Agent Types

Choose the right agent type for each role in your team. Agent types determine what tools are available and what the agent is optimized for.

Related skills:

• Orchestrating - Primitives overview and quick reference
• Team Management - Spawning agents into teams
• Orchestration Patterns - Patterns that combine different agent types

---

Agent Type Selection Guide

Need	Agent Type	Why
Search/read code	Explore	Read-only, fast, cheap (haiku)
Architecture design	Plan	Read-only, thoughtful analysis
Run commands/git	Bash	Shell access only
Multi-step implementation	general-purpose	Full tool access
Claude Code questions	claude-code-guide	Docs and web access
Security review	sdlc:security-reviewer	Vulnerability assessment, secure coding
Code quality review	sdlc:quality-enforcer	Formatting, linting, code style
Code simplification	code-simplifier:code-simplifier	Clarity, consistency, minimalism
Architecture analysis	refactor:architect	Design review, optimization planning
Best practices research	adr:adr-researcher	Codebase analysis + web research
Deep code exploration	feature-dev:code-explorer	Trace execution paths, map architecture
Code review	feature-dev:code-reviewer	Bugs, logic errors, conventions
Source code chunk analysis	swarm:rlm-code-analyzer	Code-aware, scope context, analysis focus
Data/CSV chunk analysis	swarm:rlm-data-analyzer	Column-aware, distributions, statistics
JSON chunk analysis	swarm:rlm-json-analyzer	Schema-aware, structural patterns
General chunk-level analysis	swarm:rlm-chunk-analyzer	Haiku, fast, structured JSON output
Synthesize chunk findings	swarm:rlm-synthesizer	Sonnet, aggregation and deduplication

Key rule: Match the agent's tool access to the task requirements. Read-only agents (Explore, Plan) cannot edit or write files. Never assign them implementation work.

---

Built-in Agent Types

These are always available without plugins:

Bash

Task({
  subagent_type: "Bash",
  description: "Run git commands",
  prompt: "Check git status and show recent commits"
})

• Tools: Bash only
• Model: Inherits from parent
• Best for: Git operations, command execution, system tasks

Explore

Task({
  subagent_type: "Explore",
  description: "Find API endpoints",
  prompt: "Find all API endpoints in this codebase. Be very thorough.",
  model: "haiku"  // Fast and cheap
})

• Tools: All read-only tools (no Edit, Write, NotebookEdit, Task)
• Model: Haiku (optimized for speed)
• Best for: Codebase exploration, file searches, code understanding
• Thoroughness levels: "quick", "medium", "very thorough"

Plan

Task({
  subagent_type: "Plan",
  description: "Design auth system",
  prompt: "Create an implementation plan for adding OAuth2 authentication"
})

• Tools: All read-only tools
• Model: Inherits from parent
• Best for: Architecture planning, implementation strategies

general-purpose

Task({
  subagent_type: "general-purpose",
  description: "Research and implement",
  prompt: "Research React Query best practices and implement caching for the user API"
})

• Tools: All tools (*)
• Model: Inherits from parent
• Best for: Multi-step tasks, research + action combinations

claude-code-guide

Task({
  subagent_type: "claude-code-guide",
  description: "Help with Claude Code",
  prompt: "How do I configure MCP servers?"
})

• Tools: Read-only + WebFetch + WebSearch
• Best for: Questions about Claude Code, Agent SDK, Anthropic API

statusline-setup

Task({
  subagent_type: "statusline-setup",
  description: "Configure status line",
  prompt: "Set up a status line showing git branch and node version"
})

• Tools: Read, Edit only
• Model: Sonnet
• Best for: Configuring Claude Code status line

---

Plugin Agent Types

Installed plugins provide specialized agent types grouped by function.

