From tac
Manages agent fleets via CRUD (create, command, monitor, delete) and lifecycle patterns. For multi-agent systems, resource cleanup, and state tracking with Claude Agent SDK.
npx claudepluginhub melodic-software/claude-code-plugins --plugin tacThis skill is limited to using the following tools:
Manage agent fleets through Create, Command, Monitor, and Delete operations.
Manages lifecycle of temporary and defined orchestration agents: creation, execution, promotion to permanent, cleanup, and namespacing. Activates on custom agent creation, lifecycle queries, or temp agent mentions.
Designs O-Agent orchestrators for multi-agent fleet management using Claude SDK. Guides scope definition, agent templates, prompts, and tools for create/command/monitor/delete workflows.
Orchestrates AI agents by scanning statuses, assessing progress, sending self-contained instructions, and coordinating multi-agent workflows. Use for managing agent progress or multi-agent dev tasks.
Share bugs, ideas, or general feedback.
Manage agent fleets through Create, Command, Monitor, and Delete operations.
Guide the implementation of CRUD operations for agent fleets, ensuring proper lifecycle management and resource cleanup.
Implementation Note: Full lifecycle management requires Claude Agent SDK with custom MCP tools. This skill provides design patterns for SDK implementation.
Create --> Command --> Monitor --> Aggregate --> Delete
| | | | |
v v v v v
Template Prompt Status Results Cleanup
Spin up a new specialized agent.
Parameters:
template: Pre-defined configuration to usename: Unique identifier for this agentsystem_prompt: Custom prompt (alternative to template)model: haiku, sonnet, or opusallowed_tools: Tools this agent can useExample:
create_agent(
name="scout_1",
template="scout-fast",
# OR
system_prompt="...",
model="haiku",
allowed_tools=["Read", "Glob", "Grep"]
)
Best Practices:
Send prompts to an agent.
Parameters:
agent_id: Which agent to commandprompt: The detailed instructionExample:
command_agent(
agent_id="scout_1",
prompt="""
Analyze the authentication module in src/auth/.
Focus on:
1. Current implementation patterns
2. Security considerations
3. Potential improvements
Report findings in structured format.
"""
)
Best Practices:
Check agent status and progress.
Operations:
# Check status
check_agent_status(
agent_id="scout_1",
verbose_logs=True
)
# List all agents
list_agents()
# Read agent logs
read_agent_logs(
agent_id="scout_1",
offset=0,
limit=50
)
Status Values:
| Status | Meaning |
|---|---|
idle | Ready for commands |
executing | Processing prompt |
waiting | Waiting for input |
blocked | Permission needed |
complete | Finished |
Clean up agents when work is complete.
Example:
delete_agent(agent_id="scout_1")
Key Principle:
"Treat agents as deletable temporary resources that serve a single purpose."
1. Create scout agent
2. Command: Analyze codebase
3. Monitor until complete
4. Aggregate scout findings
5. Delete scout
6. Create builder agent
7. Command: Implement based on findings
8. Monitor until complete
9. Aggregate build results
10. Delete builder
Phase 1: Scout
- Create scouts (parallel)
- Command each with specific area
- Aggregate findings
Phase 2: Build
- Create builder
- Command with scout reports
- Monitor implementation
Phase 3: Review
- Create reviewer
- Command to verify implementation
- Generate final report
Cleanup: Delete all agents
Create: scout_1, scout_2, scout_3 (parallel)
Command each with different area
Monitor all until complete
Aggregate all findings
Delete all scouts
Create: builder_1, builder_2 (parallel)
Command each with different files
Monitor all until complete
Aggregate all changes
Delete all builders
---
name: scout-fast
description: Quick codebase reconnaissance
tools: [Read, Glob, Grep]
model: haiku
---
# Scout Agent
Analyze codebase efficiently. Focus on:
- File structure
- Key patterns
- Relevant code sections
Report findings concisely.
---
name: builder
description: Code implementation specialist
tools: [Read, Write, Edit, Bash]
model: sonnet
---
# Builder Agent
Implement changes based on specifications.
Follow existing patterns.
Test your changes.
Report what was modified.
---
name: reviewer
description: Code review and verification
tools: [Read, Grep, Glob, Bash]
model: sonnet
---
# Reviewer Agent
Verify implementation against requirements.
Check for issues and risks.
Report findings by severity.
Track agent state for observability:
{
"agent_id": "scout_1",
"template": "scout-fast",
"status": "executing",
"created_at": "2024-01-15T10:30:00Z",
"last_activity": "2024-01-15T10:32:15Z",
"context_tokens": 12500,
"cost": 0.05,
"tool_calls": 15
}
| Trigger | Action |
|---|---|
| Work complete | Delete immediately |
| Error state | Delete and report |
| Timeout | Delete and warn |
| User abort | Delete all |
When implementing lifecycle management, provide:
## Lifecycle Implementation
### Agent Templates
[List of templates with configurations]
### CRUD Tools
| Tool | Implementation | Parameters |
| --- | --- | --- |
| create_agent | ... | ... |
| command_agent | ... | ... |
| check_agent_status | ... | ... |
| list_agents | ... | ... |
| delete_agent | ... | ... |
### State Schema
[JSON schema for agent state]
### Cleanup Logic
[When and how agents are deleted]
| Anti-Pattern | Problem | Solution |
|---|---|---|
| Keeping dead agents | Resource waste | Delete when done |
| Long-lived agents | Context accumulation | Fresh agents per task |
| Generic agents | Unfocused work | Specialized templates |
| Missing cleanup | Dead agents accumulate | Always delete |
| Reusing agents | Context contamination | Create fresh |
"The rate at which you create and command your agents becomes the constraint of your engineering output."
"One agent, one prompt, one purpose - then delete."
Date: 2025-12-26 Model: claude-opus-4-5-20251101