'ai-agent' 검색 결과

Plan SDK behavior changes by defining required outcomes, compatibility boundaries, and unsupported cases. Reuse existing implementation paths, assess review feedback, and reconsider designs when repeated fixes introduce complexity beyond the original requirement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define the scope of an SDK behavior change
• Assess compatibility with released APIs
• Reconsider a design after repeated review findings

Compare a Python SDK release plan or candidate with the previous release to assess compatibility, versioning, and proven regressions. Produce a ship-or-block recommendation, documentation handoff, and key changes for minor releases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan the next patch or minor SDK release
• Assess whether a release candidate can ship
• Check compatibility and migration paths

Compare SDK implementation with English documentation to find missing features, incorrect defaults, and outdated behavior descriptions. Report evidence and suggested locations, or make requested updates while keeping navigation consistent and following the repository’s documentation checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing documentation for a specific SDK feature
• Updating English docs after implementation changes
• Correcting outdated settings and default values

Measure Python SDK test coverage, the code exercised by tests, and identify meaningful gaps in user-visible behavior. Report proposed scenarios or add authorized tests, then verify the changes and report remaining gaps alongside coverage results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing Python SDK test coverage
• Investigating a decline in coverage metrics
• Finding untested error and cancellation paths

Carry agreed implementation work through a separate Git worktree, an isolated working directory. After checks and review, it prepares one local commit, a clean branch, and a pull request draft for handoff without publishing them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting agreed implementation in a separate worktree
• Preparing a reviewed local branch with one commit
• Taking over an existing pull request while preserving author credit

Review completed code changes against the original requirements, choosing review depth by behavioral risk. Coordinate independent reviewers, resolve actionable findings, and verify that the delivered changes match the content actually reviewed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review completed changes before final verification
• Choose review depth based on behavioral risk
• Re-review fixes after actionable findings

Audit Python SDK logs, exceptions, and telemetry for sensitive data exposure. Trace values to their sources, fix confirmed leaks, and test that redaction hides sensitive information while preserving the caller's expected behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit logs and exceptions for sensitive data exposure
• Fix leaks involving model or tool data
• Test redaction and caller behavior

Review logs and source files from a completed manual example run. Check that results are current and complete, verify intended behavior beyond successful exit codes, and distinguish product defects from environment or credential problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review results after a manual example run
• Verify behavior in examples that exited successfully
• Separate product failures from environment problems

Run final checks on reviewed SDK changes, covering code formatting, linting for code issues, type checking, and tests. Confirm completion only after every required check succeeds, with wrappers for macOS, Linux, and Windows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify runtime code changes after review
• Run final checks after test or build configuration changes
• Rerun required checks after fixing a failure

Find repeated engineering tasks and turn them into agent workflows with clear checks, stopping rules, and approval boundaries. Compare published workflows, adapt suitable examples, or review existing ones for weak verification and uncontrolled repetition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find recurring tasks in code and conversation history
• Compare and adapt published agent workflows
• Review workflows for weak checks and missing stop rules

Keep plans, research findings, and progress in Markdown files for complex agent work. Separate task plans and automatic planning reminders help the agent recover saved context, track decisions, and identify the next action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resuming long research tasks
• Tracking phases and the next action
• Separating plans for concurrent tasks

Organize multi-step research and project work with Markdown files for plans, findings, and progress. Gemini hooks, automatic actions at session events, surface planning context so the agent can resume from saved records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resuming research after a break
• Tracking project phases and decisions
• Recording errors and test results

Keep plans, research findings, and progress in Markdown files for multi-step work in Kiro. Read saved project records when resuming a task to recover goals, decisions, and recent actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organizing findings during extended research
• Tracking a refactor across multiple phases
• Resuming work after a long break

Organize multi-step research and project work in Continue using Markdown files for plans, findings, and progress. When invoked, the skill reads saved planning context to support resuming work and reviewing earlier decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resuming interrupted work in Continue
• Recording findings during research
• Tracking project phases and decisions

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Write Python for n8n Code nodes to transform, filter, aggregate, and validate workflow data. Get guidance on reading input items, returning the required JSON structure, using standard library modules, and avoiding common errors within n8n's Python execution modes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transforming and filtering data in n8n with Python
• Calculating totals and basic statistics
• Extracting and validating text with regular expressions

Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Build and deploy AI agents on Cloudflare Workers using the Agents SDK. Supports persistent conversation state, live WebSocket connections, scheduled tasks, and tool integration, with guidance for connecting browser or React chat interfaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chatbot that preserves conversation history
• Connect a live chat interface through WebSockets
• Schedule reminders and recurring background tasks

Write and troubleshoot native Python in n8n Code nodes. Covers data filtering and transformation, migration from older Python code, valid return formats, and restricted execution environments, including cases where errors pass unprocessed input downstream.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Filtering and aggregating data in Python Code nodes
• Migrating legacy Python (Beta) code
• Fixing input-variable and return-format errors

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a calculation tool to an AI Agent
• Defining typed inputs for multiple arguments
• Fixing tool output type errors

Review completed implementation changes before final verification. Classify their impact, arrange independent review when required, resolve actionable findings, and preserve evidence that the final delivered content matches what was reviewed and passes the applicable verification checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review the complete implementation diff before final verification
• Choose review depth based on the impact of a change
• Coordinate independent review and fixes

Fix invoice total calculations in the sandbox quickstart repository by inspecting the implementation and tests, applying tax to the subtotal, and running the repository's tests to report the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fixing an invoice total calculation
• Checking how tax is applied to a subtotal
• Running tests after a calculation fix