Claude-skill-registry claude-agent

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/claude-agent" ~/.claude/skills/majiayu000-claude-skill-registry-claude-agent && rm -rf "$T"

manifest: skills/data/claude-agent/SKILL.md

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

Parameter	Type	Required	Description
`action`	string	No	SPAWN (default), SEND, READ, LIST, KILL
`agent`	number	No	Agent instance (0, 1, 2...), default: 0
`prompt`	string	SPAWN/SEND	Task for the agent to perform
`system_prompt`	string	No	Custom system prompt for agent behavior
`await_completion_ms`	number	No	Timeout in ms (default: 300000 = 5 min)
`max_turns`	number	No	Max conversation turns (default: 10)
`allowed_tools`	array	No	Tools agent CAN use (allowlist)
`disallowed_tools`	array	No	Tools agent CANNOT use (blocklist)
`cwd`	string	No	Working directory for agent
`add_dirs`	array	No	Additional 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

Scenario	Use Agent?	Why
Search for keyword in codebase	Yes	Agent explores autonomously
Read specific known file	No	Use fs_read_file directly
Parallel research tasks	Yes	Spawn multiple agents
Write code	No	Do it yourself
Complex multi-step analysis	Yes	Agent handles autonomously
Simple calculation	No	Overkill

Best Practices

Be specific in prompts - Tell agent exactly what to return
Specify output format - Request structured results
Use tool restrictions - Limit agent capabilities when appropriate
Launch concurrently - Multiple agents in single message for parallelism
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