'feedback' 검색 결과

GitHub, GitLab, Perforce 변경의 검토 의견, 자동 검사 실패와 설명 누락을 확인하고, 할 일을 정리하며 요청에 따라 수정과 처리된 스레드 해결을 지원합니다.

• 변경 사항을 제출하기 전에 점검합니다
• 실패한 검사와 수정할 항목을 확인합니다
• 사람과 봇의 검토 의견을 정리합니다

기존 웹 화면에 버튼 조작 피드백과 상태 변화를 전하는 절제된 애니메이션을 추가합니다. 글과 레이아웃을 유지하며 동작 줄이기 설정도 지원합니다.

• 버튼에 조작 피드백을 추가할 때
• 화면의 상태 변화를 전달할 때
• 주요 콘텐츠의 등장 효과를 다듬을 때

반복하는 작업 절차를 스킬로 만들고 테스트와 피드백으로 개선합니다. 기존 스킬 수정, 적용 전후 결과 비교, 적절한 요청에 호출되도록 설명 조정도 지원합니다.

• 반복 작업을 재사용할 스킬로 만듭니다
• 피드백을 바탕으로 기존 스킬을 개선합니다
• 스킬 적용 전후의 출력을 비교합니다

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Create agents that learn from interactions and adapt their behavior over time. Monitor learning progress, review efficiency and accuracy metrics, trigger adjustments from feedback, and share acquired knowledge with other agents through dedicated tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an agent with initial learning settings
• Monitor learning progress, efficiency, and accuracy
• Adjust agent behavior based on feedback

Review and implement loading feedback for operations such as fetching content or submitting actions. Use skeleton placeholders, spinners, and accessible status updates to communicate progress, while checking for layout shifts and measuring actual performance bottlenecks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding loading feedback during data fetching
• Showing progress while an action is running
• Checking screen reader announcements for loading states

Create and edit Google Forms, inspect existing forms, and read survey responses through an agent. The skill connects answers to their questions and retrieves every response when a complete set is requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a feedback survey
• Update an existing form's description
• Read survey responses alongside their questions

Design rewards for reinforcement learning, where feedback guides model behavior. Choose how to score outcomes and intermediate actions, check for scoring loopholes, and use independent tests to detect incomplete work presented as finished.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing rewards for reinforcement learning
• Investigating scoring loopholes and early completion
• Choosing outcome rewards or intermediate feedback

Review papers, drafts, and research ideas with evidence-linked feedback focused on improvement. Audit low-stakes assessment rubrics, trace supporting evidence, and check rater agreement and uncertainty without ranking people or recommending consequential decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare evidence-linked feedback on a paper draft
• Review strengths and limitations of a research idea
• Check assessment criteria and evidence traceability

Use Fictiv in a browser to upload CAD parts, configure manufacturing requirements, compare prices and lead times, and review manufacturability feedback. Supports authorized ordering, payment workflows, order tracking, reordering, and troubleshooting upload or checkout problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Get manufacturing quotes for CAD parts
• Compare materials, quantities, and lead times
• Review manufacturability warnings and upload problems

Turn a Cherry Studio support conversation into an editable draft for a bug report, usability issue, or feature request. The user reviews the wording, chooses any diagnostic data, and submits the report themselves.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft a bug report from a support conversation
• Organize feedback about interface usability
• Prepare a feature request for review

Prepare and submit bug reports or feature requests to GitHub, checking for duplicates and following issue templates. Reports are previewed for approval before submission, or saved locally when GitHub authentication is unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Report a bug to GitHub with clear reproduction steps
• Prepare a feature request using the issue template
• Save reports locally when GitHub login is unavailable

Organize feedback on an open GitHub pull request, make scoped code fixes, and verify the results. Prepare evidence-backed replies and, when authorized, post responses or resolve individual review threads.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organizing comments and review threads on a pull request
• Fixing code based on review feedback
• Preparing replies with commits and verification results

Practice an interview tailored to a specific job, with a chosen format and difficulty. Answer one question at a time, handle follow-up questions, and receive scores and improvement suggestions grounded in your answers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Practice answering questions for a specific job
• Get feedback and retry after each answer
• Rehearse with feedback held until the end

Add social-emotional learning—practicing emotional awareness, self-management, empathy, and collaboration—to subject lessons in OpenMAIC. It connects these goals to academic concepts through discussion, choices, feedback, retries, and reflection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding emotional check-ins to the start of a subject lesson
• Practicing empathy through discussion of different viewpoints
• Building feedback and retry paths into learning activities

Design subject lessons that also teach students how to learn. Add recall practice, explanations in their own words, predictions, and reflection to help learners check their understanding and plan later review while studying the main concepts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding recall practice to subject lessons
• Having students explain concepts in their own words
• Designing feedback and retries after mistakes

Turn Codewhale bugs and feature ideas into GitHub issues. Gather reproduction details, check for duplicates, and present the exact draft for approval before submitting it and returning the issue link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Report a Codewhale bug with reproduction steps
• Submit a feature request for Codewhale
• Check open issues for duplicate reports

Work through GitHub pull request feedback by classifying findings, fixing code, running repository checks, and posting replies. With suitable permissions, the skill repeats Copilot reviews until the latest code is reviewed and every thread has an agent response.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Address outstanding Copilot review comments
• Run repeated reviews on a completed pull request
• Explain declined suggestions and hand off design decisions

Turn support feedback or chat logs into a structured requirements document. Use numbered questions to clarify problems, goals, users, constraints, and open questions, then guide human review before handing approved requirements to development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning support feedback into requirements
• Clarifying missing details in chat logs
• Recording requirements review and resubmission

Review products and strategy using a framework drawn from Steve Jobs’s public record. Examine user feedback, competing products, and design details to identify unnecessary features, clarify priorities, and assess the experience across the whole product.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing which product features to remove
• Reviewing interactions using user feedback and competing products
• Defining a product’s value in one sentence

Set up an email reporting button and an automated security response workflow. Classify reported messages, extract attack indicators, remove confirmed phishing emails, and send feedback to the people who reported them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a phishing reporting button in Outlook
• Automating triage of a dedicated reporting mailbox
• Retracting confirmed phishing emails across inboxes

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Store conversations, agent activity, and feedback in cognee sessions, then carry them into long-term memory. This skill explains improve(), its processing stages, result statuses, and settings for diagnosing skipped work or controlling automatic runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving conversations and agent activity by session
• Moving session content into long-term memory
• Investigating skipped or blocked processing stages

Combine growth results, customer feedback, and dated competitor changes into a single weekly marketing brief. Connect the findings, highlight the week’s main issue, and prioritize up to three actions with supporting numbers and estimated value.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review weekly sales and marketing trends
• Connect customer feedback with growth results
• Track meaningful changes among competitors