3,876 件 ・ 関連度順
概要と使いどころ
Builds 5-7 detailed audience personas from a URL with demographics, psychographics, pain points, buying triggers, platform-specific targeting parameters, persona scoring, and negative audiences
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。
Work with the QStash JavaScript/TypeScript SDK for serverless messaging, scheduling. Use when publishing messages to HTTP endpoints, creating schedules, managing queues, verifying incoming messages in serverless environments.
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。
Athenahealth integration. Manage Patients, Appointments, Providers, Practices, Departments, Claims. Use when the user wants to interact with Athenahealth data.
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 rsky-pds createRecord/putRecord hanging indefinitely with no error logs. Use when: (1) createSession succeeds but createRecord never returns, (2) No error appears in PDS logs despite request hanging for 60+ seconds, (3) PDS health endpoint works but write operations hang, (4) readiness probes intermittently fail on rsky-pds pod. Root cause: PDS_CRAWLERS env var configures crawler notification URLs that are called via reqwest HTTP client WITHOUT any timeout in crawlers.rs. If the crawler URL is unreachable from the cluster, every write hangs forever. Also: setting PDS_CRAWLERS="" produces [""] (one empty string element) which causes a "builder error" instead — must fully unset the env var.
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 JSON parse errors when fetching archived API responses from Wayback Machine. Use when: (1) Getting "Expecting value: line 1 column 1 (char 0)" JSON decode errors from archived URLs, (2) Wayback returns HTML instead of expected JSON/raw content, (3) Crawling archived REST APIs or JSON endpoints from web.archive.org. The `id_` modifier returns raw content without the Wayback toolbar wrapper.
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 S3 "InvalidArgument" errors when using Google Cloud Storage S3-compatible API with ObjectCannedAcl::PublicRead or x-amz-acl headers. Use when: (1) put_object or upload_blob fails with InvalidArgument on GCS, (2) Using aws-sdk-s3 Rust crate with GCS endpoint (storage.googleapis.com), (3) Bucket has uniform bucket-level access enabled (GCS default since 2023), (4) S3 operations work on MinIO/AWS but fail on GCS. GCS rejects per-object ACLs when uniform bucket-level access is enabled.
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。
Debug "API returns data that doesn't exist in database" when multiple Kubernetes clusters exist (production, staging, POC). Use when: (1) REST API returns records that direct database queries can't find, (2) Data counts match approximately but specific records don't overlap, (3) kubectl context points to a different cluster than the one serving the public domain, (4) ClickHouse/Postgres queries return stale or different data than the API, (5) "completely different datasets" despite same table names and schemas. Root cause: querying the wrong cluster's database.
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 wildcard subdomain routing for Fastly Compute@Edge services when subdomains resolve but return 500 "Domain Not Found" or empty responses. Use when: (1) TLS subscription "issued" but edge serves default certificate (CN=j.sni-644-default), (2) Fastly domain-v1 create succeeds but subdomain returns 500, (3) Static publisher/PublisherServer returns empty content for subdomain requests, (4) DNS wildcard CNAME resolves to wrong target. Covers TLS configuration SNI endpoint selection (x.sni vs j.sni vs w.sni), DNS trailing dot issues, Fastly domain-v1 activation, and reading from KV store directly when PublisherServer fails for subdomains.
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。
Debug Fastly Compute deployments that appear successful but return stale/wrong responses. Use when: (1) fastly compute publish succeeds but version check shows old version, (2) new endpoints return 404 after deployment, (3) cache-busted requests work but regular requests fail, (4) fastly domain list doesn't show your custom domain. Covers edge propagation timing, cached error responses, and domain management API differences.
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。
AutoMCM-Pro industrial-grade math modeling agent (opencode binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.
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。
AutoMCM-Pro industrial-grade math modeling agent (Codex CLI binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.
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。
AutoMCM-Pro industrial-grade math modeling agent. Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.
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。
AutoMCM-Pro industrial-grade math modeling agent (DeepSeek Harness binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.
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。
Create Fantastical calendar events and reminders from Codex on macOS. Use when the user asks Codex to add, schedule, parse, preview, or send a meeting, appointment, task, reminder, time block, daily plan item, 行事曆, 日曆, 會議, 待辦, or 提醒 into Fantastical.
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。
Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.
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。
Generate boilerplate code and project templates/skeletons automatically. Use when: (1) Creating new projects from scratch (React app, FastAPI backend, Express API), (2) Generating repetitive code patterns (CRUD endpoints, models, controllers), (3) Scaffolding components, services, or modules, (4) Creating test boilerplate, (5) Setting up monorepo structures. Provides project templates and code generation patterns for common development tasks.
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。
Explains runtime errors and compilation failures with actionable debugging guidance. Use when Python or Java code throws runtime exceptions (NullPointerException, TypeError, AttributeError, etc.), compilation errors (syntax errors, type mismatches, import failures), or dependency issues. Analyzes error messages, stack traces, and code context to identify root causes and provide concrete fixes with examples. Distinct from test-related errors - focuses on errors during normal code execution and build processes.
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。
Compares old and new requirement documents, analyzes code repository impact, and generates detailed modification plans. Use when Claude needs to: (1) Compare requirement versions and identify changes, (2) Map requirement changes to code components, (3) Identify components to modify, delete, or add, (4) Analyze dependencies and integration points, (5) Assess test impact, (6) Generate comprehensive modification plans in Markdown format. Supports text/Markdown requirements and analyzes feature-level, functional, and API 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。
Quickly analyzes Python repositories to understand their purpose, structure, and setup requirements. Use when Claude needs to onboard to a new Python codebase, understand project structure, identify entry points, determine dependencies, or generate setup instructions. Trigger when users ask to "analyze this Python repo", "understand this codebase", "how do I run this project", "what does this repo do", or provide a Python repository path for quick start guidance.
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。
Extract and summarize core features, constraints, and priorities from requirement documents. Use when analyzing PRDs, RFCs, business requirements, user stories, epics, or any requirement documentation to identify must-have features, technical constraints, priority levels (P0/P1/P2/MoSCoW), and key decision points. Triggers when users ask to summarize, analyze, extract, or understand requirements from documents.
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。
Generate concise, structured summaries of requirements for quick team understanding. Use when analyzing requirements from text documents (MD, TXT, DOCX) or technical specifications to create bullet-point summaries that highlight core functionality and dependencies/constraints. Ideal for sprint planning, stakeholder updates, team onboarding, or any situation requiring rapid comprehension of requirement documents.
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。
Produces comprehensive summaries and insights about legacy codebases to help understand unfamiliar code. Use when onboarding to a new project, planning refactoring efforts, assessing code for acquisition/migration, or generating documentation for undocumented systems. Analyzes architecture, dependencies, code quality issues, and test coverage. Creates high-level overviews with architecture diagrams, key components, entry points, and actionable insights for understanding and improving legacy 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。
Generate integration tests for multiple interacting components in Python. Use when testing interactions between: (1) Multiple services or APIs (REST/GraphQL endpoints, microservices), (2) Database operations with repositories/ORMs (SQLAlchemy, Django ORM), (3) External services (payment gateways, email services, third-party APIs), (4) Message queues and event-driven systems, (5) Full stack workflows (API + database + business logic). Provides test structure templates, fixtures, test data builders, and patterns for pytest-based integration testing.
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。
Identify the precise location of bugs in source code, modules, and systems. Use this skill when debugging applications, investigating test failures, analyzing error reports, tracing runtime issues, or performing root cause analysis. Analyzes stack traces, error messages, failing tests, and code patterns to pinpoint buggy functions, classes, files, or modules with confidence rankings and supporting evidence.
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。