Automating SLO Violation Investigation with Claude Code Skills

Claude Code skills let you define reusable workflows in Markdown files that Claude Code executes repeatedly. For SLO violation investigations, teams monitor Latency and Availability drops with New Relic and then trace the root cause. Uzabase’s Platform Engineering team automated this process with skills, reducing investigation time to 7 minutes per incident. Sources: Uzabase tech blog (June 2026) and official Claude Code Skills documentation (https://code.claude.com/docs/ja/skills).

📑Table of Contents

1. Overview of Claude Code Skills and the Importance of SLO Monitoring
2. Challenges of Traditional SLO Violation Investigation Flows
3. Five Rules for Designing Skills
4. Real-World Application: Automated Investigation of an N+1 Problem
5. Metrics and NRQL Queries Used in the Investigation
6. Effects of Skill Adoption and Time Reduction
7. Additional Improvements: Sub-skill Delegation and Sharing
8. Summary and Recommendation for Knowledge Sharing
9. Frequently Asked Questions (FAQ)
10. Comparison Table: Traditional Investigation vs Skill Automation

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Overview of Claude Code Skills and the Importance of SLO Monitoring

Claude Code skills allow developers to save recurring investigation and operations tasks as reusable instruction sets. In SLO monitoring, New Relic sends Slack notifications when Latency or Availability thresholds are breached. Starting from these notifications, root cause analysis previously required significant manual effort. The Uzabase team automated this flow with skills, improving reproducibility and reducing team burden. According to the official documentation, skills are written in Markdown format and Claude Code interprets and executes them sequentially. This turns tacit operational knowledge into explicit, shareable assets.

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Challenges of Traditional SLO Violation Investigation Flows

Traditional investigations required identifying endpoints from Slack alerts and cross-referencing multiple New Relic dashboards. A single meeting could consume 1.5 to 4 person-hours, and handling several incidents per week quickly accumulated fatigue. N+1 query patterns were easily missed when relying on individual judgment. Manual back-and-forth checks increased the risk of overlooked patterns and lowered reproducibility. Knowledge siloed with individuals, making it vulnerable to team member turnover or absences. Crossing multiple dashboards demanded high concentration and was prone to fatigue-induced errors. Source: Uzabase tech blog (June 2026).

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Five Rules for Designing Skills

The team defined five rules for skill creation. First, prohibit speculation and require an explicit hypothesis before any action. Second, cap token usage at 2K to prevent runaway loops. Third, exit after three failures. Fourth, record every reasoning step for reproducibility. Fifth, use the memory feature to store past findings. These rules follow the official Claude Code Skills documentation. The no-speculation rule prevents unverified assumptions from derailing root cause analysis. The token limit was set empirically as the point where hypotheses typically stabilize. The early-exit rule ensures human intervention remains possible. Memory stores past NRQL queries and findings to improve future accuracy. Source: Claude Code Skills official documentation (https://code.claude.com/docs/ja/skills).

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Real-World Application: Automated Investigation of an N+1 Problem

In one real case, the team investigated an N+1 problem in a video search API. They identified three explicit issues and eight implicit ones using seven NRQL queries. Metrics included Transaction, Datastore Metric, JMX ThreadPool, HikariCP, and ECS Event. The entire analysis finished in 7 minutes, and a design document was ready in 25 minutes. Sources: Uzabase tech blog (June 2026) and New Relic NRQL documentation (https://docs.newrelic.com/jp/docs/nrql/nrql-syntax-clauses-functions/). Explicit issues appear directly in code, while implicit issues are inferred from metric anomalies. The seven queries were designed to cross-reference different layers of the stack. Embedding them in skills ensures the same procedure is followed every time.

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Metrics and NRQL Queries Used in the Investigation

The NRQL queries follow syntax from New Relic’s public documentation. Transaction events track response times, Datastore Metric measures query duration, and JMX monitors thread pool health. This replaces scattered manual checks with a single, repeatable command set. Embedding the queries in skills ensures consistent execution across incidents. Adding HikariCP and ECS Event captures connection pool and container-level anomalies. Source: New Relic NRQL official documentation.

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Effects of Skill Adoption and Time Reduction

After adopting the skills, investigation time dropped to 7 minutes, metric verification consolidated into seven NRQL statements, and N+1 detection became systematic. Knowledge now accumulates in memory and shared documents instead of staying with individuals. The team further splits Latency versus Availability investigations into separate sub-skills for higher accuracy. The time savings directly reduce meeting load. Seven minutes to root cause identification also lightens on-call burden. Higher precision lowers the risk of incorrect remediation proposals. Source: Uzabase tech blog (June 2026).

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Additional Improvements: Sub-skill Delegation and Sharing

Additional improvements include delegating to sub-skills when investigation patterns differ. This keeps token usage low while maintaining precision. Skills are centralized in Notion and Claude Code memory so the entire team can reuse them. Centralization prevents knowledge from remaining siloed. Sub-skill splitting focuses on the different metrics required for Latency versus Availability investigations. Shared skills are stored in a form that individual team members can reproduce independently. Source: Uzabase tech blog (June 2026).

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Summary and Recommendation for Knowledge Sharing

In summary, Claude Code skills improve both speed and reproducibility of SLO violation analysis. The combination of official documentation and New Relic NRQL queries forms the foundation. Start with a simple workflow and expand from there. Begin with a small investigation flow, verify reproducibility, and scale gradually. Skill adoption prevents investigation knowledge from remaining siloed and raises overall operational quality. Create one simple investigation pattern first, measure its effect, and then expand to other patterns. Sources: Claude Code Skills official documentation and New Relic official documentation.

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Frequently Asked Questions (FAQ)

Q: What are Claude Code skills?

Reusable instruction sets defined in Markdown that Claude Code executes for specific workflows. See the official documentation at https://code.claude.com/docs/ja/skills.

Q: How long did SLO violation investigations previously take?

1.5–4 person-hours per meeting, with multiple incidents per week. From Uzabase tech blog (June 2026).

Q: Why prohibit speculation in skills?

To prevent unverified human hypotheses or untested proposals from delaying root cause identification. Based on Claude Code official documentation.

Q: Why set the token limit at 2K?

It was determined empirically as the point where hypotheses typically stabilize and to prevent infinite loops.

Q: How are skills shared within the team?

Centralized in Notion and Claude Code memory for knowledge accumulation.

Q: Should Latency and Availability investigations use separate skills?

Yes, because the required metrics differ; delegating to sub-skills improves accuracy. From the Uzabase case.

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Comparison Table: Traditional Investigation vs Skill Automation

Item	Traditional	After Skill Adoption
Investigation time	1.5-4 person-hours per case	Root cause identified in 7 minutes
Metric verification	Manual dashboard hopping	7 NRQL queries in one pass
N+1 discovery	Individual-dependent, high miss risk	Systematic detection of explicit + implicit issues
Knowledge accumulation	Siloed with individuals	Stored in memory and shared docs

Sources: Uzabase tech blog (June 2026), Claude Code Skills official documentation (https://code.claude.com/docs/ja/skills), New Relic NRQL official documentation (https://docs.newrelic.com/jp/docs/nrql/nrql-syntax-clauses-functions/).

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• How to Catch Up with Claude Code — Official Docs and Best Practices
• Mastering Claude Code: 7 Ways to Steer Behavior with CLAUDE.md, Skills, Hooks, Subagents and More
• Building an Asset Portfolio Dashboard by Switching Between Claude Artifacts and Claude Code

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