'failure' 검색 결과

Classify failures in CI, automated checks run during development, to distinguish code regressions, temporary infrastructure problems, security test failures, and inconsistent results. Record each failure's category, suggested next action, and attempt count in a structured report.

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• Sort failed CI checks by likely cause
• Identify small regressions that may qualify for automatic fixes
• Refer infrastructure and security failures to a human

Monitor a GitHub pull request for review feedback, automated check failures, and merge readiness. Diagnose failures, fix problems caused by the branch, and retry likely temporary failures while continuing to watch for updates.

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• Monitor automated checks after opening a pull request
• Address actionable review feedback
• Distinguish code failures from temporary service problems

Turn bug reports into small, runnable tests or scripts with clear failure checks. Verify that they expose the reported problem, then reduce inputs and setup while preserving the same failure.

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• Turn a bug report into a runnable test
• Reduce a complex test to its essential trigger
• Record failure rates for intermittent bugs

Identify agent failures that happen before any work starts by checking iteration counts, tool use, artifacts, and logs. Classify these incidents as pre-execution failures and guide investigation toward environment setup, initialization, and resource limits.

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• Investigating agent sessions with zero completed iterations
• Classifying failures before the first tool call
• Routing incidents to infrastructure or platform teams

[omh] Hidden or swallowed errors suspected: find swallowed errors, unsafe fallbacks, hidden UI/runtime failures, and missing propagation before they become false green status. Use when the user says: failure-signal-audit, failure signal audit, silent failure, silent failures, silent failure hunter, swallowed error, swallowed errors, empty catch.

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[omh] Build or CI failure to triage: classify build, typecheck, lint, test, CI, and DCO failures into minimal safe fix handoffs. Use when the user says: build-failure-triage, build failure triage, build failure, build-failure, build fix, build failed, build failing, compile error.

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Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.

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• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build first

Investigate recurring SGLang automated test failures by comparing logs, commit history, and GPU environments. Narrow down code changes or machine-specific problems, optionally reproduce failures remotely, and summarize the evidence in a structured report.

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• Finding the change behind a recurring test failure
• Checking whether failures depend on a GPU or runner
• Reproducing a test failure on a remote GPU server

Add error handling to n8n workflows with connected error outputs, retries, and failure notifications. Return HTTP status codes that explain what went wrong, distinguish invalid requests from service failures, and keep internal error details out of public responses.

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• Investigating workflows that fail without notification
• Returning clear errors from webhook APIs
• Retrying temporary network failures

Diagnose unreachable or unhealthy OpenShell gateways and sandbox startup failures. Identify the underlying execution platform, then examine logs, authentication, certificates, and configuration with the matching tools to narrow down the cause.

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• Investigate gateway connection and health check failures
• Find why a sandbox remains pending or fails to start
• Check Docker or Podman runtime and image problems

Design controlled failure experiments for distributed systems to examine how services recover. Produce failure injection scripts, monitoring plans, rollback procedures, and exercise guides, then turn findings into documented improvements.

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• Planning controlled failure experiments
• Testing recovery after Kubernetes Pod failures
• Checking service behavior under network delays

Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.

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Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.

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ソフトウェアにおける非正常状態（Error, Defect, Fault, Failure）の分類と定義を提供する。 JIS規格・JSTQB・Bertrand Meyer等の複数の標準に基づき、各概念の違いを明確化し、 適切なエラー設計・障害対策の判断を支援する。エラー設計、障害分析、コードレビュー、 テスト設計時に用語の混乱を防ぎ、正確な分類に基づく対処戦略の選択に使用。 対象言語: 言語非依存。 トリガー：「ErrorとFaultの違い」「DefectとBugの関係」「障害と故障の区別」 「エラーの分類」「Failureとは何か」「非正常状態の設計」「エラー用語の定義」 「エラーと欠陥の違い」「faultとfailureの違い」 といったエラー分類・用語定義関連リクエストで起動。

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Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).

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Assess an application against OWASP Top 10:2025 risk categories using Strix. Get findings backed by working exploit examples, organized by category, with clear explanations of what was tested and what remains unassessed.

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• Assessing an app against OWASP Top 10:2025
• Testing access controls across users and tenants
• Organizing proven vulnerabilities by risk category

Validates skill files and registration consistency, then checks project tests and system health. Compares failures with recent changes to distinguish regressions, outdated tests, intermittent failures, and infrastructure issues, producing a report that tracks results over time.

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• Validating definitions and registration for new skills
• Running daily tests and system health checks
• Classifying failures after code changes

Explore an existing Vitest 3.2 JSON test report to count results, search failed tests, and inspect file errors. Review original diagnostic details and keep tentative explanations separate from evidence, without launching tests.

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• Check failure counts in an existing test report
• Search failures by file path or diagnostic text
• Inspect file errors separately from failed assertions

Classify outstanding pull request CI failures, automated checks run on code changes, using logs and changed files. Produce JSON decisions recommending a rerun, a comment, or no action, and identify specific nondeterministic tests when evidence supports it.

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• Sorting outstanding CI failures on pull requests
• Identifying transient failures suitable for reruns
• Checking whether PR changes caused a failure

Analyze failures in CI, the automated checks run during development. Identify the failing job and step, classify the likely cause, and suggest a small fix, continued monitoring, or human review.

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• Identifying failed jobs and steps in CI
• Distinguishing intermittent failures from code regressions
• Routing environment failures to a human

Build failure handling into n8n workflows with separate error paths, retries, alerts, and clear API responses. Use it for webhooks and unattended jobs to make failures visible and return status codes that explain what went wrong.

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• Add error responses to a webhook API
• Receive alerts when scheduled jobs fail
• Retry temporary external service failures

[omh] Failing run handed to Jev for a next move: Jev failure triage: retry, fix a dependency, ask for access, or change approach on a failing run. Use when the user says: jev-failure-triage, jev failure triage, ask jev if this failure is transient.

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Debugs failing GitHub Actions CI runs for PostHog PRs, commits, and branches, and answers broad CI-health questions ("is CI red?", "is master green today?", "what's broken right now?"). Use when the user asks why CI is red, asks for the current CI or master status, or mentions a failing check, GitHub Actions run, Depot runner, workflow, job, shard, merge queue kick, flaky test, lint failure, typecheck failure, snapshot diff, migration check, generated types drift, or skills build failure. Start with the `hogli ci:insights` digest (cross-run CI history from engineering analytics), then guides read-only inspection, failure classification, smallest local reproduction with hogli, and safe reporting without rerunning CI or posting to GitHub. Running unattended as the "Master-red diagnosis" workflow: see references/master-red-incident.md.

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General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.

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