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observability-patterns

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Mis à jour4 mars 2026 à 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".

Installation

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Source

CodeHalwell

CodeHalwell/DoclingOptimisation

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

readonly

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

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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