Organize a task into parallel work streams with clear dependencies, separate places for changes, and explicit checks. Batch independent reads and tests, coordinate concurrent work, and report completed or blocked tasks with verification results.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Speed up a task by turning it into a dependency graph of parallel lanes with a lane matrix, batched reads and checks, write surfaces isolated by file, worktree, branch, or service, and a final verification table. Use when the user wants a task done much faster through parallel work, concurrent agents, batched tool calls, isolated worktrees, or many independent verification lanes without losing correctness.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when speed comes from doing independent work at the same time: repo inspection, file reads, API checks, browser checks, build/test lanes, deploy readbacks, or multi-worktree implementation passes.
Turn urgency into a dependency graph before acting.
Before a large push, write a compact matrix:
Lane | Can run in parallel? | Write surface | Risk | Verification
Repo scan | yes | none | low | rg/git status outputs
Backend patch | maybe | src/api | medium | unit tests
Frontend patch | maybe | app/components | medium | browser screenshot
Deploy readback | after build | remote service | high | live URL + logs
Only run lanes in parallel when their write surfaces do not collide.
Use this when reporting:
Parallel execution result:
- Lanes run: 5
- Lanes completed: 4
- Blocked lane: deploy readback, waiting on DNS propagation
- Fast path found: batched repo scan + focused tests
- Verification: lint pass, unit pass, live smoke pass
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Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.