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Agent Types

Agent Types

Section titled “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:

Agent Type Selection Guide

Section titled “Agent Type Selection Guide”
NeedAgent TypeWhy
Search/read codeExploreRead-only, fast, cheap (haiku)
Architecture designPlanRead-only, thoughtful analysis
Run commands/gitBashShell access only
Multi-step implementationgeneral-purposeFull tool access
Claude Code questionsclaude-code-guideDocs and web access
Security reviewsdlc:security-reviewerVulnerability assessment, secure coding
Code quality reviewsdlc:quality-enforcerFormatting, linting, code style
Code simplificationcode-simplifier:code-simplifierClarity, consistency, minimalism
Architecture analysisrefactor:architectDesign review, optimization planning
Best practices researchadr:adr-researcherCodebase analysis + web research
Deep code explorationfeature-dev:code-explorerTrace execution paths, map architecture
Code reviewfeature-dev:code-reviewerBugs, logic errors, conventions
Source code chunk analysisswarm:rlm-code-analyzerCode-aware, scope context, analysis focus
Data/CSV chunk analysisswarm:rlm-data-analyzerColumn-aware, distributions, statistics
JSON chunk analysisswarm:rlm-json-analyzerSchema-aware, structural patterns
General chunk-level analysisswarm:rlm-chunk-analyzerHaiku, fast, structured JSON output
Synthesize chunk findingsswarm:rlm-synthesizerSonnet, 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

Section titled “Built-in Agent Types”

These are always available without plugins:

Bash

Section titled “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

Section titled “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

Section titled “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

Section titled “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

Section titled “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

Section titled “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

Section titled “Plugin Agent Types”

Installed plugins provide specialized agent types grouped by function.

Review Agents

Section titled “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

Section titled “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

Section titled “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

Section titled “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

Section titled “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

Section titled “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

Section titled “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