'subagent' 검색 결과

Design autonomous agents for Claude Code plugins, including when they should run, what tools they may use, and how they should respond. Provides configuration templates, prompt guidance, and validation steps for creating agent files.

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• Create a code review agent
• Refine examples that trigger an agent
• Limit the tools an agent can use

Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.

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• Turning product requirements into development tasks
• Coordinating implementation, testing, and reviews
• Resuming extended work from saved progress

Coordinate multiple agents to maintain context-mode, from sorting GitHub issues and reviewing pull requests to preparing releases and announcements. Workflows require reproduced bugs, source evidence, test-first changes, and checks across supported platforms.

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• Investigating reported bugs and their history
• Reviewing contributor pull requests
• Preparing releases and compatibility checks

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

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• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

Deliver knowledge-page updates with links derived from Git paths and checked after pushing. Use defined alternatives for local repositories, rewrite paths relayed by other agents, and keep links between pages relative to preserve their connections.

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• Include verified page links in completion messages
• Investigate page links that return a 404 error
• Rewrite local paths returned by other agents for delivery

Build a workflow for importing large collections of audio, email, documents, or chats into knowledge pages. Test varied samples first, track progress in a persistent JSON file, and verify outputs before marking work complete.

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• Importing a large email export into knowledge pages
• Converting document collections into linked pages
• Resuming imports that span multiple sessions

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.

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

Prepare an OpenSEO release by updating the version, selecting user-visible changes from commits, and writing concise release notes. Review agents check the draft against the code before a pull request is opened for the release.

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• Prepare an OpenSEO release pull request
• Summarize changes since the previous release
• Update the version and release notes together

Review agent work histories to identify recurring failures, repeated discoveries, and stalled tasks. Improve the prompts, specialist agents, skills, tools, and memory that guide future work, then submit those changes separately for review.

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• Addressing recurring command or tool failures
• Recording findings that agents repeatedly rediscover
• Turning repeated procedures into skills or tools

Guide new Kortix users from a vague idea to a concrete task and a finished deliverable. Introduce relevant connections, memory, parallel agents, or scheduled follow-ups as useful next steps after completing the first result.

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• Help new users choose their first Kortix task
• Turn a vague request into a concrete deliverable
• Guide users through completing their first result

Configure agents, tools, app deployments, and scheduled work in Kortix. This reference explains project files, command-line operations, permissions, and the differences between pi and OpenCode, helping agents follow the platform's configuration and change-review process.

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• Configure agents, skills, and custom tools
• Understand pi and OpenCode configuration differences
• Find procedures for deploying a Kortix App

Guide a Trae agent through recurring failures with diagnosis, alternative approaches, and relevant checks. The skill defines when to intervene, requires evidence for completion, and separates the agent’s self-review from external validation.

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• Change approaches after repeated failures
• Check evidence behind a completion claim
• Review risks and validation gaps before delivery

Use cmux on macOS to organize visible teams of AI agents, send prompts, inspect terminal output, and monitor completion. Set up role-based teams or have several agents investigate the same problem; live operation remains unproven in the supplied material.

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• Launch a team to investigate an intermittent test failure
• Have multiple agents investigate the same bug
• Send follow-up prompts to a running agent

Run repeated reviews of research work, implement fixes, and request another assessment for up to four rounds by default. The workflow records progress and stops when both the review score and verdict meet its acceptance rules.

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• Revising research work through repeated reviews
• Keeping a record of review findings and fixes
• Resuming an interrupted review process

Audit a website for visibility in AI search, including how easily its content can be quoted, technical access, content quality, and structured data. Receive a weighted score out of 100 and a prioritized improvement plan.

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• Assessing a website’s readiness for AI search
• Finding crawler access and structured data issues
• Reviewing content credibility and ease of quotation

Test polly's coordination of coding agents using a scripted mock language model and live command-line tools. Check startup, task dispatch, safety rules, parallel delegation, and whether real agents make appropriate orchestration decisions.

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• Reproducing polly orchestration bugs
• Checking safety rules with a mock language model
• Verifying delegation and cross-review with real agents

Analyze bug-fix commits in a Git repository to uncover recurring failure patterns. Produce individual bug analyses and a reusable pattern guide that removes project-specific details, helping inform future code reviews.

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• Extract recurring bug patterns from past fixes
• Build a reusable guide for code reviews
• Analyze fixes within a chosen date range or path

Review medium to large code changes using checks selected for the actual patch. Analyze the change and surrounding code, assign focused reviews by function, file, or feature, and combine findings with locations, confidence levels, and supporting evidence.

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• Reviewing patches of roughly 50 to 1,000 lines
• Checking risks specific to a changed feature
• Reviewing changes alongside callers and related implementations

Turn development requirements into an implementation plan with specific files, function signatures, tests, and verification commands. Use it before coding to document decisions and divide the work into independently testable tasks.

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• Planning a feature before writing code
• Handing implementation work to another developer
• Checking that planned tasks cover every requirement

Execute an implementation plan by assigning each independent task to a fresh helper agent in sequence. Review specification compliance and code quality after each task, track decisions in files, and finish with a review of the entire branch.

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• Execute a plan with separate implementation and review roles
• Check specification compliance and code quality after each task
• Resume work using a persistent progress ledger

Organizes a code review by giving a reviewer subagent the requirements, a change summary, and the relevant Git commits. It helps check completed work and handle findings by severity before continuing or merging.

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• Review a completed development task
• Check a major feature against its requirements
• Request a code review before merging into main

Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.

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• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

Select relevant code using Trailmark's map of function relationships, then delegate explanation, review, or edit proposals to a smaller model. Limit the supplied source, check cited evidence, and verify proposed changes before applying them.

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• Asking a small model to explain a function
• Tracing callers and paths from entrypoints
• Requesting edit proposals for a specific line range

Organize open GitHub issues and pull requests using the gh CLI, GitHub's command-line tool. Identifies resolved issues and pending fixes, estimates priorities and change sizes locally, and proposes closures, links, reviews, or merges for approval.

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• Clean up resolved issues left open
• Link issues to pull requests with pending fixes
• Estimate priorities and change sizes for outstanding work