Analyzes codebases to understand structure, tech stack, patterns, and conventions. Use when onboarding to a new project, exploring unfamiliar code, or when asked "how does this work?" or "what's the architecture?"
日本語の概要は準備中です。原文の説明を表示しています。
Patterns for parallel subagent execution using the Agent tool (formerly Task). Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Parallel execution spawns multiple subagents simultaneously using the Agent tool (named Task before Claude Code 2.1.63; Task still works as an alias). Subagents run in the background by default, so N tasks run concurrently, dramatically reducing total execution time.
Critical Rule: ALL Agent calls MUST be in a SINGLE assistant message for true parallelism. If the calls are in separate messages, they launch one after another.
Before spawning, verify tasks are independent:
Each subagent receives a custom prompt defining its role:
You are a [ROLE] specialist for this specific task.
Task: [CLEAR DESCRIPTION]
Context:
[RELEVANT CONTEXT ABOUT THE CODEBASE/PROJECT]
Files to work with:
[SPECIFIC FILES OR PATTERNS]
Output format:
[EXPECTED OUTPUT STRUCTURE]
Focus areas:
- [PRIORITY 1]
- [PRIORITY 2]
CRITICAL: Make ALL Agent calls in the SAME assistant message:
I'm launching N parallel subagents:
[Agent 1]
description: "Subagent A - [brief purpose]"
prompt: "[detailed instructions for subagent A]"
[Agent 2]
description: "Subagent B - [brief purpose]"
prompt: "[detailed instructions for subagent B]"
[Agent 3]
description: "Subagent C - [brief purpose]"
prompt: "[detailed instructions for subagent C]"
On Claude Code versions that still run subagents in the foreground by default, add run_in_background: true to each call.
Each subagent returns its final result to the parent conversation automatically when it finishes. Wait until every subagent has reported before synthesizing; do not poll, and do not start dependent work early. (The separate TaskOutput call is deprecated.)
Combine all subagent outputs into unified result:
When you have N tasks to implement, spawn N subagents:
Plan:
1. Implement auth module
2. Create API endpoints
3. Add database schema
4. Write unit tests
5. Update documentation
Wave 1 - spawn 3 subagents (independent of each other):
- Subagent 1: Implements auth module
- Subagent 2: Creates API endpoints
- Subagent 3: Adds database schema
Wave 2 - after wave 1 has finished (these depend on its output):
- Subagent 4: Writes unit tests
- Subagent 5: Updates documentation
Analyze multiple directories simultaneously:
Directories: src/auth, src/api, src/db
Spawn 3 subagents:
- Subagent 1: Analyzes src/auth
- Subagent 2: Analyzes src/api
- Subagent 3: Analyzes src/db
Review from multiple angles simultaneously:
Perspectives: Security, Performance, Testing, Architecture
Spawn 4 subagents:
- Subagent 1: Security review
- Subagent 2: Performance analysis
- Subagent 3: Test coverage review
- Subagent 4: Architecture assessment
When using parallel execution, task tracking (TaskCreate/TaskUpdate, or TodoWrite on older versions) differs:
Sequential execution: Only ONE task in_progress at a time
Parallel execution: MULTIPLE tasks can be in_progress simultaneously
# Before launching parallel tasks
todos = [
{ content: "Task A", status: "in_progress" },
{ content: "Task B", status: "in_progress" },
{ content: "Task C", status: "in_progress" },
{ content: "Synthesize results", status: "pending" }
]
# As each subagent reports back, mark its task completed
todos = [
{ content: "Task A", status: "completed" },
{ content: "Task B", status: "completed" },
{ content: "Task C", status: "completed" },
{ content: "Synthesize results", status: "in_progress" }
]
Good candidates:
Avoid parallelization when:
| Approach | 5 Tasks @ 30s each | Total Time |
|---|---|---|
| Sequential | 30s + 30s + 30s + 30s + 30s | ~150s |
| Parallel | All 5 run simultaneously | ~30s |
Parallel execution is approximately Nx faster where N is the number of independent tasks.
User request: "Implement user authentication with login, registration, and password reset"
Orchestrator creates plan:
Parallel execution:
Wave 1 - launching 4 subagents in parallel:
[Agent 1] Login endpoint implementation
[Agent 2] Registration endpoint implementation
[Agent 3] Password reset endpoint implementation
[Agent 4] Auth middleware implementation
[Results arrive as each subagent finishes]
Wave 2 - depends on wave 1:
[Agent 5] Integration test writing
[Synthesize into cohesive implementation]
Tasks running sequentially?
run_in_background: true is set for eachResults not available?
Conflicts in output?
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Analyzes codebases to understand structure, tech stack, patterns, and conventions. Use when onboarding to a new project, exploring unfamiliar code, or when asked "how does this work?" or "what's the architecture?"
日本語の概要は準備中です。原文の説明を表示しています。
Convex backend development guidelines. Use when writing Convex functions, schemas, queries, mutations, actions, or any backend code in a Convex project. Triggers on tasks involving Convex database operations, real-time subscriptions, file storage, or serverless functions.
日本語の概要は準備中です。原文の説明を表示しています。
Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data modeling.
日本語の概要は準備中です。原文の説明を表示しています。
Designs REST and GraphQL APIs including endpoints, error handling, versioning, and documentation. Use when creating new APIs, designing endpoints, reviewing API contracts, or when asked about REST, GraphQL, or API patterns.
日本語の概要は準備中です。原文の説明を表示しています。
Designs software architecture and selects appropriate patterns for projects. Use when designing systems, choosing architecture patterns, structuring projects, making technical decisions, or when asked about microservices, monoliths, or architectural approaches.
日本語の概要は準備中です。原文の説明を表示しています。
Designs and implements testing strategies for any codebase. Use when adding tests, improving coverage, setting up testing infrastructure, debugging test failures, or when asked about unit tests, integration tests, or E2E testing.
日本語の概要は準備中です。原文の説明を表示しています。