「paths」の検索結果

Debug 404 errors for API endpoints in microservice architectures where code exists but routing sends requests to wrong service. Use when: (1) API endpoint returns 404 despite code being deployed, (2) Multiple services share same hostname via HTTPRoute/Ingress, (3) Service logs show "Health server" but you expected API endpoints, (4) Monorepo deploys multiple images and endpoint might be in different service than expected. Covers: verifying which service handles which paths, HTTPRoute path precedence, testing endpoints directly from inside cluster, matching image tags across services.

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Fix silent video/media processing failures caused by URL extraction code that filters on file extensions (.mp4, .webm, .webp). Use when: (1) Media moderation, transcoding, or analysis silently skips files from Blossom or content-addressed storage servers, (2) URL extraction from Nostr event tags (imeta, r tags) drops URLs without recognized extensions, (3) CDN fallback URLs append .mp4 but the actual server uses extensionless content-addressed paths like /{sha256}. Common in Nostr video events (kind 34236) where different clients use different URL formats.

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Fix .well-known files (apple-app-site-association, assetlinks.json) being served as HTML by the @fastly/compute-js-static-publish SPA fallback instead of JSON. Use when: (1) iOS Universal Links or Android App Links are broken because the verification files return text/html instead of application/json, (2) PublisherServer with spaFile config intercepts /.well-known/ paths and returns index.html (200, text/html) instead of 404 for missing files, (3) apple-app-site-association (no file extension) gets wrong content type even when correctly stored in KV. Covers both apex domain and subdomain handler patterns.

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Fix ClickHouse "Code: 243 Cannot reserve 1.00 MiB, not enough space" errors caused by system log tables (text_log, trace_log, processors_profile_log, query_log) filling the disk. Use when: (1) ClickHouse inserts fail with NOT_ENOUGH_SPACE error, (2) Disk is 100% full but application tables are small, (3) system database is 10-100x larger than user databases, (4) Sentry shows batch insert/commit failures across multiple tables simultaneously. Covers both self-hosted ClickHouse (Altinity operator on K8s) and ClickHouse Cloud with different remediation paths for each.

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Create narrow iMessage/PTT Border-Light HyperCard style nine-grid cards in Chinese or mixed Chinese-English. Use when the user says "iMessage 九宮 HyperCard", "iMessage 九宮", "做成 iMessage 九宮", "用 iMessage 九宮 HyperCard 整理", asks for a phone-readable short card with Context below, or wants Mandala/九宮/HyperCard content formatted for blue-bubble/iMessage sharing with long links, paths, and rules moved out of the card.

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Analyze Chinese notes, articles, manuscripts, and digital card-box material with FIRE 2.0: Full-D numbering, Index keyword webs, Route thinking paths, and Evolution over time. Use when Codex needs to prepare material for semantic search, turn temporary notes into permanent or project notes, build table-of-contents/index/search structures, create compact Chinese FIRE cards, or maintain this skill's original discussion thread provenance.

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Automatically generates comprehensive unit tests for functions, classes, and modules. Use when you need to create tests for Python (pytest, unittest) or Java (JUnit, TestNG) code. Generates tests with comprehensive coverage including happy paths, edge cases, and error conditions. Analyzes existing test patterns in the codebase to match style and conventions. Supports mocking, parameterized tests, fixtures, and follows best practices for each framework.

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Instruments code to track the flow of untrusted or sensitive data at runtime, enabling detection of injection vulnerabilities, data leaks, and privilege violations. Use when users need to: (1) Track untrusted input propagation through code, (2) Detect SQL injection, XSS, or command injection vulnerabilities, (3) Identify sensitive data leaks, (4) Monitor privilege escalation paths, (5) Perform dynamic taint analysis for security testing. Supports Python, Java, JavaScript, and C/C++ with configurable taint sources and sinks.

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Generates comprehensive test scenarios from requirements including BDD/Gherkin scenarios, unit tests, integration tests, and end-to-end test cases. Use when converting requirements, user stories, or specifications into testable scenarios with full coverage including happy paths, error cases, edge cases, and boundary conditions. Outputs structured test suites ready for implementation.

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Instruments authentication, authorization, and input-handling code paths to monitor security-relevant events and states at runtime. Use this skill when developers need to add security monitoring and logging to their code, including tracking authentication attempts (login/logout), authorization decisions (access control checks), input validation failures, session management events, and other security-critical operations. Supports Python, JavaScript/TypeScript, and Java with structured logging patterns. Triggers when users ask to add security instrumentation, monitor security events, log authentication/authorization, track security-sensitive operations, or add security observability to their codebase.

