スキル一覧に戻る
cyrup-ai

claude-agent

by cyrup-ai

0🍴 0📅 2025年12月26日
GitHubで見るManusで実行

SKILL.md


name: claude-agent description: > Spawn and manage Claude sub-agents for parallel or delegated tasks. WHEN: User needs parallel operations, multi-step autonomous research, or wants to delegate complex tasks to sub-agents. Use when task can be parallelized or requires independent exploration. WHEN NOT: Simple single-step operations, sequential dependencies, specific file reads (use fs_read_file), writing code directly (do it yourself). version: 0.1.0

Claude Agent - Sub-Agent Delegation

Core Concept

mcp__plugin_kg_kodegen__claude_agent spawns independent Claude sub-sessions that can execute tasks autonomously. Each agent has its own conversation context, can use tools, and returns a final report. Perfect for parallel research, independent code analysis, or complex multi-step delegations.

Five Actions

SPAWN (Default)

Create a new agent session with initial prompt.

SEND

Send additional prompt to existing agent.

READ

Read current output from agent.

LIST

List all active agent sessions.

KILL

Terminate agent session and cleanup.

Key Parameters

ParameterTypeRequiredDescription
actionstringNoSPAWN (default), SEND, READ, LIST, KILL
agentnumberNoAgent instance (0, 1, 2...), default: 0
promptstringSPAWN/SENDTask for the agent to perform
system_promptstringNoCustom system prompt for agent behavior
await_completion_msnumberNoTimeout in ms (default: 300000 = 5 min)
max_turnsnumberNoMax conversation turns (default: 10)
allowed_toolsarrayNoTools agent CAN use (allowlist)
disallowed_toolsarrayNoTools agent CANNOT use (blocklist)
cwdstringNoWorking directory for agent
add_dirsarrayNoAdditional context directories

Usage Examples

Spawn Research Agent

{
  "action": "SPAWN",
  "prompt": "Research all error handling patterns in this codebase. Return a summary of patterns found with file locations.",
  "max_turns": 15
}

Parallel Agents for Different Tasks

// Agent 0: Research
{
  "agent": 0,
  "prompt": "Find all API endpoints and document their signatures"
}

// Agent 1: Analysis (concurrent)
{
  "agent": 1,
  "prompt": "Analyze test coverage and identify untested code paths"
}

Restricted Agent (Read-Only)

{
  "prompt": "Review this codebase for security vulnerabilities",
  "allowed_tools": ["fs_read_file", "fs_search", "fs_list_directory"],
  "disallowed_tools": ["terminal", "fs_write_file", "fs_delete_file"]
}

Background Agent with Timeout

{
  "prompt": "Deep dive into the authentication system architecture",
  "await_completion_ms": 60000,
  "max_turns": 20
}

Check Agent Progress

{"action": "READ", "agent": 0}

List All Agents

{"action": "LIST"}

Terminate Agent

{"action": "KILL", "agent": 0}

When to Use What

ScenarioUse Agent?Why
Search for keyword in codebaseYesAgent explores autonomously
Read specific known fileNoUse fs_read_file directly
Parallel research tasksYesSpawn multiple agents
Write codeNoDo it yourself
Complex multi-step analysisYesAgent handles autonomously
Simple calculationNoOverkill

Best Practices

  1. Be specific in prompts - Tell agent exactly what to return
  2. Specify output format - Request structured results
  3. Use tool restrictions - Limit agent capabilities when appropriate
  4. Launch concurrently - Multiple agents in single message for parallelism
  5. Trust agent output - Results are generally reliable

Remember

  • Agents are stateless - each invocation is independent
  • Agent results are not visible to user - you must summarize
  • Prompts should be highly detailed - agent works autonomously
  • Launch multiple agents concurrently for parallel work
  • Specify if agent should research only vs write code

スコア

総合スコア

55/100

リポジトリの品質指標に基づく評価

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

+10
説明文

100文字以上の説明がある

0/10
人気

GitHub Stars 100以上

0/15
最近の活動

3ヶ月以内に更新がある

0/10
フォーク

10回以上フォークされている

0/5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

0/5
タグ

1つ以上のタグが設定されている

0/5

レビュー

💬

レビュー機能は近日公開予定です