observability-analyze-logs
Parses OpenTelemetry-formatted logs to reconstruct execution traces, extract errors with call chains, and provide AI-powered root cause analysis. Use when investigating errors, checking logs, debugging issues, viewing traces, or analyzing execution flow. Triggers on "check the logs", "analyze errors", "what's failing", "debug this issue", "show me the traces", or "investigate the error".
npx skills add majiayu000/claude-skill-registry --skill observability-analyze-logs-dawiddutoit-custom-claude-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Intelligent log analysis for any project using OpenTelemetry trace reconstruction and AI-powered error diagnosis. Works with projects that generate OpenTelemetry-formatted logs in a configurable log directory.
Core capabilities:
- Parse OpenTelemetry-formatted logs with trace/span IDs
- Reconstruct complete execution traces
- Extract errors with full call chain context
- AI-powered root cause analysis
- Multiple output formats (summary, markdown, JSON)
- Advanced filtering (by error ID, trace ID, file)
How it works
- Triggered by phrases such as "check the logs", "analyze errors", "show me the traces", or "investigate the error".
- Quick Health Check uses a default summary mode:
- Run:
python3 .claude/tools/utils/log_analyzer.py {{LOG_DIR}}/{{LOG_FILE}}.log - Output includes a compact table with error IDs and trace IDs.
- Run:
- For deeper inspection, you can:
- Use
--error-id <ID>to get detailed error information (full message, call chain, stack trace). - Use
--trace <TRACE_ID>to view all errors in a trace with execution context. - Use
--file <filename>to find errors in a specific file.
- Use
- Real-time monitoring uses
tail -f {{LOG_DIR}}/{{LOG_FILE}}.log. - Outputs can be formatted in markdown or JSON, and there are performance options like
--no-aito skip AI analysis.
When to use it
- When investigating errors, checking logs, debugging issues, viewing traces, or analyzing execution flow.
- When you need quick health checks or trace-based debugging across distributed operations.
What it can touch
- Commands involve the Bash tool to execute the specified Python script and standard shell utilities (e.g., tail).
- Filtering and output options are invoked via the log_analyzer.py CLI using flags such as
--error-id,--trace,--file,--format, and--no-ai.
Caveats
- License: MIT
- Declared tools: Bash, Read
- Requires that logs follow the OpenTelemetry format with trace/span IDs.
- AI analysis results depend on the --format rendering (markdown/JSON) and may include root-cause analysis and patterns.
# Analyze Logs ## Table of Contents ### Core Sections - [What This Skill Does](#what-this-skill-does) - Intelligent OpenTelemetry log analysis with trace reconstruction - [When to Use This Skill](#when-to-use-this-skill) - Trigger phrases and common scenarios - [Quick Start](#quick-start) - Most common usage for quick health checks - [Analysis Workflow](#analysis-workflow) - Complete step-by-step implementation guide - [Step 1: Determine User's Need](#step-1-determine-users-need) - Identify analysis type (health check, error investigation, trace debugging) - [Step 2: Choose Analysis Mode](#step-2-choose-analysis-mode) - 6 modes: Summary, Error Detail, Trace Analysis, File Filter, Fast Parsing, Real-Time - [Step 3: Execute Analysis](#step-3-execute-analysis) - Running commands with Bash tool - [Step 4: Interpret Results](#step-4-interpret-results) - Understanding summary tables, detailed output, AI analysis - [Step 5: Report Findings](#step-5-report-findings) - Communicating results to users - [Command Reference](#command-reference) - All analyzer commands with examples - Basic Commands (summary, markdown, JSON) - Filtering Commands (--error-id, --trace, --file) - Performance Comman
- Table of Contents
- Core Sections
- Advanced Topics
- Supporting Resources
- Purpose
- What This Skill Does
- When to Use This Skill
- Quick Start
- Instructions
- Step 1: Determine User's Need
- Step 2: Choose Analysis Mode
- Step 3: Execute Analysis
- Step 4: Interpret Results
- Step 5: Report Findings
python3 .claude/tools/utils/log_analyzer.py {{LOG_DIR}}/{{LOG_FILE}}.log
python3 .claude/tools/utils/log_analyzer.py --error-id 1 --format markdown
python3 .claude/tools/utils/log_analyzer.py --trace TRACE_ID --format markdown
python3 .claude/tools/utils/log_analyzer.py --file database.py --format markdown
python3 .claude/tools/utils/log_analyzer.py --no-ai
tail -f {{LOG_DIR}}/{{LOG_FILE}}.log
Example for summary
Quick summary (default)
Detailed markdown with AI analysis
python3 .claude/tools/utils/log_analyzer.py --format markdownWhat does the observability-analyze-logs skill do?
Parses OpenTelemetry-formatted logs to reconstruct execution traces, extract errors with call chains, and provide AI-powered root cause analysis. Use when investigating errors, checking logs, debugging issues, viewing traces, or analyzing execution flow. Triggers on "check the logs", "analyze errors", "what's failing", "debug this issue", "show me the traces", or "investigate the error".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill observability-analyze-logs-dawiddutoit-custom-claude-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.