'ai-agent' 검색 결과

Review GitHub issues and pull requests to assess demonstrated user need, existing alternatives, correctness, compatibility, and maintenance cost. Using static evidence, it develops a maintainer recommendation and compares competing proposals without running code or changing remote items.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether an issue warrants maintainer action
• Assessing whether a pull request is worth merging
• Comparing competing pull requests for the same problem

Look up OpenAI API and platform details in official documentation through the documentation MCP server. Retrieve relevant pages, endpoint definitions, and parameters so integration guidance and factual claims are grounded in the fetched source text.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify specifications before integrating an OpenAI API
• Check endpoint parameters and definitions
• Confirm documented defaults and limits

Investigate runtime behavior that code review and normal tests cannot resolve. Plan controlled cases, disclose commands and effects for approval, then use temporary scripts to report observed results, comparison conditions, and supporting evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating behavior that normal tests cannot explain
• Comparing latency under controlled conditions
• Checking integration failures and retry behavior

Update pnpm locally and align package.json with GitHub Actions settings. Check the published package and CI installation paths first, then record package integrity and pin the setup action to a specific commit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Updating pnpm across local development and CI
• Checking installation paths before an upgrade
• Refreshing GitHub Actions commit pins

Turn the actual task changes into an English PR title, description, and branch name suggestion. Include relevant committed and uncommitted work, explain the motivation and behavior changes, and prepare text ready to paste into GitHub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a PR draft after implementation
• Explaining the motivation and behavior changes in English
• Summarizing changes including untracked files

Define the scope of SDK behavior changes, including required behavior, compatibility obligations, unsupported cases, and existing alternatives. Guide implementation toward existing code paths, and reassess the design when review feedback introduces repeated complexity or expands supported behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define the supported scope before changing SDK behavior
• Assess compatibility with released APIs and saved data
• Revisit a design as review findings accumulate

Carry an agreed implementation through a separate Git working copy, focused checks, and final review. Receive a local branch with one commit and a PR draft, while preserving the original checkout and unrelated changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing agreed changes in a separate working copy
• Preparing a reviewed local branch with one commit
• Taking over another author's pull request

Analyze CSV files with Python and get concise numerical summaries. The skill checks column structure before calculating requested aggregates, includes units when available, and explains assumptions when data is missing or incorrectly formatted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Quick summaries of CSV data
• Calculating requested aggregates
• Reporting assumptions about missing or malformed data

Measure JS SDK test coverage and identify untested behavior using coverage reports. It supports gap assessments and scoped test improvements, prioritizing public behavior, error handling, cancellation, and lifecycle paths, then reporting verification results and remaining gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing current JS SDK test coverage
• Investigating a decline in coverage metrics
• Adding tests for uncovered public behavior

Compare English SDK documentation with source code to find missing features, incorrect defaults, and outdated explanations. Produce evidence-backed findings or update the requested pages, with verification matched to the scope of the documentation changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit documentation for a specific SDK feature
• Check documentation against branch changes
• Correct outdated option names and defaults

Run final checks after reviewed SDK changes, covering dependency installation, builds, types, generated declarations, linting, tests, and formatting. The workflow requires every check to pass before the work is marked complete.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Final verification after SDK code changes
• Checking changes to tests or build configuration
• Rechecking the full validation sequence after fixes

Analyze existing logs and source files from a completed example run to verify that intended actions actually occurred. Review every entry, classify failures and skips, and report evidence without running examples, controlling processes, or modifying files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify behavior after a completed example run
• Check whether exit-zero examples performed their intended actions
• Separate repository failures from environment restrictions

Audit JS SDK logs, exceptions, and telemetry for exposed model or tool inputs and outputs. It traces values to logging destinations, verifies suppression settings, fixes demonstrated leaks, and adds regression tests while preserving permitted operational diagnostics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing logs for model and tool payload exposure
• Checking that data-suppression flags work
• Fixing demonstrated leaks through error logging

Review a JavaScript SDK release plan or final candidate against the previous release. Check compatibility, packaging, regressions, and Changesets version records, then recommend shipping or blocking with supporting evidence and a separate assessment of documentation coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review the next SDK release plan
• Assess whether a final release candidate can ship
• Check generated versions and changelogs for consistency

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

Route generic agent-history ingestion requests to the appropriate specialized history-ingest skill. Use when the user asks to ingest AI-agent history without directly selecting the underlying agent-specific skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[omh] AI agent progress for managers: help managers inspect AI-agent progress, blockers, quality gates, and throughput levers. Use when the user says: agent-ops-review, agent ops review, agent productivity, operator productivity, manager view, quality dashboard, throughput review, agent work quality.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Discover, find, compare, audit, repair, adapt, craft, run, debrief, save, and prepare repeatable AI-agent loops for publication. Use when a user asks to analyze code or coding threads for recurring work, find a published loop, interview them to turn a goal into a bounded loop, review a loop for weak checks or unsafe authority, execute a loop with an evidence receipt, learn from completed runs, save or reuse a project loop, or validate and submit a loop to Loop Library.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent agents", "agent threads", "agent runs", "streaming agents", "function calling agents .NET".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate a local repository risk receipt before Claude Code or other AI-agent edits. Use when the user asks to prepare a repository for agent changes, check for risky hooks or credential-writing automation, or run the /agent-preflight command. Trigger with "run agent preflight", "check this repo before agent edits", or "/agent-preflight".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Grida AI agent system work: `@grida/daemon` (DaemonServer, loopback HTTP perimeter, files/workspaces, secrets store, daemon discovery) and `@grida/agent` (the agent tenant: sessions, providers/BYOK, runtime/tool execution, skills discovery, prompts, tiers, sandbox hosts). Use for `packages/grida-daemon/**`, `packages/grida-ai-agent/**`, desktop sidecar protocol changes, agent chat transport, and bugs in agent state or streams. For pure Electron window, preload, menu, deep-link, or CDP work, use `desktop`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.