'search debug' 검색 결과

Use when the user needs to search or navigate code with Sourcegraph MCP tools. Provides disciplined search workflows for finding implementations, understanding systems, debugging issues, fixing bugs, and reviewing code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build applications and agents with Exa's API Platform: search, contents, answer, context, Agent API, monitors, websets, OpenAI-compatible endpoints, and exa-py / exa-js. Use when choosing Exa endpoints, writing Exa API calls, integrating semantic web search or research into products, or debugging Exa request shapes. Load references/ on demand for endpoint details.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Search and analyze Estuary task logs with jq filtering. Use when investigating task failures, debugging connector errors, analyzing behavior patterns, or streaming live logs. Use when user says "check logs", "show me logs", "task failing", "why is my capture failing", "why is my materialization failing", "error messages", "why did it fail", "search task logs", "shard failed", "follow logs", "increase log level", "debug logging", "is this log message normal", "what does this message mean", "connection timeout", or "i/o timeout".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Kotlin Coroutines와 Flow로 Android 및 Kotlin Multiplatform의 비동기 작업과 데이터 갱신을 구현하고 병렬 실행, 화면 상태, 취소, 오류 처리와 테스트를 다룹니다.

• 여러 데이터를 병렬로 불러옵니다
• StateFlow로 화면 상태를 관리합니다
• 검색 입력을 조절하고 통신을 재시도합니다

REST·GraphQL API 오류를 연결, 인증, 송수신 데이터 순으로 조사합니다. 재현 명령과 원인, 수정안을 정리하고 재발을 감지하는 회귀 테스트 작성을 돕습니다.

• API 인증 오류를 조사합니다
• Postman과 코드의 동작 차이를 재현합니다
• 페이지 분할과 호출 제한을 확인합니다

Hermes Docker 이미지의 s6 서비스 감독 구성을 파악합니다. 상주 서비스를 추가하고 프로필별 gateway의 시작·재시작 오류와 파일 소유권 문제를 조사합니다.

• Docker 이미지에 상주 서비스 추가
• gateway가 시작되지 않는 원인 조사
• 재시작 후 gateway 복원 실패 조사

PyTorch FSDP를 활용한 대규모 모델의 분산 학습 구현과 오류 조사를 돕습니다. 공식 문서 기반 자료와 코드 예제로 분할 전략, 혼합 정밀도, 학습 상태 저장을 검토합니다.

• 대규모 모델의 분산 학습을 구현합니다
• FSDP 적용 방법과 분할 전략을 비교합니다
• 혼합 정밀도와 체크포인트 예제를 확인합니다

Retrieve current documentation and code examples from Context7 for libraries, frameworks, and developer tools. Use the results to answer questions about API syntax, configuration, installation, version migration, and library-specific problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking current API syntax
• Finding setup and configuration examples
• Investigating version migration issues

Find current documentation and code examples through the Context7 MCP server. Look up library setup, API usage, migration steps, and library-specific problems, using documentation for the requested version when available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Looking up documentation for a specific version
• Finding library setup and configuration steps
• Finding examples for code that uses external libraries

Search current library documentation and code examples with Context7 CLI, a command-line tool. Find API usage and configuration guidance, select available version-specific documentation, and prioritize examples in the programming language you use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking current API usage and configuration
• Finding documentation for a requested version
• Prioritizing code examples in a chosen language

Plan Claude Code agent teams by choosing team size, roles, and agent types with the tools each task needs. Preset compositions cover reviews, debugging, implementation, research, security audits, and migrations, alongside display settings for local or CI environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose team size and roles for a development task
• Assign separate review areas to multiple agents
• Split bug investigation across competing hypotheses

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating errors in production
• Comparing error counts across time periods
• Following request traces across services

Investigate bugs using a graph of code relationships. Find relevant functions, trace calls and execution paths, check recent changes, and assess what a proposed fix could affect before reading and changing the implementation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Narrowing down code related to a bug
• Tracing the execution path that triggers an issue
• Checking whether recent changes caused a bug

Search past coding sessions across Claude Code, Codex, and Cursor to recover earlier attempts, failed approaches, decisions, and findings. It filters history by topic and time, then summarizes technical details relevant to the question.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recovering approaches tried in earlier sessions
• Reviewing past debugging attempts and failures
• Summarizing recent work across coding agents

Build and maintain interfaces with shadcn/ui by finding, adding, and combining components. The skill uses project settings, component documentation, and change previews to guide styling, debugging, preset changes, and updates that preserve local edits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building settings pages and dashboards from existing components
• Fixing form and dialog composition issues
• Updating components while preserving local edits

Turn plain-language requests into Cloud Logging queries for Google Cloud. Generate service-specific filters to find errors, investigate issues, or review audit logs using LQL, the language used to search these logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding recent error logs
• Filtering logs for a specific Google Cloud service
• Investigating who changed or accessed a resource

Guides adapting OpenPI robot models to custom datasets and serving them to produce actions from observations. Covers JAX and PyTorch training, checkpoint conversion, and troubleshooting for normalization statistics and GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tune OpenPI models on custom robot datasets
• Convert JAX checkpoints to PyTorch
• Serve policies for ALOHA, DROID, or LIBERO

Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate AI application problems using execution traces
• Evaluate answer quality across a test dataset
• Compare prompts and models through experiments

Add PyTorch FSDP2 to training scripts so model state can be split across GPUs. Covers distributed initialization, mixed precision, CPU offloading, and checkpoint saving and loading for models that exceed a single GPU's memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a model that exceeds one GPU's memory
• Adding FSDP2 to an existing training script
• Configuring mixed precision and CPU offloading

Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring faster training with lower memory use
• Learning about LoRA and QLoRA fine-tuning
• Checking Unsloth features and APIs

Get guidance on adapting LLMs through additional training with LLaMA-Factory. The skill covers a visual interface requiring no code, LoRA and QLoRA training methods, and documentation-based help with implementation, features, and debugging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learning additional training through a visual interface
• Exploring LoRA and QLoRA training methods
• Checking supported models and multimodal features

Build and maintain interfaces with shadcn/ui by finding, adding, and combining components. The skill uses project settings, component documentation, and change previews to guide styling, debugging, preset changes, and updates that preserve local edits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building settings pages and dashboards from existing components
• Fixing form and dialog composition issues
• Updating components while preserving local edits

Bugcrowd-specific reporting tactics complementing report-writing: VRT category search-and-fallback strategy when no exact match exists, manual severity override when VRT defaults underrate impact, severity-request paragraph as first body section, OOS-clause rebuttal templates (rate limiting on auth-flow endpoints, debug-info framing, user-enumeration with sensitive PII, theoretical-issue counter), chained-finding cross-reference patterns, target selection for QA-vs-prod programs, researcher-side hygiene (Bugcrowdninja email alias, account state restoration, friendly-tester posture). Use when filing a Bugcrowd submission, when VRT default seems wrong, when triager closes as OOS or downgrades severity, when chaining linked submissions, or when scope distinguishes production from QA. Pairs with report-writing and triage-validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.