Nw Agent Creation Workflow logo

Nw Agent Creation Workflow

Organization
nWave-ai
nw-agent-creation-workflow

Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-agent-creation-workflow
Stars
616
Forks
65
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 nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Agent Creation Workflow 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/nWave-ai/nWave/tree/main/nWave/skills/nw-agent-creation-workflow
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-agent-creation-workflow .claude/skills/nw-agent-creation-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Agent Creation Workflow 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 Nw Agent Creation Workflow 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 Nw Agent Creation Workflow 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.

Agent Creation Workflow

Overview

Create agents through 5 phases: ANALYZE -> DESIGN -> CREATE -> VALIDATE -> REFINE. Each phase has clear inputs, outputs, and quality gates. Follow "start minimal, add based on failure."

Phase 1: ANALYZE

Goal: Understand requirements and determine agent architecture.

Inputs: User requirements, use case description, existing codebase context.

Steps:

  1. Identify single clear responsibility
  2. Determine new agent or modification of existing
  3. Check overlap with existing agents (avoid duplication)
  4. Classify agent type:
    • Specialist: Single-domain expert (most common)
    • Reviewer: Validates outputs from another agent (Reflection pattern)
    • Orchestrator: Coordinates multiple agents
  5. Identify required tools (start with Read, Glob, Grep -- add only what's needed)
  6. Determine if Skills needed (domain knowledge > 50 lines)

Gate: Single responsibility identified. Agent type classified. No overlap.

Output: Requirements summary with agent type, tools list, skill needs.

Phase 2: DESIGN

Goal: Design agent architecture and structure.

Inputs: Requirements summary from Phase 1.

Steps:

  1. Select design pattern (load design-patterns skill)
  2. Define role and goal (1-2 sentences each)
  3. Identify core principles that DIVERGE from Claude defaults:
    • What must this agent do differently than Claude naturally would?
    • Domain-specific methodology steps
    • Non-obvious constraints | Project-specific conventions
  4. Design workflow (3-7 phases)
  5. Plan Skills extraction: domain knowledge -> separate Skill | Testing/validation -> separate Skill | Keep workflow and principles in core agent
  6. Design Skill Loading Strategy (required for 3+ skills):
    • Map each skill to the workflow phase where it's needed
    • Create a loading table: Phase → Skill → Trigger condition
    • Add explicit Load: skill-name directives in each workflow phase
    • Document path: ~/.claude/skills/nw-{skill-name}/SKILL.md (installed) or nWave/skills/nw-{skill-name}/SKILL.md (repo)
    • Note: skills: in frontmatter is declarative only — Claude Code does NOT auto-load skill files. The agent must use Read tool to load them, triggered by Load: directives in workflow text.
  7. Draft frontmatter:
    yaml
    ---
    name: {kebab-case-id}
    description: Use for {domain}. {When to delegate.}
    model: inherit
    tools: [{minimum tools needed}]
    maxTurns: 30
    skills:
      - nw-{skill-name}
    ---

Gate: Design fits ~200-300 lines (core) + Skills. Pattern selected. Frontmatter drafted.

Output: Agent architecture document (working notes, not deliverable).

Phase 3: CREATE

Goal: Write agent definition file and Skills.

Inputs: Design from Phase 2.

Steps:

  1. Create agent .md file:

    • YAML frontmatter (name, description, tools, model, maxTurns, skills)
    • Role + Goal paragraph
    • Core Principles (divergences only, 3-8 items)
    • Workflow phases
    • Critical Rules (3-5, where violation causes real harm)
    • Examples (3-5 canonical cases)
    • Subagent mode instructions
    • Constraints (what agent does NOT do)
  2. Create Skill files if needed: each in nWave/skills/{agent-name}/ | YAML frontmatter with name and description | Focused content, 100-250 lines each

  3. Measure: wc -l. Target: under 300 lines.

  4. Add Skill Loading Strategy section (required for agents with 3+ skills):

    markdown
    ## Skill Loading Strategy
    
    Load on-demand by phase, not all at once:
    
    | Phase | Load | Trigger |
    |-------|------|---------|
    | 1 Phase Name | `skill-name` | Always — core methodology |
    | 2 Phase Name | `other-skill` | When condition is met |
    
    Skills path: `~/.claude/skills/nw-{skill-name}/SKILL.md` (installed) or `nWave/skills/nw-{skill-name}/SKILL.md` (repo)
  5. Add Load: directives at the start of each workflow phase referencing the applicable skills

  6. Verify: every skill in frontmatter skills: has at least one Load: directive in the workflow text. Orphan skills (declared but never loaded) are a bug.

Gate: Agent file created. Under 300 lines. Skills created if needed. Skill Loading Strategy present for 3+ skills.

Output: Agent .md file + Skill files.

Phase 4: VALIDATE

Goal: Verify agent meets quality standards.

Steps:

  1. Run 14-point validation checklist:
    • Uses official YAML frontmatter format
    • Total definition under 400 lines (domain knowledge in Skills)
    • Only specifies behaviors diverging from Claude defaults
    • No aggressive signaling language
    • 3-5 canonical examples for critical behaviors
    • Tools restricted to minimum needed
    • maxTurns set in frontmatter
    • Safety via platform features, not prose
    • Instructions phrased affirmatively
    • Consistent terminology throughout
    • Description clearly states delegation criteria
  2. Check anti-patterns: no monolithic sections (>50 lines without structure) | No duplicated Claude defaults | No embedded safety frameworks | No aggressive language
  3. Test with representative inputs (Layer 1 testing)

Gate: All 14 items pass. No anti-patterns.

Output: Validation report (pass/fail per item).

Phase 5: REFINE

Goal: Iteratively improve based on testing feedback.

Steps:

  1. Address validation failures
  2. Test with edge cases
  3. Add instructions ONLY for observed failure modes: wrong decision -> add rule/example | Missed step -> clarify workflow | Over-generated -> add constraint
  4. Re-measure: wc -l. If approaching 400 lines, extract to Skills.
  5. Re-validate with 14-point checklist.

Gate: All validation passes. Line count within target. Edge cases handled.

Output: Final agent definition, ready for installation.

Quality Gates Summary

PhaseGateBlocks
ANALYZESingle responsibility, no overlapDESIGN
DESIGNArchitecture fits size targetCREATE
CREATEFile created, under 300 linesVALIDATE
VALIDATE14-point checklist passesREFINE/Deploy
REFINEEdge cases handled, within targetDeploy

Naming Conventions

  • Agent files: nw-{name}.md in nWave/agents/
  • Skill files: {skill-name}.md in nWave/skills/{agent-name}/
  • Reviewer agents: nw-{name}-reviewer.md
  • Agent names in frontmatter: nw-{name} (kebab-case with nw- prefix)

Reviewer Agent Creation (Special Case)

Reviewer agents pair with a primary agent and use the Reflection pattern:

  1. Set model: haiku in frontmatter (cost-efficient review)
  2. Use same tools as primary agent (no Write/Edit -- reviewers don't modify)
  3. Define structured critique output format (YAML)
  4. Include max 2 review iterations
  5. Define clear approval/rejection criteria

Frequently asked questions

What does the Nw Agent Creation Workflow AI skill do?

Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement

Why use Nw Agent Creation Workflow on TypingMind?

Because you install it once and use it with any model. Nw Agent Creation Workflow 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 Nw Agent Creation Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-agent-creation-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Agent Creation Workflow?

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 Nw Agent Creation Workflow?

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

Is the Nw Agent Creation Workflow AI skill free?

Yes. It is published on GitHub by nWave-ai 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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