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Selectively instruments code to capture runtime data for debugging failures and bugs. Use when investigating crashes, exceptions, unexpected behavior, test failures, or performance issues. Analyzes stack traces and error messages to identify suspicious code regions, then adds targeted logging, tracing, and assertions to capture variable values, execution paths, timing, and conditional branches. Supports Python, JavaScript/TypeScript, Java, and C/C++.

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Automatically generate regression tests for Java codebases by analyzing changes between old and new code versions. Use when users need to: (1) Generate tests after refactoring or code changes, (2) Ensure previously tested behavior still works in new versions, (3) Cover modified or newly added code paths, (4) Migrate existing tests to work with updated APIs or signatures, (5) Maintain test coverage during code evolution. Supports JUnit and TestNG frameworks with unit tests, parameterized tests, and exception testing patterns.

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Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).

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Analyze existing test suites and source code to suggest additional unit tests that improve test coverage. Use this skill when working with test files and source code to identify untested code paths, missing edge cases, uncovered branches, untested error conditions, and gaps in test coverage. Supports major testing frameworks (pytest, Jest, JUnit, Go testing, etc.) and generates targeted test suggestions based on coverage analysis.

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Automatically migrates build systems and CI/CD configurations to target platforms. Use when modernizing build infrastructure, switching CI/CD providers, or standardizing across projects. Supports common migration paths including Maven↔Gradle, npm↔Yarn, Travis CI→GitHub Actions, CircleCI→GitHub Actions, Jenkins→GitLab CI, and GitLab CI→GitHub Actions. Analyzes existing configuration, generates equivalent target configuration, maps dependencies and commands, and provides validation and migration documentation.

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Add, inspect, repair, or validate CodeDrobe Core adapters for Chromium/Electron AI desktop applications. Use when supporting a new app, updating Codex or WorkBuddy compatibility after an app release, discovering installation paths or CDP targets, selecting stable DOM landmarks, adding renderer profiles or transactional host settings, recording lastVerified versions, or writing adapter and real-renderer tests.

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Interview employees to identify recurring daily work, office workflows, and repetitive tasks that can be automated, co-piloted, or supported by AI. Use when the user wants to find what AI can take off their plate, map available tools such as ChatGPT/OpenAI, Claude, Microsoft Copilot, Excel or PowerPoint add-ins, Google Workspace, no-code/RPA tools, coding agents, or internal automation platforms, and write tool-aware specs with fallback paths.

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Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current paradigms, a complete roadmap or curriculum, deliberate practice and feedback, or a top-1%-level mastery plan. Answer explanation questions completely before offering a curriculum; route explicit mastery goals to an adaptive practice and proof-of-capability system.

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Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.

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The order of operations for an Active Directory penetration test: setup, collection, exploitation, post-processing. Use this whenever you are planning or driving an AD assessment and need to know what to run before what and why (map before you exploit; harvest easy credentials before spraying to avoid lockouts; collect the graph before you reason about paths). Covers phase sequencing with standard tooling: netexec/nxc, impacket, certipy, bloodyAD, kerbrute, BloodHound CE. Invoke it at the start of an engagement, when deciding the next phase, or when a step feels out of order.

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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.

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Think and work like an expert HVAC Engineer. Use when a task calls for HVAC Engineer judgment. Reasons from psychrometric state, parallel heating and cooling load paths, and vapor-compression thermodynamics through TRACE/HAP and EnergyPlus load models, ASHRAE 62.1/55/90.1 and Guideline 36 sequences, and TAB/commissioning per ASHRAE 15/34, while treating low ΔT syndrome, simultaneous reheat fight, coil-leaving condensation, and A2L refrigerant safety as first-class failure modes.

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Think and work like an expert Electric Machines Engineer. Use when a task calls for Electric Machines Engineer judgment. Reasons from magnetic circuit design, dq-frame machine models, FEM flux paths, and drive efficiency maps while treating saturation, cogging, thermal derating, and inverter harmonics as first-class failure modes.

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Think and work like an expert Edge / Embedded AI Engineer. Use when a task calls for Edge / Embedded AI Engineer judgment. Reasons from tensor-arena budgets, full-int8 PTQ with representative calibration, and TFLM/CMSIS-NN or Vela/Ethos-U compile paths through ONNX Runtime QNN HTP and mobile delegates—treating train–serve preprocessing skew, float thresholds on quantized outputs, and NPU operator fallback 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。