'correctness' 검색 결과

Benchmark how CodeGraph affects an agent investigating a real codebase. Choose a version, language, repository, and execution mode, then compare cost, tool calls, file reads, and answer correctness with and without CodeGraph.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating a local development build
• Auditing a published CodeGraph version
• Comparing investigation cost and file reads

Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a skill bundle before distribution
• Evaluating activation accuracy and output improvements
• Checking scripts, safety, and distributed files

Review code changes in a diff, pull request, branch, or file path to identify bugs and cleanup opportunities. Findings include file locations and concrete failure scenarios, with options to apply fixes or post comments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review uncommitted changes for bugs
• Check a pull request or branch before merging
• Find duplicated logic and unnecessary work

Fix white flashes and blending problems between scenes in HyperFrames videos. This skill explains background setup, overlapping scene containers, final-frame holds, and timeline templates so transitions render with the intended appearance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fixing white flashes between dark scenes
• Checking opacity dips during crossfades
• Assembling the master timeline

Review code changes through a structured check of requirements, design, correctness, security, performance, and tests. Produce specific, constructive feedback that separates required fixes from optional suggestions, and build shared review guidelines for a development team.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a pull request systematically
• Create a shared code review checklist
• Give actionable feedback to junior developers

Simplify code once its behavior is understood. Confirm the existing behavior through tests or reproduction, reduce unnecessary complexity in small, reviewable steps, and keep verification passing while preserving behavior and necessary safety checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Making code easier to read without changing behavior
• Reducing complexity after confirming existing behavior
• Reviewing cleanup changes in small steps

Review a pull request or code diff for bugs by reading callers, expected behavior, and nearby error handling. Receive findings tied to specific lines, ranked by severity, plus a summary of merge risks and needed tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a pull request for behavioral regressions
• Reviewing risks to stored data
• Investigating input validation and access checks

Add a CUDA/C++ GPU operation to sgl-kernel using AOT, meaning compilation before use. Follow the implementation through PyTorch registration, Python API exposure, SGLang integration, correctness tests, and benchmarks that account for cache reuse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a CUDA/C++ operation to sgl-kernel
• Registering an operation with PyTorch and exposing a Python API
• Testing numerical results and invalid inputs

Guides language model training with GRPO in TRL, comparing several responses and rewarding better results. Covers dataset preparation, scoring rules for correctness and format, training configuration, monitoring, and saving models for later use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training models to follow JSON or XML formats
• Designing rewards for math and coding tasks
• Adjusting training to available GPU memory

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

Assess GitHub issues and pull requests by checking demonstrated user needs, existing alternatives, compatibility, and maintenance costs. Produce an evidence-based maintainer recommendation through code and document inspection, without running tests or examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether a pull request merits merging
• Checking whether existing features already solve an issue
• Comparing competing fixes for the same problem

Edit videos through conversation, from transcription and take selection to cuts, color adjustments, animated overlays, and subtitles. Confirm an editing plan, review a checked preview, and refine the result before the final render.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Select the best takes for a presentation video
• Trim tutorials and add subtitles
• Shape interviews or travel footage

Reviews code changes through documented engineering and product principles associated with named practitioners. Combines two engineering perspectives and one product perspective to produce findings with severity, attribution confidence, and technical reasoning for architecture, performance, or user experience issues.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing architecture or public API changes
• Checking user experience alongside code correctness
• Strengthening a pull request before submission

Review agent-written code changes before merge, focusing on concrete correctness defects. Uses independent subagent reviews, targeted verification, and fresh checks after substantial repairs, with a deeper evidence-based process for security, concurrency, persistence, or data-loss risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing agent-written changes before merge
• Rechecking a substantial bug fix
• Examining security, concurrency, or data-loss risks

Review skill and agent definitions for structural issues and whether their instructions meet expected behavior. Four modes cover structure, test specifications, category criteria, and coverage reports that reveal missing specifications or checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check one skill or all skills for structural issues
• Compare skill or agent instructions with a test specification
• Review definitions against category criteria

Test user journeys affected by a branch’s changes in a real browser. Check correctness and the experience for intended users, fix small defects with regression tests, and commit fixes alongside a report documenting results and unresolved decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check user-facing changes before a PR is merged
• Test complete user journeys affected by a branch
• Fix small defects and add regression tests

Compare multiple NemoClaw pull requests addressing the same issue. It checks merge eligibility, correctness, quality, and attribution, then produces an evidence-based recommendation for merging or choosing a candidate for further repair.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing competing pull requests for one issue
• Checking merge eligibility before ranking proposals
• Choosing a candidate to repair when none is ready

Review Handsontable code changes for architecture, code quality, performance, accessibility, and test coverage. Findings include severity, confidence, and file locations, helping developers assess whether changes follow project rules and demonstrate the intended behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a Handsontable branch or pull request
• Check architecture before or during core changes
• Review keyboard access and performance concerns

Review code changes for unnecessary complexity, including unused flexibility, avoidable dependencies, and abstractions with little purpose. Receive concise findings that name what to remove, what can replace it, and how many lines could be cut.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check code changes for over-engineering
• Find code replaceable with standard library features
• Identify unnecessary dependencies and abstractions

Improve MNN inference on OpenCL by measuring bottlenecks and refining GPU kernels, the code that performs calculations. The skill guides baseline measurements, correctness checks, Android device benchmarks, and integration of new OpenCL features with compatibility fallbacks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find bottlenecks in a model's GPU inference
• Optimize a specific operator's GPU kernels
• Integrate an OpenCL extension with a fallback

Optimize and troubleshoot MNN's Hexagon DSP backend, which runs computation on a dedicated processor. The workflow uses device measurements to identify bottlenecks, compare v79 and v81 architectures, verify correctness, and investigate crashes or memory usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in Hexagon operators
• Checking changes on v79 and v81 devices
• Investigating cDSP crashes and device disconnects

Guide MNN Metal backend development and optimization, from finding performance bottlenecks to changing GPU computation, combining operators, and adjusting execution scheduling. A task-based document index directs work toward relevant implementation rules, benchmarks, and correctness checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in Metal inference
• Adding or modifying Metal kernels
• Combining operators into one GPU dispatch

Review Doris code changes using project-specific checklists for data correctness, concurrency, memory safety, compatibility, performance, and tests. Trace related code paths and explain findings with concrete failure scenarios, using query plan trees when reviewing optimizer behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review newly completed Doris code changes
• Check concurrency and memory management in related code paths
• Assess compatibility and test coverage before merging

Review code changes and files for bugs, security risks, performance problems, design issues, and test coverage. Receive a structured report with prioritized findings, concrete improvement examples, positive feedback, and an overall review verdict.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a pull request
• Auditing code quality and security
• Finding refactoring opportunities