Systematically trace bugs backward through call stack to find original trigger
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概要と使いどころ
Systematically trace bugs backward through call stack to find original trigger
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 at every layer data passes through to make bugs impossible
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。
Expert en QA exploratoire v4 (tests systématiques, chasse aux bugs, preuves, rapports, régression)
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。
Validates custom dotnet new templates for correctness before publishing. Catches missing fields, parameter bugs, shortName conflicts, constraint issues, and common authoring mistakes that cause templates to fail silently. USE FOR: checking template.json files for errors before publishing or testing, diagnosing why a template doesn't appear after installation, reviewing template parameter definitions for type mismatches and missing defaults, finding shortName conflicts with dotnet CLI commands, validating post-action and constraint configuration. DO NOT USE FOR: finding or using existing templates (use template-discovery), creating projects from templates (use template-instantiation), creating templates from existing projects (use template-authoring).
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。
When isolating and fixing unpredictable or complex software bugs.
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, fix, or validate SwiftUI animation and scroll behavior by temporarily instrumenting views with MotionEyes, capturing console traces over time, and comparing motion data to user intent or test expectations. Use when users report bugs such as wrong direction, premature fade, timing or easing mismatch, unexpected movement, missing movement, incorrect relative movement between views, scroll jumps, scroll restoration drift, content-offset desynchronization, or when they want to turn MotionEyes traces into focused assertions. Prefer XcodeBuildMCP log capture first and fallback to CLI log streaming; remove only agent-added MotionEyes instrumentation after validation.
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Parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, flaky behavior, or unexplained failures. Use when the user asks to investigate a bug, find the root cause, trace a regression, understand why something broke, or wants a ranked diagnosis with the fastest proof path without making code edits.
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Use when turning something that happened into something that makes the next run better - "write a retro", "record this paper cut", "this bug escaped verification", "make this an eval", "why do we keep hitting this", "did that change actually help", "scorecard", "what did we learn from that run", "lessons keep getting re-learned". Covers the six-step loop (capture, classify, convert, change, measure, retro), the repo artifact each step produces, and how escaped bugs become eval cases. Do NOT use to design or judge a single workflow experiment (see `workflow-experimentation`, which this loop feeds), to decide where to run work (see `dogfooding`), or to debug one failed execution in the moment.
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。
Systematically finds and fixes bugs using proven debugging techniques. Traces from symptoms to root cause, implements fixes, and prevents regression.
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Uses Microsoft RESTler to perform stateful REST API fuzzing by automatically generating and executing test sequences that exercise API endpoints, discover producer-consumer dependencies between requests, and Bul: security and reliability bugs. The tester compiles an OpenAPI specification into a RESTler fuzzing grammar, configures authentication, runs test/fuzz-lean/fuzz modes, and analyzes results for 500 errors, authentication bypasses, resource leaks, and payload injection vulnerabilities. ...
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Provides binary exploitation techniques for CTF challenges. Use when you already have a vulnerable native target or service and need to turn memory corruption or low-level primitives into code execution or privilege escalation, such as buffer overflows, format strings, heap bugs,
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4-phase root cause debugging: understand bugs before fixing.
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。
Exploratory QA of web apps: find bugs, evidence, reports.
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-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
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Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.
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-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
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Consolidate PostHog error tracking issues that are the same actual error reported under different fingerprints. Use when the user asks "why do I have so many TypeError issues that look the same?", "merge these duplicates", "stop splitting this error into new issues", or wants to clean up fingerprint sprawl. Decides between a one-shot merge of existing issues and a durable grouping rule that keeps future events from creating new fingerprints. Does NOT group conceptually similar bugs across different runtimes, SDKs, or call sites.
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Debugs why session recordings aren't appearing in the local dev environment. Use when a developer reports that local replay ingestion isn't working, recordings aren't showing up despite /s calls, or the replay pipeline seems broken after hogli start. Covers the full local pipeline: SDK capture, Caddy proxy, capture-replay (Rust), Kafka, ingestion-sessionreplay (Node), recording-api (Node), SeaweedFS, and common failure modes like orphaned processes, stuck phrocs workers, and trigger misconfiguration.
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Solve a hard problem by trying multiple approaches in parallel using isolated git worktrees. Each attempt runs in its own branch, and the best solution is selected. Use for complex refactors, tricky bugs, or architectural decisions where multiple strategies could work.
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Creates, debugs, and optimizes GitHub Actions workflow YAML files. Recommends current action versions with SHA pinning from a daily-updated index of 120+ actions. Configures matrix builds, dependency caching, reusable workflows, OIDC authentication, secrets management, and runner selection. Reviews existing workflows for outdated actions, security issues, and performance problems. Use when the user asks about GitHub Actions, workflows, CI/CD, .github/workflows, any action by name (actions/checkout, setup-node, setup-python, etc.), or workflow optimization.
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Use when you need to add or configure Maven plugins in your pom.xml — including quality tools (enforcer, surefire, failsafe, jacoco, pitest, spotbugs, pmd), security scanning (OWASP), code formatting (Spotless), version management, container image build (Jib), build information tracking, and benchmarking (JMH) — through a consultative, modular step-by-step approach that only adds what you actually need. This should trigger for requests such as Add Maven plugins in pom.xml; Improve Maven plugins in pom.xml. Part of cursor-rules-java project
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Canonical UI/UX best practices ruleset with 14 sections and 70+ rules prioritized P0–P3. Use when implementing, reviewing, or auditing UI components. P0 rules are mandatory — violations are bugs. All rules reference tokens from ui-theme-reference.
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Trace bugs backward through call stacks and execution flows to identify the original source of errors, invalid data, or incorrect behavior. Use when debugging complex issues, tracing error origins, investigating data corruption, following execution paths, identifying where invalid data enters the system, or finding root causes of cascading failures.
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Implement test-driven development (TDD) workflow using the red-green-refactor cycle. Use when writing new features, fixing bugs, or refactoring existing code. Always write the failing test first, then implement minimal code to pass, then refactor. Essential for ensuring code reliability, preventing regressions, improving design through testability requirements, documenting expected behavior through tests, enabling confident refactoring, and maintaining high code quality standards throughout the development process.
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。