Skills vs Agents

SpecWeave extends AI coding assistants (Claude Code, Cursor, Copilot, Windsurf, and others) with two core AI components: Skills (portable instructions) and Custom Subagents (isolated workers with memory). Understanding the difference — and how they compose — is key to building effective AI-assisted workflows.

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Quick Comparison

Aspect	Skills	Custom Subagents
Activation	Natural language, /command, or CLI keyword	Explicit (Agent() call)
Context	Main conversation or forked (context: fork)	Always isolated
Memory	None (stateless)	Persistent (memory: project/user/local)
Resumable	No	Yes (by agent ID)
Background exec	No	Yes (run_in_background: true)
File	skills/name/SKILL.md	agents/name.md
Preloads skills	No	Yes (skills: field)

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Skills

Skills are markdown instructions that AI assistants follow. They can be invoked three ways: natural language (auto-activation on keywords), slash commands (sw:name) in Claude Code, or CLI keywords (name) in other AI tools. Skills can also be preloaded by subagents.

How They Work

Execution Modes

Mode	When	How
Inline	Reference knowledge (conventions, patterns)	No context: fork — enriches main conversation
Forked	Standalone tasks (grill, brainstorm)	context: fork — isolated context
Preloaded	Inside a subagent	skills: field on subagent — subagent provides isolation

Skill File Structure

# skills/pm/SKILL.md
---
description: Product Manager for spec-driven development.
context: fork
model: opus
---

# Product Manager Skill

## Workflow
[Phased domain logic, templates, validation rules...]

Example Skills

The most common entry point — starting a new feature:

"Let's build user authentication with OAuth"

Other frequently used skills:

sw:pm                 # Product management domain logic (preloaded by sw-pm subagent)
sw:architect          # Architecture domain logic (preloaded by sw-architect subagent)
sw:grill              # Code review (standalone, context: fork)
sw:brainstorm         # Multi-perspective ideation (standalone, context: fork)

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Custom Subagents

Custom subagents are isolated AI workers with their own context, persistent memory, and optional skills preloading. They run in a separate conversation, can be resumed, and support background execution.

How They Work

Key Capabilities (beyond Skills)

• Persistent memory — retains learnings across sessions (memory: project)
• Resumability — can be resumed by agent ID for multi-turn workflows
• Background execution — runs concurrently while you keep chatting
• Auto-compaction — handles context overflow at ~95% capacity
• Skills preloading — skills: field guarantees domain logic injection at startup
• Permission overrides — can restrict tool access for safety

Subagent File Structure

# agentssw-pm.md
---
name: sw-pm
description: Product Manager for writing spec.md
model: opus
memory: project
skills:
  - sw:pm
---

# Product Manager Subagent

You are a PM specializing in spec-driven development.
The sw:pm skill is preloaded with full domain logic.

SpecWeave Core Subagents

Subagent	Preloads Skill	Writes	Model
sw-pm	sw:pm	spec.md	Opus
sw-architect	sw:architect	plan.md	Opus
sw-planner	(inline BDD logic)	tasks.md	Sonnet

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The Recommended Pattern: Subagents Preloading Skills

The most powerful pattern combines both: subagents own isolation and memory, skills own domain logic.

plugins/specweave/
├── agents/
│   ├── sw-pm.md            # Subagent: memory, model, skills: [sw:pm]
│   ├── sw-architect.md     # Subagent: memory, model, skills: [sw:architect]
│   └── sw-planner.md       # Subagent: memory, model, inline BDD task logic
├── skills/
│   ├── pm/
│   │   ├── SKILL.md        # Domain logic, phases, templates
│   │   ├── phases/         # Supporting files loaded on demand
│   │   └── templates/
│   └── architect/
│       └── SKILL.md

How the increment skill orchestrates them:

// PM writes spec.md
Agent({ subagent_type: "sw:sw-pm", prompt: "Write spec for increment..." })

// Architect writes plan.md
Agent({ subagent_type: "sw:sw-architect", prompt: "Design architecture..." })

