, typo fixes, or trivial single-line edits. When in doubt, log it.

2026-03-040

docker-cicd-patterns

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Use this skill when creating or modifying Dockerfiles, docker-compose files, GitHub Actions workflows, or deployment pipelines. Triggers on: container builds, CI/CD configuration, deployment automation, Azure Container App Jobs, Docker Hub, or any mention of "Docker", "container", "GitHub Actions", "workflow", "CI/CD", "pipeline", "deploy", or "build".

2026-03-040

git-workflow-patterns

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Use this skill when working on branching, PRs, commits, or releases. Triggers on: branch naming, PR workflow, commit messages, release strategy, or mentions of "branch", "PR", "commit", "release", "versioning", "changelog", or "git workflow".

2026-03-040

security-patterns

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Use this skill when working on credentials, secrets, environment variables, Docker security, or any security-related concern. Triggers on: secrets management, credential handling, Docker security, environment variables, or mentions of "security", "secrets", "credentials", "env vars", or "vulnerability".

2026-03-040

name	observability-patterns
description	Use this skill when implementing logging, monitoring, timing, or diagnostics. Triggers on: logging setup, performance profiling, timing, stderr output, or mentions of "logging", "monitoring", "timing", "profiling", "observability", or "diagnostics".

Observability Patterns — DoclingOptimisation

Current Logging

Python logging module with custom format (%(message)s)
All logs go to stderr (stdout reserved for markdown output)
Timing helper _step() logs elapsed time since process start
Pipeline profiling enabled: settings.debug.profile_pipeline_timings = True

Logging Standards

Use logging.getLogger("docling-processor") for the main logger
Include elapsed time in all step messages
Log CPU detection results, accelerator config, batch sizes
Log input file metadata (name, size)
Log conversion timing (convert duration, export duration, total)

For Azure Deployment

Container App Job logs go to Azure Log Analytics
Consider structured JSON logging for machine-parseable output
Track: conversion time, file size, thread count, model used
Set up alerts for: job failures, timeouts, excessive memory usage

Health Monitoring

Container App Jobs don't need health check endpoints
Monitor job execution status via Azure Container Apps API
Track job duration trends to detect performance regressions