「failure analysis」の検索結果

Audit transit damage prediction and prevention systems for packaging failure mode analysis, handling chain risk assessment, claims pattern detection, and protection level optimization..

日本語の概要は準備中です。原文の説明を表示しています。

Specialized GitHub PR intelligence agent for automatically gathering comprehensive context from Pull Requests. Activate when users need CI failure analysis, review comment investigation, or PR status assessment. Triggers on requests like "CI 실패 원인 찾아줘", "gather review comments", "check PR status", "analyze PR".

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose Azure application and infrastructure issues with a structured, evidence-based workflow. Use this skill when users ask to troubleshoot outages, latency, failures, configuration drift, or anomalies. Root cause analysis using logs, metrics, traces, and dependency mapping.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose agent operational failures through default-policy checks, multi-gap analysis, explicit service ownership, request-context lifetime, and degradation-versus-correctness checks.

日本語の概要は準備中です。原文の説明を表示しています。

Systematic debugging with root cause investigation. Produces a diagnostic report; mk:fix owns remediation and mk:build-fix owns compile/build failures. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively suggest when the user reports errors, unexpected behavior, or is troubleshooting why something stopped working. NOT for applying fixes without investigation (see mk:fix); NOT for step-by-step evidence-based reasoning (see mk:sequential-thinking).

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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

日本語の概要は準備中です。原文の説明を表示しています。

Draft a Root Cause Analysis for an incident, outage, or repeated test failure. Unlike intake/spec/brd, RCA is not a workflow phase — it's a standalone postmortem artifact that often precedes a bugfix intake. Output lives at `docs/rca/<slug>.md`.

日本語の概要は準備中です。原文の説明を表示しています。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

日本語の概要は準備中です。原文の説明を表示しています。

Use when introducing a feature flag system, choosing between release flags / experiment flags / operational flags / kill switches / permission flags, designing percentage rollouts, building kill-switch + circuit-breaker patterns, structuring evaluation contexts (user IDs, plan tier, region), or building flag-cleanup discipline. Triggers: LaunchDarkly / GrowthBook / Unleash / Flagsmith / OpenFeature, percentage ramp, sticky bucketing, kill switch wired to alerting, "we still have flags from 2 years ago", flag debt, OpenFeature provider, evaluation context, default value on provider failure. NOT for A/B test statistical analysis (separate skill), traffic-split routing at the edge, build-time bundler defines, or environment-based config.

日本語の概要は準備中です。原文の説明を表示しています。

Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

日本語の概要は準備中です。原文の説明を表示しています。

Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

日本語の概要は準備中です。原文の説明を表示しています。

Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.

日本語の概要は準備中です。原文の説明を表示しています。

Error handling review — silent failure detection, catch block analysis, error logging.

日本語の概要は準備中です。原文の説明を表示しています。

Error handling review — silent failure detection, catch block analysis, error logging.

日本語の概要は準備中です。原文の説明を表示しています。

Specialized GitHub PR intelligence agent for automatically gathering comprehensive context from Pull Requests. Activate when users need CI failure analysis, review comment investigation, or PR status assessment. Triggers on requests like "CI 실패 원인 찾아줘", "gather review comments", "check PR status", "analyze PR".

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user reports a hard bug, unexpected failure, or performance regression that needs root-cause analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

日本語の概要は準備中です。原文の説明を表示しています。

Knowledge base from NASA-STD-8729.1A — NASA's R&M (Reliability and Maintainability) standard covering spaceflight and support systems (Revision A, 2017-06-13). Use for NASA's objectives-driven R&M framework — the R&M objectives hierarchy (one Top Objective decomposed into four sub-objectives, paired with tailorable strategies), how R&M requirements are established inside the SMA Plan required by NPR 7120.5, the SMA Technical Authority concurrence/independent-evaluation governance, milestone vs. readiness review gates, the reliability/maintainability/availability vocabulary (Ai vs. Ao, the failure causal chain, risk as a triplet), and the R&M Evidentiary Methods catalogue (FMEA/FMECA, FTA, RBDA, RCM, LORA, plus the space-environment and parts-pedigree analyses). Scoped to NASA spaceflight and support systems and to assurance planning: it is an objectives-and-strategies standard, so it deliberately does NOT prescribe step-by-step analysis procedures (those live in the referenced NASA Preferred Reliability Practices) and is thin on facility R&M, detailed math/derivations, and non-NASA or commercial life cycles.

日本語の概要は準備中です。原文の説明を表示しています。

Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Dbt — dbt (data build tool) project management and monitoring. Covers model runs, test results, source freshness, documentation generation, manifest analysis, and dbt Cloud API integration. Use when checking dbt run status, investigating test failures, analyzing model lineage, or managing dbt Cloud jobs.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Cloudformation Deep — aWS CloudFormation deep stack management. Covers stack operations, change sets, drift detection, nested stacks, stack sets, resource imports, template analysis, and rollback troubleshooting. Use when managing complex CloudFormation deployments, debugging stack failures, detecting drift, operating stack sets, or analyzing template dependencies.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Circleci — circleCI pipeline and workflow management. Covers pipeline status, workflow analysis, job logs, credit usage monitoring, orb management, and project configuration. Use when checking CI status, investigating build failures, analyzing credit consumption, or managing CircleCI orbs.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Bitbucket Deep — bitbucket deep platform management covering repository inventory, Pipelines CI/CD monitoring, pull request analysis, branch permission auditing, deployment environment tracking, and workspace member management. Use when auditing repository configurations, investigating pipeline failures, reviewing branch restrictions, or managing workspace access.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Bazel — bazel build system management. Covers BUILD file analysis, target querying, dependency graphs, remote execution, caching configuration, and build performance profiling. Use when managing Bazel workspaces, debugging build failures, querying targets, or optimizing build performance.

日本語の概要は準備中です。原文の説明を表示しています。