Formulate and test data-driven hypotheses against Axiom datasets.
npx skills add https://github.com/axiomhq/skills --skill axiom-sre
Please help me install this Agent Skill. Skill: axiom-sre Source: https://github.com/axiomhq/skills/tree/main/skills/sre Command: npx skills add https://github.com/axiomhq/skills --skill axiom-sre
This Skill anchors incident investigations to data, enabling hypothesis-driven debugging by querying Axiom observability resources and correlating logs, metrics, and traces. It reduces guesswork during outages and accelerates root cause identification.
To begin, activate the skill in a live incident and run: scripts/init to discover available environments, then scripts/setup to configure memory and Axiom access, and finally run scripts/axiom-query to fetch logs for your investigation.
High-intent search queries and answers about installing and using this skill.
Hypothesis-driven incident investigation requires stating, testing, and falsifying hypotheses against Axiom datasets using targeted APL queries. You correlate logs, metrics, and traces to verify relationships between error spikes, latency, and service health during production outages.
Triage production outages by applying golden signals and RED/USE methodologies to correlate logs, metrics, and traces. This disciplined workflow anchors root cause analysis to data-driven observations rather than guesswork, accelerating service recovery.
Query Axiom datasets by formulating targeted APL queries that test specific hypotheses about error spikes and latency. Fetch logs using the axiom-query script after configuring memory and Axiom access to verify correlations between service health metrics.
Yes, you need curl and jq installed along with git, python3, cloudflared, and coreutils. These dependencies support the scripts that configure Axiom access, fetch observability data, and process query results during incident response.
Yes, the memory system persists investigation patterns, facts, and insights in a shared knowledge base. This memory-backed reasoning captures correlations and outcomes during incidents, enabling faster root cause identification in future investigations.
Memory-backed reasoning improves SRE workflows by storing patterns and facts from previous investigations in a shared knowledge base. This reduces guesswork during outages by providing historical context that accelerates root cause identification and correlation analysis.
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