Review Agents

// Security review
Task({
  subagent_type: "sdlc:security-reviewer",
  description: "Security audit",
  prompt: "Audit this PR for security vulnerabilities"
})

// Code quality
Task({
  subagent_type: "sdlc:quality-enforcer",
  description: "Quality check",
  prompt: "Check formatting, linting, and code style"
})

// Code review (bugs, logic, conventions)
Task({
  subagent_type: "feature-dev:code-reviewer",
  description: "Code review",
  prompt: "Review this code for bugs, logic errors, and adherence to project conventions"
})

// Code simplification
Task({
  subagent_type: "code-simplifier:code-simplifier",
  description: "Simplicity check",
  prompt: "Check if this implementation can be simplified"
})

// Architecture review
Task({
  subagent_type: "refactor:architect",
  description: "Architecture review",
  prompt: "Review the system architecture of the authentication module"
})

// General code review against plan and standards
Task({
  subagent_type: "superpowers:code-reviewer",
  description: "Plan compliance review",
  prompt: "Review implementation against the original plan and coding standards"
})

// SDLC compliance audit
Task({
  subagent_type: "sdlc:compliance-auditor",
  description: "Compliance audit",
  prompt: "Audit this project against all SDLC standards"
})

All review agents:

• sdlc:security-reviewer - Vulnerability assessment, supply chain security, secure coding
• sdlc:quality-enforcer - Formatting, linting, error handling, documentation standards
• sdlc:compliance-auditor - Full SDLC compliance audit
• feature-dev:code-reviewer - Bugs, logic errors, security, code quality, conventions
• code-simplifier:code-simplifier - Clarity, consistency, minimalism
• refactor:architect - Architectural analysis, optimization planning
• superpowers:code-reviewer - Review against plan and coding standards
• human-voice:voice-reviewer - Content voice and tone review

Research Agents

// Best practices and architectural research
Task({
  subagent_type: "adr:adr-researcher",
  description: "Research auth best practices",
  prompt: "Research current best practices for JWT authentication"
})

// Deep codebase exploration
Task({
  subagent_type: "feature-dev:code-explorer",
  description: "Analyze auth module",
  prompt: "Trace execution paths and map the architecture of the authentication module"
})

// Feature architecture design
Task({
  subagent_type: "feature-dev:code-architect",
  description: "Design feature architecture",
  prompt: "Design the architecture for adding OAuth2 authentication based on existing patterns"
})

// Memory/knowledge search
Task({
  subagent_type: "mnemonic:mnemonic-search-subcall",
  description: "Search prior knowledge",
  prompt: "Search for prior decisions and learnings about authentication"
})

All research agents:

• adr:adr-researcher - Codebase analysis + web search for best practices
• feature-dev:code-explorer - Trace execution paths, map architecture, understand dependencies
• feature-dev:code-architect - Design feature architectures based on existing patterns
• mnemonic:mnemonic-search-subcall - Iterative search across stored knowledge

Refactoring Agents

// Architecture analysis and planning
Task({
  subagent_type: "refactor:architect",
  description: "Plan refactoring",
  prompt: "Analyze code architecture and create a prioritized optimization plan"
})

// Implementation
Task({
  subagent_type: "refactor:code",
  description: "Implement refactoring",
  prompt: "Implement the architectural optimizations while preserving functionality"
})

// Test coverage
Task({
  subagent_type: "refactor:test",
  description: "Refactoring test coverage",
  prompt: "Analyze coverage, add missing tests, ensure all tests pass"
})

CI/CD and Infrastructure Agents

// CI pipeline design
Task({
  subagent_type: "sdlc:ci-architect",
  description: "Design CI pipeline",
  prompt: "Configure GitHub Actions workflow for this project"
})

Bug Reproduction and Testing

// Bug reproduction (full tool access)
Task({
  subagent_type: "general-purpose",
  description: "Validate bug",
  prompt: "Reproduce and validate this reported bug: [description]"
})

// Test generation
Task({
  subagent_type: "auto-harness:test-generator",
  description: "Generate tests",
  prompt: "Generate comprehensive test definitions for this component"
})

RLM Agents

Content-aware chunk analyzers — the Team Lead selects the analyst based on detected content type.

