'debugging' 검색 결과

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Test and debug REST and GraphQL APIs using curl, httpie, Postman collections, or scripted tests. Check authentication, invalid inputs, pagination, and response details to identify failures that a successful status code alone may miss.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking authentication and access permissions
• Testing invalid inputs and boundary conditions
• Debugging failed HTTP requests

Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating repeated agent failures
• Reviewing incorrect tool choices
• Refining prompts through repeated evaluation

Investigate software errors, version conflicts, and API questions across official documentation, GitHub, technical Q&A, and Chinese developer communities. Results distinguish matching errors, source authority, and reported environments, giving you documented leads and next checks for debugging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find existing reports of the same error
• Investigate package and runtime version conflicts
• Look up API usage in documentation and Q&A

Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Review lessons from difficult tasks, debugging, and failed attempts, then turn them into reusable guidance. Update relevant skill instructions or shared memory so future tasks can benefit from what was learned.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preserving lessons learned from debugging
• Improving skill instructions after repeated failed attempts
• Turning mistaken assumptions into reusable guidance

Find, add, and combine shadcn/ui components to build forms, dashboards, and other interfaces. Use project settings and component documentation to guide styling, debugging, preset changes, and updates that preserve local modifications.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build forms and dashboards from existing components
• Find and add components from registries
• Debug component structure and styling

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Split debugging, audits, searches, and code questions into read-only tasks for sub-agents. Combine their structured reports into an evidence-based answer, requesting follow-up investigations when findings conflict or leave important gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a bug without changing code
• Understanding repository structure and design
• Comparing architectures from multiple perspectives

Investigate production incidents and bugs using logs, metrics, and code evidence. The skill guides hypothesis testing, root cause analysis, fix validation, and reusable investigation notes, with access to configured observability tools such as Axiom.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate production outages and rising error rates
• Trace causes using logs, metrics, and code
• Reproduce bugs and validate minimal fixes

Investigate bugs, failing tests, and unexpected behavior through reproducible steps, logs, and comparisons with working code. Follow four stages to identify the root cause, test a hypothesis, apply a focused fix, and verify the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failing tests
• Tracing production bugs to their source
• Finding where a build or integration breaks

Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

Investigate bugs, failed tests, and unexpected behavior through reproducible steps, evidence gathering, and comparison with working code. Test one hypothesis at a time, then implement a focused fix and verify it with tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failed tests or builds
• Tracing production bugs across components
• Reassessing a problem after repeated failed fixes

Coordinate temporary agents for code exploration, independent implementation tasks, verification, reviews, or complex debugging. Define self-contained assignments, run independent work in parallel, and check returned evidence before combining the results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegating codebase exploration to inform a decision
• Running independent implementation tasks in parallel
• Getting an independent check of a high-risk change

Set up libFuzzer to test C/C++ code with varied inputs guided by which code paths run. Build test harnesses, manage sample inputs and dictionaries, enable error detectors, and reproduce crashes or investigate stalled coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting fuzz testing for a C/C++ library
• Writing or debugging an LLVMFuzzerTestOneInput harness
• Investigating runs that find no crashes or new code paths

Support shellcode development for security testing: small machine-code programs executed in memory. The skill covers x86/x64 implementation, code that works regardless of its memory address, loaders, executable conversion, debugging, and platform-specific execution constraints.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Studying address-independent x86/x64 code
• Reviewing loader memory allocation and execution
• Comparing EXE and DLL conversion tools

Plan a browser game's architecture before coding. Choose an engine, separate game rules from rendering, and define input, assets, menus, saves, debugging, and performance checks within a shared design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose an engine for a 2D or 3D browser game
• Separate game state from rendering responsibilities
• Plan input mappings and asset organization

Test and debug rendered web apps by reproducing interaction bugs, checking layouts and console errors, making focused fixes, and validating again. Results include screenshots, observed state changes, and a QA report explaining coverage and remaining gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reproducing and fixing broken UI interactions
• Checking desktop and mobile layouts
• Investigating blank pages and console errors

Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an agent that calls Python tools
• Coordinating sequential or parallel agent workflows
• Saving agent memory across restarts

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

Inspect and control live Chrome pages to investigate display problems, console errors, failed requests, and slow loading. Capture screenshots, automate page interactions, and record performance traces to identify where delays or layout shifts occur.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating page errors and failed network requests
• Finding causes of slow loading and layout shifts
• Capturing screenshots of pages or individual elements

Design specialized assistants that work with their own separate context. Define responsibilities, delegation conditions, working steps, and report formats for reusable subagents handling verification, debugging, security audits, or code reviews.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an independent agent to verify completed work
• Defining a debugging specialist
• Creating a security audit agent

Create specialized assistants for Cursor that work with their own context, separate from the main conversation. Define responsibilities, instructions, output formats, model choices, and execution settings for reusable workflows such as verification, debugging, and documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an assistant to verify completed work
• Defining a specialist for debugging and test failures
• Creating a read-only security audit assistant