Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/arsxxi/iterative-dev-workflow/phase-2-step-4git clone --depth 1 https://github.com/Arsxxi/iterative-dev-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/arsxxi/iterative-dev-workflow/phase-2-step-4)<a href="https://agentmods.dev/commands/arsxxi/iterative-dev-workflow/phase-2-step-4"><img src="https://agentmods.dev/badge/commands/arsxxi/iterative-dev-workflow/phase-2-step-4.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00011 | $0.00752 |
| Opus 5 | $0.00005 | $0.00376 |
| Sonnet 5 | $0.00002 | $0.00150 |
| Haiku 4.5 | $0.00001 | $0.00075 |
Grade A, and why
phase-2-step-4 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arguments
Kilo Code does not substitute $ARGUMENTS. Wherever this command refers to
$ARGUMENTS, read it as the text I typed after the slash command in this message.
Expected: project name
If I typed nothing after the command, do not guess and do not invent a value: ask me
for it with the question tool, then continue from there.
Step 0 — find which project this is for
Before anything else, figure out which project this command applies to:
- If you were given a project name after the slash (e.g.
/phase-2-step-4 my-project), use it. - Otherwise, look at the subfolders under
.workflow/:- Exactly one folder → use it. Tell the user which project you picked (e.g. "Working on
my-project"), so it's never a silent guess. - Two or more folders → ask the user once which project this is for. To make the question
useful, show the first line of each project's
00-context.md, not just the folder name. - No folders → stop and tell the user to run
/kickofffirst. Don't proceed.
- Exactly one folder → use it. Tell the user which project you picked (e.g. "Working on
Phase 2 Step 4: High-Fidelity Design
Purpose
Create High-Level Visualizations for the Component Architecture of the chosen design, using Mermaid.js syntax.
Steps
-
Check for the previous result from Step 3 (Quality Attribute). If it's empty or not generated, DO NOT PROCEED TO EXECUTE THIS STEP — remind the user.
-
Remind the user to double-check Step 3's result before proceeding. Present this to the user:
Step 3 gave you the chosen design according to the Quality Attributes you designed previously. Make sure to double-check the results from Step 3 and do your own assessment of the results given by the AI agent in Step 3. Make sure you select the correct design, not just bias from the AI's results.
-
Ask the user to explain their chosen design. Present this to the user:
Briefly explain the design you've selected from the previous phase, and explain from your opinion why you selected that design. Or if you have your own thoughts/modification, let the AI agent know here.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 79 lines · 11 tokens per session scan A b323e730d2cd
phase-2-step-4 is a command published in the GitHub repository Arsxxi/iterative-dev-workflow (2 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 752 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
report
Generate a publication-grade HTML report from a markdown source using the TRAID Design System catalog (5 templates). Agy matches content to template, you confirm, agy produces final branded HTML.
design-review
Run a UX/visual design audit of a URL using Antigravity (agy). Captures desktop + mobile screenshots, scores 10 dimensions (hierarchy, typography, color, spacing, a11y, etc.), benchmarks against industry. Saves to docs/agy/design-reviews/.
verify
Fidelity check — rebuild a page from the extracted tokens, pixel-diff it against the live site, and score how faithfully the tokens capture the design.
design-chatbot
Design a complete chatbot or conversational assistant UI — dialog flows, message bubbles, quick replies, typing indicators, error states, and accessibility.
design-system
Generate design tokens, theme configuration, or extract a design system from existing code or Figma files.
figma
Convert a Figma design to production code. Provide a Figma URL or select a node in Figma desktop.