「failure analysis」の検索結果

Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.

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。

Fix git pre-commit hook failures in Flutter projects when using git worktrees. Use when: (1) Pre-commit hook with `flutter analyze` fails during `git commit` but passes when run manually, (2) Hook output shows "Resolving dependencies..." then immediately fails with no actual analysis errors, (3) Working in a git worktree (created via `git worktree add`). Root cause: git sets GIT_DIR to a worktree-style path during hook execution, which breaks `flutter pub get` (called internally by `flutter analyze`). Fix: unset GIT_DIR, GIT_INDEX_FILE, and GIT_WORK_TREE before flutter/dart commands in the hook.

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。

Automatically generate clear, actionable issue reports from failing tests and repository analysis. Analyze test failures to understand expected vs. actual behavior, identify affected code components, and produce well-structured Markdown reports suitable for GitHub Issues or similar trackers. Use when a test fails, when debugging issues, or when the user asks to create an issue report, generate a bug report, or document a test failure.

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。

Think and work like an expert Metallurgist. Use when a task calls for Metallurgist judgment. Reasons from phase diagrams, TTT/CCT paths, and Scheil solidification through Jominy hardenability (ASTM A255), ASM heat-treat cycles, metallography (ASTM E3/E112/E407), and staged failure analysis while treating decarburization, quench cracking, HAZ liquation, hot tearing, and microsegregation as first-class failure modes.

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。

Think and work like an expert Hydrogeologist. Use when a task calls for Hydrogeologist judgment. Reasons from Darcy's law, mass conservation, aquifer heterogeneity, and coupled biogeochemical transport through Theis and Cooper-Jacob aquifer-test analysis, MODFLOW/MT3DMS modeling with PEST uncertainty, and low-flow geochemical sampling while treating wellbore-storage and skin artifacts, equivalent-porous-medium failure in fractured/karst flow, and non-unique K-S calibration as first-class failure modes.

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。

Think and work like an expert Computer Scientist. Use when a task calls for Computer Scientist judgment. Reasons from computational models, abstraction contracts, invariants, and measurable complexity through CLRS-grade algorithm analysis, impossibility results (FLP, CAP, NP-hardness), property-based and chaos testing, and formal tools (TLA+, Coq, Z3) while treating partial failure, race conditions, label leakage, and abstraction leaks like GC pauses and clock skew as first-class failure modes.

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。

Think and work like an expert Aerospace Engineer. Use when a task calls for Aerospace Engineer judgment. Reasons from margin-managed factors of safety, mass properties, and the analysis-test-similarity V&V hierarchy through FUN3D/SU2 CFD, NASTRAN/Abaqus FEA, JSBSim/Simulink 6-DOF, and FAR/CS, DO-178C, and NASA-STD compliance while treating flutter, buckling, inlet distortion, mass growth, and single-point failures as first-class failure modes.

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。

Produce a risk matrix or heatmap that quantifies what could break by business impact × probability, runs failure mode analysis on the top items, and maps test coverage to risk zones. Includes stakeholder interview frameworks and continuous reassessment. Run this BEFORE test-strategy or test-planning. Use when: "risk assessment," "risk matrix," "risk heatmap," "what could break," "critical paths," "failure modes," "where to focus testing." Not for: multi-quarter QA direction — use test-strategy. Not for: a single sprint/release test plan — use test-planning. Not for: hands-on session-based bug hunting — use exploratory-testing. Related: test-strategy, test-planning, release-readiness, qa-metrics.

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。

Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.

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 Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority 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。

Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.

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。

Test execution reference: framework detection (pytest/jest/vitest/go/cargo/junit) and correct run/coverage commands, plus failure-analysis steps. Use when running tests, analyzing test failures, or verifying coverage after code changes.

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。

Diagnoses ArgoCD application sync failures using the ArgoCD REST API and Kubernetes resource diff analysis. Identifies Helm value conflicts, Kustomize overlay errors, and resource health check 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。

"Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Use this skill when the user needs to assess product or process risks, prioritize corrective actions, or build a risk register — even if they say 'failure mode analysis', 'risk assessment', 'what could go wrong', or 'RPN calculation'.".

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。

Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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。

Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).

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。

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

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。

Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.

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。

Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

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 for deep Symfony project analysis: kernel/bootstrap, container wiring, routing/request flow, Doctrine, security, Messenger, and Symfony-specific failure patterns.

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 for deep Laravel project analysis: boot flow, request lifecycle, container usage, Eloquent/data flow, async systems, and Laravel-specific failure patterns.

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 for deep React analysis: component tree, state flow, props flow, hooks usage, rendering behavior, and React-specific failure patterns.

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 for deep Node.js / Express project analysis: boot flow, middleware order, async behavior, data layer, auth/security, and Node-specific runtime failure patterns.

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。

Detect RDP brute force attacks by analyzing Windows Security Event Logs for failed authentication patterns (Event ID 4625), successful logons after failures (Event ID 4624), NLA failures, and source IP frequency 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。