Is this skill suitable for Docker environments?

Yes, it is specifically designed for structured logging in containerized applications and services managed via Docker Compose.

What is required to use the Logfire Read skill?

You need a Logfire Read URL and a valid Logfire Read Token configured in your environment variables to authenticate with the API.

Can I filter logs by specific error messages?

Yes, the skill includes a --message-like filter and severity level filters to pinpoint specific entries in your logs.

What output format does this skill use?

The skill primarily outputs logs in JSONL (JSON Lines) format, which provides one JSON object per record for easy processing.

Can I use this skill programmatically in my own Python scripts?

Absolutely. The skill includes a 'query_logfire()' function that can be imported and used in your Python codebase for custom integrations.

Logfire Log Reader

Name: Logfire Log Reader
Author: michaeltschreiber

bymichaeltschreiber

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Analytics & Monitoring

Queries and retrieves structured Python logs from the Logfire API using specialized CLI tools and JSONL output.

About

The Logfire Read skill enables Claude to interact directly with the Logfire Query API to fetch, filter, and analyze structured logs from containerized Python applications. It provides agent-friendly CLI scripts and a programmatic Python API to retrieve records in JSONL format, making it easier to diagnose production issues, track specific error patterns, and monitor application health. By streamlining the retrieval of logs using read tokens and environment-based configuration, it allows for deep observability and automated root cause analysis within your development workflow.

Key Features

Support for JSONL output to enable easy machine parsing
Programmatic Python API for custom log processing and integration
4 GitHub stars
Agent-optimized CLI for targeted log retrieval
Environment-driven configuration for secure read token management
Advanced filtering by severity level, message patterns, and timestamps

Use Cases

Retrieving historical log data for specific time windows to analyze past system behavior
Troubleshooting failed processes by searching for specific log messages and severity levels
Automating log-based debugging within Claude Code to accelerate incident response

About

Key Features

Support for JSONL output to enable easy machine parsing
Programmatic Python API for custom log processing and integration
4 GitHub stars
Agent-optimized CLI for targeted log retrieval
Environment-driven configuration for secure read token management
Advanced filtering by severity level, message patterns, and timestamps

Use Cases

Retrieving historical log data for specific time windows to analyze past system behavior
Troubleshooting failed processes by searching for specific log messages and severity levels
Automating log-based debugging within Claude Code to accelerate incident response