Skill v1.0.1

version: "1.0.1" name: agents-managers description: Expert guidance for creating, building, and using Claude Code agents and the Task tool. Use when working with agents, setting up agent configurations, understanding how agents work, or using the Task tool to launch specialized agents.

<objective> Agents are specialized Claude instances that run in isolated contexts with focused roles and limited tool access. This skill teaches you how to create effective agents, write strong system prompts, configure tool access, and orchestrate multi-agent workflows using the Task tool.

Agents enable delegation of complex tasks to specialized agents that operate autonomously without user interaction, returning their final output to the main conversation. </objective>

<quick_start> <workflow>

1. Run /agents command
2. Select "Create New Agent"
3. Choose project-level (.claude/agents/) or user-level (~/.claude/agents/)
4. Define the agent:

• name: lowercase-with-hyphens
• description: When should this agent be used?
• tools: Optional comma-separated list (inherits all if omitted)
• model: Optional (sonnet, opus, haiku, or inherit)

1. Write the system prompt (the agent's instructions)

</workflow>

<example>

---
name: code-reviewer
description: Expert code reviewer. Use proactively after code changes to review for quality, security, and best practices.
tools: Read, Grep, Glob, Bash
model: sonnet
---
 
<role>
You are a senior code reviewer focused on quality, security, and best practices.
</role>
 
<focus_areas>
 
-Code quality and maintainability
-Security vulnerabilities
-Performance issues
-Best practices adherence
  </focus_areas>
 
<output_format>
Provide specific, actionable feedback with file:line references.
</output_format>
 

</example> </quick_start>

<file_structure>

Project-level agents override user-level when names conflict. </file_structure>

<configuration> <field name="name">

• Lowercase letters and hyphens only
• Must be unique

</field>

<field name="description">

• Natural language description of purpose
• Include when Claude should invoke this agent
• Used for automatic agent selection

</field>

<field name="tools">

• Comma-separated list: Read, Write, Edit, Bash, Grep
• If omitted: inherits all tools from main thread
• Use /agents interface to see all available tools

</field>

<field name="model">

• sonnet, opus, haiku, or inherit
• inherit: uses same model as main conversation
• If omitted: defaults to configured agent model (usually sonnet)

</field> </configuration>

<execution_model> <critical_constraint> Agents are black boxes that cannot interact with users.

Agents run in isolated contexts and return their final output to the main conversation. They:

• ✅ Can use tools like Read, Write, Edit, Bash, Grep, Glob
• ✅ Can access MCP servers and other non-interactive tools
• ❌ Cannot use AskUserQuestion or any tool requiring user interaction
• ❌ Cannot present options or wait for user input
• ❌ User never sees agent's intermediate steps

The main conversation sees only the agent's final report/output. </critical_constraint>

<workflow_design> Designing workflows with agents:

Use main chat for:

• Gathering requirements from user (AskUserQuestion)
• Presenting options or decisions to user
• Any task requiring user confirmation/input
• Work where user needs visibility into progress

Use agents for:

• Research tasks (API documentation lookup, code analysis)
• Code generation based on pre-defined requirements
• Analysis and reporting (security review, test coverage)
• Context-heavy operations that don't need user interaction

Example workflow pattern:

 
Main Chat: Ask user for requirements (AskUserQuestion)
↓
Agent: Research API and create documentation (no user interaction)
↓
Main Chat: Review research with user, confirm approach
↓
Agent: Generate code based on confirmed plan
↓
Main Chat: Present results, handle testing/deployment
 

</workflow_design> </execution_model>

<system_prompt_guidelines> <principle name="be_specific"> Clearly define the agent's role, capabilities, and constraints. </principle>

<principle name="use_pure_xml_structure"> Structure the system prompt with pure XML tags. Remove ALL markdown headings from the body.

---
name: security-reviewer
description: Reviews code for security vulnerabilities
tools: Read, Grep, Glob, Bash
model: sonnet
---
 
<role>
You are a senior code reviewer specializing in security.
</role>
 
<focus_areas>
-SQL injection vulnerabilities
-XSS attack vectors
-Authentication/authorization issues
-Sensitive data exposure
</focus_areas>
 
<workflow>
1.Read the modified files
2.Identify security risks
3.Provide specific remediation steps
4.Rate severity (Critical/High/Medium/Low)
</workflow>

</principle>

<principle name="task_specific"> Tailor instructions to the specific task domain. Don't create generic "helper" agents.

❌ Bad: "You are a helpful assistant that helps with code" ✅ Good: "You are a React component refactoring specialist. Analyze components for hooks best practices, performance anti-patterns, and accessibility issues." </principle> </system_prompt_guidelines>

<agent_xml_structure> Agent.md files are system prompts consumed only by Claude. Like skills, they should use pure XML structure for optimal parsing and token efficiency.

<recommended_tags> Common tags for agent structure:

• <role> - Who the agent is and what it does
• <constraints> - Hard rules (NEVER/MUST/ALWAYS)
• <focus_areas> - What to prioritize
• <workflow> - Step-by-step process
• <output_format> - How to structure deliverables
• <success_criteria> - Completion criteria
• <validation> - How to verify work

</recommended_tags>

<intelligence_rules> Simple agents (single focused task):

• Use role + constraints + workflow minimum
• Example: code-reviewer, test-runner

Medium agents (multi-step process):

• Add workflow steps, output_format, success_criteria
• Example: api-researcher, documentation-generator

Complex agents (research + generation + validation):

• Add all tags as appropriate including validation, examples
• Example: mcp-api-researcher, comprehensive-auditor

</intelligence_rules>

<critical_rule> Remove ALL markdown headings (##, ###) from agent body. Use semantic XML tags instead.

