'ai-agent' 검색 결과

Design agents that operate desktop or browser software through screenshots, mouse actions, and keyboard input. Compare ways to locate interface elements, diagnose coordinate errors, handle changing screens, and evaluate task completion, operating speed, and safety boundaries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing screen-based automation for software without APIs
• Comparing methods for locating interface elements
• Investigating misclicks and coordinate scaling errors

Design a running status summary that keeps an agent aware of tasks, tool call counts, time, errors, and its environment. The skill covers updating that summary and balancing cache reuse against the buildup of outdated information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Addressing repeated tool calls and retry loops
• Keeping TODO progress and original goals visible
• Choosing how to update runtime status messages

Design knowledge systems that preserve document structure and connections across sources. Choose retrieval methods, organize conversation memory, and plan evidence-based updates, review workflows, and access controls for information that scattered text searches cannot adequately cover.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing searches that combine information across documents
• Organizing and retrieving long conversation histories
• Establishing knowledge update and review workflows

Plan and review systems where several AI agents work together. Assess whether multiple agents are useful, choose roles and communication methods, and define shared files, verification, and resource limits to address conflicts, spreading errors, and uncontrolled execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding between one agent and several agents
• Designing agent roles and communication
• Reviewing shared files and context boundaries

Perform mathematical calculations and descriptive statistics, summaries of a dataset, using information already supplied with sources. Report expressions, units, and assumptions so results can be checked, and identify missing data rather than guessing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculating from data with known sources
• Summarizing a dataset with descriptive statistics
• Checking expressions, units, and assumptions

Turn verified facts and calculation results from the shared conversation into a finished draft. Follow the requested language, audience, structure, and length while retaining necessary sources and qualifications without inventing citations or numbers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning verified research into a report
• Writing an explanation for a specific audience
• Drafting within a requested structure and length

Design and improve retrieval for RAG, which supplies external knowledge to AI answers. Cover document splitting, semantic and keyword search, combined result ranking, and quality measurements to identify where relevant information is being missed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building search for a knowledge base
• Comparing semantic, keyword, and hybrid search
• Diagnosing missed search results

Plan how to add external capabilities to an AI agent using MCP, a connection protocol, or Skill Hub distribution. Compare integration methods, design tools and data access, and review context costs, credentials, and third-party security risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing between MCP connections and skill distribution
• Designing an MCP server and its connection method
• Reviewing third-party tools and credential permissions

Investigate slow response starts and low cache reuse in agent applications. The skill explains how to keep instructions and tool definitions stable, append changing information, and preserve message formats so earlier processing can be reused across requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow response starts and low cache hit rates
• Deciding where to place timestamps and changing user data
• Reviewing tool order and session restoration

Find facts and data with web search, prioritizing primary or authoritative sources. Record exact figures, units, time periods, and source URLs in shared history so later calculation, coding, or writing can use traceable evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding facts backed by primary or authoritative sources
• Recording figures with units, time periods, and source URLs
• Collecting evidence before calculation or writing

Design how an AI agent finds and loads tools as needed. Compare discovery approaches, organize large tool collections, and assess tool selection accuracy, token costs, and cache reuse without placing every tool definition in the initial context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing on-demand loading for many MCP tools
• Comparing tool discovery approaches
• Investigating cache misses after loading tools

Organize the instructions, conversation history, and tool information an agent receives. Use the skill to clarify business rules, structure SKILL.md files for loading when needed, and distinguish external source material from instructions to reduce prompt injection risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reorganizing an agent's system instructions
• Reviewing SKILL.md structure and activation conditions
• Turning vague business rules into explicit decisions

Review and rewrite Chinese product announcements, articles, emails, and README text using eleven style checks. Identify repetitive phrasing, overloaded sentences, vague openings, and decorative language, then revise them into clearer statements suited to each type of writing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Revising formulaic Chinese product announcements
• Reviewing repetitive phrasing in Chinese articles
• Shortening overloaded sentences in Chinese emails

Turn agent failure records into training examples by locating the first incorrect decision and pairing preferred actions with rejected ones. Assess whether training is appropriate, then check both targeted improvements and preservation of correct behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnosing the first incorrect decision in an agent failure
• Building preference pairs from failure records
• Choosing between system fixes and model training

Design the operating layer around a coding agent so it can read and edit code, run checks, and correct errors. Define completion criteria, permission boundaries, feedback, and recovery methods, and assess which tasks suit autonomous execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a coding agent's tools and workflow
• Adding acceptance checks and permission boundaries
• Assessing tasks for autonomous agent execution

Design and evaluate how an AI agent remembers user preferences and history across conversations. Compare storage formats and memory frameworks, plan updates and compression, and distinguish personal memory from shared document retrieval while addressing sensitive information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design user memory that persists across conversations
• Compare storage formats, Mem0, and Memobase
• Evaluate recall and cross-conversation reasoning

Design agents that respond to incoming emails, external notifications, and timers. Plan how they handle interruptions, prioritize new requests, track concurrent tasks, and deliver results while longer operations continue in the background.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting agents to incoming emails and external notifications
• Scheduling reports and recurring checks
• Handling interruptions and changes during long tasks

Design how an AI agent trims long conversations and large tool outputs while retaining decisions, constraints, failed approaches, and sources. Compare summarization strategies, choose compression triggers, and consider separate subagents to keep intermediate information out of the main conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Managing large search results and tool outputs
• Preserving decisions and constraints during summarization
• Choosing compression triggers and batch processing

Design a process for AI agents to learn from execution records. Evaluate outcomes, actions, and quality, choose whether to update knowledge, instructions, code, or model parameters, and define independent validation, release checks, and rollback procedures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning repeated agent failures into reusable guidance
• Designing evidence-based reviews of execution records
• Converting browser actions into validated workflows

Design evaluation tasks that check whether agents actually complete work. Define requests, simulated user behavior, starting conditions, and verifiable success criteria, then plan difficulty levels, training-data leakage checks, and ongoing maintenance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating evaluation tasks for real business workflows
• Reviewing success criteria and verification logic
• Designing checks for evaluation data leakage into training

Design recovery paths for AI agents that loop, lose streamed responses, or hit service limits. Classify failures, choose retries or backup models, and set stopping rules to keep recovery attempts from causing further failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating repeated tool calls with no progress
• Designing recovery for interrupted responses
• Setting retry limits and stopping rules

Design and troubleshoot agent workflows that stop too early or keep repeating. The skill explains how to separate creation from review, verify real results, give actionable repair instructions, and set clear completion criteria and resource limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating completion claims made before tests run
• Designing separate creator and reviewer agents
• Resuming long tasks from verified progress

Write small, reproducible Python scripts and run them in a sandbox using execute_python. Check the script's logic and output, then report the actual results or errors while clearly distinguishing executed results from code that has not been run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a Python script's logic
• Confirming actual output from a short script
• Reproducing an error with minimal code

Design tools that AI agents can choose and use correctly. Define clear descriptions, inputs, and outputs, manage large search results, and plan approval checks, isolated execution environments, and interfaces for collaboration between agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing the scope of tools for an MCP server
• Improving tool descriptions and input examples
• Designing pagination and truncation for search results