nw-agent-creation-workflow
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
Best use case
nw-agent-creation-workflow is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
Teams using nw-agent-creation-workflow should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nw-agent-creation-workflow/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nw-agent-creation-workflow Compares
| Feature / Agent | nw-agent-creation-workflow | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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
| Phase | Gate | Blocks |
|-------|------|--------|
| ANALYZE | Single responsibility, no overlap | DESIGN |
| DESIGN | Architecture fits size target | CREATE |
| CREATE | File created, under 300 lines | VALIDATE |
| VALIDATE | 14-point checklist passes | REFINE/Deploy |
| REFINE | Edge cases handled, within target | Deploy |
## 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 criteriaRelated Skills
nw-review-workflow
Detailed review process, v2 validation checklist, and scoring methodology for agent definition reviews
nw-discovery-workflow
4-phase discovery workflow with decision gates, phase transitions, success metrics, and state tracking
nw-command-optimization-workflow
Step-by-step workflow for converting bloated command files to lean declarative definitions
nw-jtbd-workflow-selection
JTBD workflow classification and routing - ODI two-phase framework, five job types with workflow sequences, baseline type selection, workflow anti-patterns, and common recipes
nw-ux-web-patterns
Web UI design patterns for product owners. Load when designing web application interfaces, writing web-specific acceptance criteria, or evaluating responsive designs.
nw-ux-tui-patterns
Terminal UI and CLI design patterns for product owners. Load when designing command-line tools, interactive terminal applications, or writing CLI-specific acceptance criteria.
nw-ux-principles
Core UX principles for product owners. Load when evaluating interface designs, writing acceptance criteria with UX requirements, or reviewing wireframes and mockups.
nw-ux-emotional-design
Emotional design and delight patterns for product owners. Load when designing onboarding flows, empty states, first-run experiences, or evaluating the emotional quality of an interface.
nw-ux-desktop-patterns
Desktop application UI patterns for product owners. Load when designing native or cross-platform desktop applications, writing desktop-specific acceptance criteria, or evaluating panel layouts and keyboard workflows.
nw-user-story-mapping
User story mapping for backlog management and outcome-based prioritization. Load during Phase 2.5 (User Story Mapping) to produce story-map.md and prioritization.md.
nw-tr-review-criteria
Review dimensions and scoring for root cause analysis quality assessment
nw-tlaplus-verification
TLA+ formal verification for design correctness and PBT pipeline integration