Results for “crash-analysis”

Analyze core dumps, debug live processes, and investigate crashes, deadlocks, or memory issues in C/C++ programs using GDB with intelligent source code correlation.

在启动一个重要项目或关键行动之前，用于主动识别和防范风险

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Fetches and summarizes TestFlight crash reports, beta feedback, and performance diagnostics using the asc CLI.

Maps external dependencies and I/O boundaries, generates chaos tests for failure modes like timeouts and connection errors, runs them, and reports resilience gaps with fixes.

Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

Use this skill when the user asks about accident claims management — first-notice-of-loss procedures, working with insurance adjusters, claim severity classification, subrogation, claim closure timelines, and how to influence claim outcomes. Different from accident-investigation-playbook (which covers facts); this covers the insurance + claim side.

Analyze core dumps, debug live processes, and investigate crashes, deadlocks, and memory issues in C/C++ programs using GDB with source code correlation.

Challenge

Use this skill when a crash has been incorrectly classified as DOT-recordable when it shouldn't be. Covers the § 390.5 criteria, common misclassifications, and challenge process.

Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"

当分析个人或他人成就差异的根本原因，并寻求提升路径时

Runbook skill for failures: cron error, PA failure, cascade, gateway disconnect, semantic DB stale. Walks: detect → classify → diagnose → notify → log. Replaces ad-hoc failure handling. Triggers: "cron failed", "X is broken", "cascade", "incident", "gateway down", "PA failure".

Use when investigating a bug, runtime error, unexpected behavior, test failure, or crash. For systematic root cause analysis, error message interpretation, stack trace reading, reproduction steps, and targeted fixes. Activate when the user says "it's broken", "this doesn't work", "I'm getting an error", or "help me debug".

Responding to incidents: identifies impact scope, formulates recovery procedures, creates postmortems. Use when incident response or disaster recovery is needed. Delegates fixes to Builder.

Analyze conversion funnel drop-offs and identify biggest leak points with impact-ranked recommendations

Implements error tracking and crash reporting in Flutter apps using Sentry and Firebase Crashlytics, including automatic error capture, enriched context, breadcrumbs, and CI/CD integration for source maps and debug symbols.

Runs full Trailmark structural analysis by building a graph and computing pre-analysis passes for hotspots, taint, blast radius, privilege boundaries, and attack surface.

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates.

Analyzing pre-change impact across vertical (dependency chains, files) and horizontal (pattern consistency, naming) dimensions. Use to estimate blast radius before a refactor. No code.

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.

Investigates bugs, regressions, and crashes by dispatching four parallel read-only sub-agents, then ranks hypotheses and recommends the fastest proof or fix path.

Urgent issue classification, root cause analysis, and fast-path routing for production hotfixes

Turn churned and downgraded accounts into an early-warning system. Reads the customers who left or shrank, extracts the themes behind why, builds a warning checklist from the real causes, then reads your active book and flags which accounts look like the ones that just churned. Built for B2B retention teams, customizable to your CRM and your customer data. Trigger on "why do customers churn", "what do churned accounts have in common", "build me a churn early-warning list", "which accounts are at risk of churning", "who looks like a recent churner", or any retention post-mortem.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

在启动高风险项目或重要决策前，需要预先识别潜在失败点以增强心理韧性和方案鲁棒性时

Incident Post-Mortem Builder