Keep markdown formatting WITHIN content (bold, italic, lists, code blocks, links).

For XML structure principles and token efficiency details, see @skills/create-agent-skills/references/use-xml-tags.md - the same principles apply to agents. </critical_rule> </agent_xml_structure>

<invocation> <automatic> Claude automatically selects agents based on the description field when it matches the current task. </automatic>

<explicit> You can explicitly invoke a agent:

> Use the code-reviewer agent to check my recent changes

> Have the test-writer agent create tests for the new API endpoints

</explicit> </invocation>

<background_execution> Agents can run in the background using the run_in_background parameter, allowing parallel execution while the main conversation continues.

<how_it_works> Starting a background agent: The Task tool accepts run_in_background: true to launch agents asynchronously:

Task tool call:
- description: "Analyze security vulnerabilities"
- prompt: "Review all authentication code for security issues..."
- subagent_type: "security-reviewer"
- run_in_background: true

The agent starts immediately and returns an agent_id for tracking. </how_it_works>

<retrieving_results> Getting results with TaskOutput: Use the TaskOutput tool to retrieve results from background agents:

TaskOutput tool call:
- task_id: "agent-12345"  # The agent_id from the Task call
- block: true            # Wait for completion (default)
- timeout: 30000         # Max wait time in ms

Parameters:

Non-blocking check: Set block: false to check status without waiting:

TaskOutput tool call:
- task_id: "agent-12345"
- block: false

Returns current status: running, completed, or the final result. </retrieving_results>

<parallel_agents> Launching multiple agents in parallel:

To maximize performance, launch multiple independent agents simultaneously:

Single message with multiple Task tool calls:
 
Task 1:
- description: "Review code quality"
- prompt: "Check code quality..."
- subagent_type: "code-reviewer"
- run_in_background: true
 
Task 2:
- description: "Run security scan"
- prompt: "Scan for vulnerabilities..."
- subagent_type: "security-scanner"
- run_in_background: true
 
Task 3:
- description: "Check test coverage"
- prompt: "Analyze test coverage..."
- subagent_type: "test-analyzer"
- run_in_background: true

Then retrieve all results:

TaskOutput calls for each agent_id

</parallel_agents>

<when_to_use_background> Use background agents for:

• Long-running analysis (security review, comprehensive code analysis)
• Multiple independent tasks that can run in parallel
• Tasks where you want to continue working while waiting
• Research tasks that may take significant time

Don't use background for:

• Quick operations (< 10 seconds)
• Tasks that depend on each other sequentially
• Tasks where immediate results are needed for next step
• Simple single-file operations

Pattern: Parallel Analysis Pipeline

1. Launch multiple analysis agents in background
2. Continue with other work or wait
3. Collect all results
4. Synthesize findings in main conversation

</when_to_use_background>

<resuming_agents> Resuming agents: Agents can be resumed using the resume parameter with their agent ID:

Task tool call:
- description: "Continue security review"
- prompt: "Please continue with the remaining files..."
- subagent_type: "security-reviewer"
- resume: "agent-12345"  # Previous agent ID

The agent continues with its full previous context preserved. </resuming_agents> </background_execution>

<management> <using_agents_command> Run /agents for an interactive interface to:

• View all available agents
• Create new agents
• Edit existing agents
• Delete custom agents

</using_agents_command>

<manual_editing> You can also edit agent files directly:

• Project: .claude/agents/agent-name.md
• User: ~/.claude/agents/agent-name.md

</manual_editing> </management>

<reference> Core references:

Agent usage and configuration: references/agents.md

• File format and configuration
• Model selection (Sonnet 4.5 + Haiku 4.5 orchestration)
• Tool security and least privilege
• Prompt caching optimization
• Background execution (run_in_background, TaskOutput, parallel agents)
• Complete examples

Writing effective prompts: references/writing-agent-prompts.md

• Core principles and XML structure
• Description field optimization for routing
• Extended thinking for complex reasoning
• Security constraints and strong modal verbs
• Success criteria definition

Advanced topics:

Evaluation and testing: references/evaluation-and-testing.md

• Evaluation metrics (task completion, tool correctness, robustness)
• Testing strategies (offline, simulation, online monitoring)
• Evaluation-driven development
• G-Eval for custom criteria

Error handling and recovery: references/error-handling-and-recovery.md

• Common failure modes and causes
• Recovery strategies (graceful degradation, retry, circuit breakers)
• Structured communication and observability
• Anti-patterns to avoid

Context management: references/context-management.md

• Memory architecture (STM, LTM, working memory)
• Context strategies (summarization, sliding window, scratchpads)
• Managing long-running tasks
• Prompt caching interaction

Orchestration patterns: references/orchestration-patterns.md

• Sequential, parallel, hierarchical, coordinator patterns
• Sonnet + Haiku orchestration for cost/performance
• Multi-agent coordination
• Pattern selection guidance

Debugging and troubleshooting: references/debugging-agents.md

• Logging, tracing, and correlation IDs
• Common failure types (hallucinations, format errors, tool misuse)
• Diagnostic procedures
• Continuous monitoring

</reference>

<success_criteria> A well-configured agent has:

• Valid YAML frontmatter (name matches file, description includes triggers)
• Clear role definition in system prompt
• Appropriate tool restrictions (least privilege)
• XML-structured system prompt with role, approach, and constraints
• Description field optimized for automatic routing
• Successfully tested on representative tasks
• Model selection appropriate for task complexity (Sonnet for reasoning, Haiku for simple tasks)

</success_criteria>