'root cause' 검색 결과

Writes a single failing test that reproduces a bug after its root-cause analysis is complete, before any fix is written. TRIGGER when: a bug has a completed root-cause analysis and you need the failing reproduction test, writing a test that proves a bug exists, the second step of the bug-fixing pipeline. DO NOT TRIGGER when: still triaging or diagnosing the bug → opsmill-dev-analyzing-bugs; implementing the fix once the test exists → opsmill-dev-fixing-bugs; general feature test-first work → superpowers test-driven-development.

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Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.

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Production incident response automation. Reads logs, checks recent deploys, identifies root cause, suggests fixes, drafts incident comms, creates post-mortem templates. Severity classification (SEV1-4), escalation paths, status page updates. Generates incident-report.md with timeline, root cause, impact assessment, remediation steps, and prevention measures.

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Evidence-based investigation of issues, bugs, API calls, integrations, and other aspects of software development that need a deep dive to find the root cause and solutions. Use when you need to debug, troubleshoot, diagnose, or figure out why something is broken. Does not review code for quality or style — use code-review for auditing changes or post-code-review-to-pr for posting review feedback to GitHub. Does not assess architectural health or structural risk — use architectural-analysis for architectural concerns. Does not research open-ended options, prior art, or how something works when nothing is broken — use research for that. Does not plan the structural change a root cause calls for — use plan-a-change. Does not map bounded contexts or domain boundaries — use ddd-analysis. Does not capture feedback on Han's own skills — use han-feedback for that.

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Triage a raw, vague issue or bug report into a structured document that names what is known, what is missing, and what to do next. Use when an incoming issue, bug report, or problem description is too vague or incomplete for investigation or planning, and recommend the right next han skill. Does not investigate root causes or trace code paths — use investigate for debugging, diagnosis, and root cause analysis. Does not plan features or build solutions — use plan-a-feature or plan-implementation for that.

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Analyze vulnerable code to identify underlying root causes such as violated assumptions, incorrect invariants, missing validation checks, or unsafe component interactions. Use when investigating security vulnerabilities, CVEs, exploit code, or security audit findings. Infers why the vulnerability exists beyond surface symptoms, identifies systemic issues, and explains the chain of failures that enable exploitation.

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Apply when debugging. Trace each symptom to its root cause and fix it there; reproduce first, ask why until you reach it, resist nil-check guards that silence crashes.

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Expert in running effective incident postmortems. Covers blameless analysis, root cause investigation, action item prioritization, and building a learning culture. Understands that incidents are opportunities to improve systems, not punish people. Use when "postmortem, incident review, what went wrong, root cause, blameless, outage, post-incident, " mentioned.

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Root-cause debugging discipline. Use when a test, build or pipeline fails, behaviour does not match expectations, a bug task arrives, or a task comes back in need_revision — before proposing any fix.

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Uncover root causes through recursive Why questioning and consequence exploration. Use before acting on problems.

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Investigates a bug, failing test, build error, regression, flaky behavior, performance symptom, or unexpected result by reproducing it, minimizing the case, testing ranked hypotheses, tracing the first incorrect state, and locking the root cause with a regression check. Use before proposing a fix. Not for planning a new feature or reviewing a healthy diff.

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Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.

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Guides systematic root-cause debugging. Use when tests fail, builds break, something that worked yesterday broke, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need to figure out what broke and why — a systematic approach to finding and fixing the root cause rather than guessing.

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Root cause analysis for bugs and unexpected behavior. Traces errors through code, uses structured reasoning, and hands off to fix when cause is found. Core of the debug↔fix mesh. When the diagnosed cause is a memory leak in a long-running process, escalates to perf for cost-impact framing (leaks drive OOM-restart → cold-start → autoscaler spend, often 20-40% bill inflation).

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Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.

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Systematic root-cause isolation and minimal fix proposal for software bugs.

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AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.

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Systematic debugging with root cause investigation. Four phases: investigate, analyze, hypothesize, implement. Iron Law: no fixes without root cause.

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分布式训练梯度尖刺 (Gradient Spike) 根因定位。基于梯度监控数据 (trend.db/CSV)、 msprobe dump 统计数据，三阶段渐进式从异常坐标定位逐层深入到算子级根因。 当用户明确指明分析梯度尖刺问题时使用此 skill。 即便用户提供以上数据，但未指明分析梯度尖刺问题时，不可使用此skill。

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Investigate bugs with unknown root cause. Spawns fresh-context investigation subagent + parallel /review solve companion, persists hypotheses across iterations, hands off to /fix when root cause found.

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Conduct structured root cause analysis using Ishikawa (Fishbone), 5 Whys, and Pareto methods. Suitable for production incidents, quality defects, customer complaints, and recurring operational problems. Use when problems recur or when initial fixes do not resolve the issue. Do not use for one-time, obvious fixes or strategic decision-making.

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Analyze a set of related tickets to identify the underlying root cause.

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Use for the 5 Whys, or when the user wants the root cause of a failure: "why did this happen?", "this keeps breaking", "write the postmortem", "find the real cause". Asks why repeatedly with evidence at each step, then sets countermeasures. Skip for one-off typos or when the cause is already proven.

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Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

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