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Add Agent

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jmagly
add-agent

Scaffold a new agent definition file inside an existing addon or framework

Overview

Publisherjmagly
Repositoryaiwg
Skill nameadd-agent
Stars
225
Forks
34
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jmagly on GitHub. Read the source before you install it.

Installation

Install the Add Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/add-agent
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/aiwg-utils/skills/add-agent .claude/skills/add-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Add Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Add Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Add Agent

Scaffold a new agent definition file inside an existing addon or framework.

Triggers

Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

  • "I need a new agent" → scaffold agent in specified target
  • "build me an agent" → scaffold agent with interactive guidance
  • "write an agent for X" → scaffold agent named after the described role
  • "agent for " → derive name from role, prompt for target

Trigger Patterns Reference

PatternExampleAction
Named add"add agent security-auditor --to sdlc-complete"Scaffold directly
Role description"create an agent that reviews PRs"Derive name, scaffold
Interactive"add agent --interactive"Guided mode, ask for name/target/template
Target omitted"add agent code-reviewer"Ask which addon or framework

Process

1. Parse Arguments

Extract from $ARGUMENTS:

  • <name> — kebab-case agent name (required)
  • --to <target> — addon or framework directory name (required)
  • --template <type> — one of simple (default), complex, orchestrator, validator
  • --interactive — enable guided design questions

If either <name> or --to is missing, ask before proceeding.

2. Validate Target

Confirm the target addon or framework exists:

bash
# Check addons
ls agentic/code/addons/<target>/

# Check frameworks
ls agentic/code/frameworks/<target>/

If not found, report available targets and stop.

3. Interactive Design (if --interactive)

Ask the following before generating:

  1. Role: What is this agent's primary role?
  2. Expertise: What domains or technologies does it specialize in?
  3. Responsibilities: What are its 3-5 key responsibilities?
  4. Tools needed: Which tools does it require? (Read, Write, Bash, Grep, WebFetch, Task)
  5. Model tier: haiku (fast/utility), sonnet (balanced), opus (reasoning-heavy)
  6. Output format: What structured output should it produce?

4. Select Template

TemplateUse WhenDefault ModelTools
simpleSingle-purpose utility agenthaikuRead, Write
complexDomain expert, reviewer, analystsonnetRead, Write, Grep
orchestratorCoordinates other agentsopusTask
validatorRead-only quality/compliance gatehaikuRead, Grep

5. Run Scaffolding

bash
aiwg add-agent <name> --to <target> --template <type>

This generates <target>/agents/<name>.md with the appropriate frontmatter and section stubs.

6. Customize the Generated File

The generated agent file requires population of these sections:

markdown
---
name: <name>
description: <one-sentence purpose>
model: <claude-haiku-4-5|claude-sonnet-5|claude-opus-5>  # Pin specific variant; bare aliases (`sonnet`) inherit the parent session's 1M-context attributes and hit the usage-credit gate on subagent dispatch. See #1442.
tools: <comma-separated tool list>
category: <phase or domain>
---

# Agent Title

[Role description]

## Expertise

[Domain knowledge and skills]

## Responsibilities

[Numbered list of what this agent does]

## Workflow

[How it approaches tasks — research, draft, validate, output]

## Output Format

[Expected structure of deliverables]

7. Validate Against 10 Golden Rules

After creation, run the agent linter:

bash
aiwg lint agents <target>/agents/<name>.md --verbose

Key checks:

  • Single responsibility (no "and" overload in description)
  • Minimal tools (0-3 tools, each justified)
  • Explicit I/O contract defined
  • Model tier matches task complexity

8. Update Manifest

The CLI tool updates <target>/manifest.json automatically. Verify:

json
{
  "agents": ["existing-agent", "<name>"]
}

Generated Structure

<target>/agents/<name>.md

Manifest updated: <target>/manifest.json

Output Format

Agent Created: <name>
─────────────────────
Location: <target>/agents/<name>.md
Template:  <type>
Model:     <model>

Created:
  ✓ <target>/agents/<name>.md
  ✓ manifest.json updated

Validation:
  ✓ Single responsibility
  ✓ Tool count within limit
  ✓ I/O contract defined

Next Steps:
  1. Edit Expertise section with domain knowledge
  2. Define Responsibilities (3-5 items)
  3. Specify Output Format structure
  4. Run: aiwg lint agents <target>/agents/<name>.md

Examples

Example 1: Simple utility agent

User: "add agent dependency-auditor --to aiwg-utils"

Action:

bash
aiwg add-agent dependency-auditor --to aiwg-utils

Result: agentic/code/addons/aiwg-utils/agents/dependency-auditor.md scaffolded with simple template (haiku, Read + Grep tools).

Example 2: Complex domain expert

User: "create a threat modeling agent for sdlc-complete"

Extraction: name=threat-modeler, target=sdlc-complete, template=complex

Action:

bash
aiwg add-agent threat-modeler --to sdlc-complete --template complex

Result: agentic/code/frameworks/sdlc-complete/agents/threat-modeler.md scaffolded with sonnet model, Read/Write/Grep tools.

Example 3: Interactive guided creation

User: "add agent --interactive --to sdlc-complete"

Process: Guided questions gather role, expertise, responsibilities, tools, and model tier before scaffolding.

References

  • @$AIWG_ROOT/agentic/code/addons/aiwg-utils/skills/devkit-create-agent/SKILL.md — Devkit equivalent (interactive design)
  • @$AIWG_ROOT/src/cli/handlers/scaffolding.ts — CLI handler implementation
  • @$AIWG_ROOT/docs/cli-reference.md — Full CLI reference
  • @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/agents/ — Example agent definitions

Frequently asked questions

What does the Add Agent AI skill do?

Scaffold a new agent definition file inside an existing addon or framework

Why use Add Agent on TypingMind?

Because you install it once and use it with any model. Add Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Add Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/add-agent. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Add Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Add Agent?

As many as you like. As long as a model supports skills, you can use Add Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Add Agent AI skill free?

Yes. It is published on GitHub by jmagly under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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