Single-file mode: One analyst type per session (determined by content type). Multi-file mode: Different analyst types run simultaneously when a directory contains mixed content types. 1 analyst per task (fresh context each), distributed proportionally across content types, staged spawning in batches of ~15 for large workloads. See Multi-File Directory Analysis.

IMPORTANT: In actual RLM workflows, spawn these as teammates (with team_name + name) so they communicate via SendMessage instead of dumping results into the leader's context. In multi-file mode, analysts write findings to task descriptions via TaskUpdate and send only one-line summaries to team-lead. See RLM Pattern for the full team lifecycle. The examples below show the subagent_type syntax only:

// Source code analysis (code-aware boundaries)
Task({
  subagent_type: "swarm:rlm-code-analyzer",
  description: "Analyze code chunk",
  prompt: "Query: Review for security issues\nFile: /tmp/rlm-chunks/chunk-01.py\nLanguage: python\nAnalysis focus: security\nThis is chunk 1 of 10."
})

// CSV data analysis (header-preserving chunks)
Task({
  subagent_type: "swarm:rlm-data-analyzer",
  description: "Analyze data chunk",
  prompt: "Query: Analyze distribution by region\nFile: /tmp/rlm-chunks/chunk-03.csv\nThis is chunk 3 of 9.\nKey columns: region, plan, mrr"
})

// JSON analysis (schema-aware chunks)
Task({
  subagent_type: "swarm:rlm-json-analyzer",
  description: "Analyze JSON chunk",
  prompt: "Query: Report schema patterns and anomalies\nFile: /tmp/rlm-chunks/chunk-02.jsonl\nFormat: jsonl\nThis is chunk 2 of 8."
})

// General chunk analysis (logs, prose, config, other)
Task({
  subagent_type: "swarm:rlm-chunk-analyzer",
  description: "Analyze log chunk",
  prompt: "Query: What errors occurred?\nFile: /var/log/app/server.log\nStart line: 1\nEnd line: 200\nThis is chunk 1 of 10."
})

// Synthesis (higher quality)
Task({
  subagent_type: "swarm:rlm-synthesizer",
  description: "Synthesize findings",
  prompt: "Original query: What errors occurred?\n\nFindings:\n[...findings JSON...]"
})

RLM agents (defined by this plugin):

• swarm:rlm-code-analyzer — Haiku model, code-aware chunk analysis with scope context and analysis focus
• swarm:rlm-data-analyzer — Haiku model, CSV/TSV chunk analysis with column distributions and statistics
• swarm:rlm-json-analyzer — Haiku model, JSON/JSONL chunk analysis with schema patterns and field distributions
• swarm:rlm-chunk-analyzer — Haiku model, general-purpose chunk analysis for logs, prose, config, markup
• swarm:rlm-synthesizer — Sonnet model, aggregates findings from multiple chunk analyses into coherent reports

Do NOT override analyst models. Do not pass model: "sonnet" or model: "opus" in the Task tool call when spawning analyst agents. The agent frontmatter defines model: haiku because structured counting, frequency analysis, and JSON output are well within Haiku's capability. Overriding to a more expensive model burns 10-50x the cost per chunk with no material quality gain. Only the synthesizer uses Sonnet. Leave the model parameter unset — let the agent definition's default apply.

---

Agent Type Naming Convention

Plugin agents use the format: {plugin}:{agent-name} or {plugin}:{category}:{agent-name}

Examples:

• sdlc:security-reviewer
• feature-dev:code-reviewer
• refactor:architect
• code-simplifier:code-simplifier

Built-in agents use simple names: Bash, Explore, Plan, general-purpose, claude-code-guide, statusline-setup.

Swarm plugin agents follow the same convention:

• swarm:rlm-code-analyzer
• swarm:rlm-data-analyzer
• swarm:rlm-json-analyzer
• swarm:rlm-chunk-analyzer
• swarm:rlm-synthesizer