Analyze RIOT logs with pattern matching and hypothesis generation.
npx skills add https://github.com/weifuchow/work-agent-os --skill riot-log-triage
Please help me install this Agent Skill. Skill: riot-log-triage Source: https://github.com/weifuchow/work-agent-os/tree/main/.claude/skills/riot-log-triage Command: npx skills add https://github.com/weifuchow/work-agent-os --skill riot-log-triage
This Skill enables detailed, multi-stage analysis of RIOT project logs and现场问题， helping users diagnose root causes by correlating log patterns, code snippets, and system states.
Use the RIOT log triage skill to analyze the logs and attachments provided in the folder, utilizing the SKILL.md instructions to identify anomalous patterns and potential system阻塞点。
High-intent search queries and answers about installing and using this skill.
You can analyze distributed logs by running Python scripts to traverse log directories, normalize timestamps, and match anomalous patterns. This multi-stage workflow routes logs, generates hypotheses, and validates evidence to infer root causes for unexpected state changes.
Troubleshooting现场问题 and abnormal stack traces in complex distributed environments requires structured evidence extraction. By applying multi-round pattern matching to trace data, you can correlate system states with code snippets to pinpoint network delays and root causes.
Yes, Python scripts using the json, re, and pathlib modules can investigate network delays in logback or log4j2 files. These scripts traverse distributed log archives, match error patterns, and extract structured evidence for hypothesis validation.
Log pattern matching works for diagnosing why a scheduled vehicle did not move by extracting relevant log entries and attachments from exported archives. It correlates network delays with unexpected状态变化 to validate hypotheses against actual system states.
Prerequisites for multi-round log analysis of RIOT project issues include exported log directories containing logback or log4j2 files and a Python environment with json, re, datetime, and pathlib libraries. These enable the scripts to perform traversal and structured evidence extraction.
You should not use a structured troubleshooting workflow for simple single-file logs lacking complex状态变化 or附件分析 requirements. This multi-stage hypothesis generation and evidence validation process is specifically designed for complex distributed log environments.
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