// Planner writes tasks.md
Agent({ subagent_type: "sw:sw-planner", prompt: "Generate tasks..." })

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When to Use Which

Use Skills When:

• Reference knowledge — conventions, patterns, style guides that enrich the main conversation
• Standalone tasks — one-shot work like code review (sw:grill), brainstorming, validation
• User-invocable commands — sw:pm, sw:grill, sw:brainstorm
• Repetitive instructions — the DRY principle for AI instructions

Use Custom Subagents When:

• Persistent memory needed — agent retains learnings across sessions
• Resumability needed — multi-turn workflows that may span sessions
• Background execution — run concurrently while user continues chatting
• Skills preloading — guarantee domain logic injection at startup
• Orchestrated pipelines — PM → Architect → Planner chain

Use Them Together When:

Subagents preload skills for combined benefits:

Subagent (isolation, memory, resumability)
├── Preloads: Domain skill (full logic, phases, templates)
├── Has: Persistent memory across sessions
└── Produces: Deliverable (spec.md, plan.md, tasks.md)

Decision Flowchart

Does it need persistent memory or resumability?
  YES → Custom subagent (with skills: preloading)
  NO  →
    Is it a standalone task producing output?
      YES → Skill with context: fork
      NO  →
        Is it reference knowledge for the main conversation?
          YES → Skill, NO context: fork (runs inline)
          NO  → Built-in subagent (Explore/Plan/general-purpose)

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Beyond Claude: Extending Any AI Tool

While SpecWeave's skills and subagents are most deeply integrated with Claude Code, the architecture is designed to extend any AI coding assistant:

Extension	Claude Code	Cursor / Copilot / Windsurf
Skills	Full support (auto-activate, /commands, context: fork)	Via AGENTS.md instruction files
Custom subagents	Full support (memory, resume, background)	Not available (Claude Code exclusive)
Hooks	Shell scripts on tool events	Not available
MCP servers	Full support	Varies by tool
Plugins	Bundle skills + agents + hooks	Skills only (via AGENTS.md)

See AGENTS.md in any SpecWeave project for non-Claude tool instructions.

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Skills vs MCP

MCP connects AI to data; Skills teach AI what to do with that data.

Need	Use	Example
Access a database or API	MCP	Query your PostgreSQL database
Procedure for using that data	Skill	"When querying our database, always filter by date range first"
Read Excel files	MCP	Open and parse spreadsheet data
Format Excel reports	Skill	"Format reports with these specific formulas and layouts"

Use both together: MCP for connectivity, Skills for procedural knowledge.

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Context Management

Skills: Shared or Forked

Main Context (200K tokens)
├── User conversation
├── Inline skill (loaded, 3K)       ← shared context
├── Forked skill (context: fork)    ← isolated context
└── Response

Subagents: Always Isolated

Main Context
├── User conversation
├── Agent() call
│   └── Subagent Context (separate)
│       ├── Preloaded skill instructions
│       ├── Persistent memory
│       ├── Relevant files
│       └── Generated output (spec.md, plan.md, etc.)
└── Subagent result returned

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Decision Framework Summary

Question	Skills	Custom Subagents	MCP
What does it do?	Provides instructions	Executes isolated work	Provides data access
Memory	None	Persistent across sessions	None
Context model	Shared or forked	Always isolated	Shared (tool results)
Best for	Conventions, expertise, commands	Complex workflows, pipelines	Data access, external services
AI tool support	Any (via AGENTS.md)	Claude Code only	Varies

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Further Reading

• Skills-First Architecture — Why skills are the user-facing layer
• Skills, Plugins & Marketplaces — How skills, plugins, and marketplaces relate
• Extensible Skills — Customizing skills for your project
• Claude Code Skills Docs — Official Claude Code skills documentation
• Claude Code Subagents Docs — Official custom subagents documentation

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Related Terms

• Role Orchestrator
• Hooks
• Increments