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.
git clone --depth 1 https://github.com/DauQuangThanh/hanoi-rainbowWrote 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/dauquangthanh/hanoi-rainbow/standardize)<a href="https://agentmods.dev/commands/dauquangthanh/hanoi-rainbow/standardize"><img src="https://agentmods.dev/badge/commands/dauquangthanh/hanoi-rainbow/standardize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/dauquangthanh/hanoi-rainbow/standardize"><img src="https://agentmods.dev/badge/commands/dauquangthanh/hanoi-rainbow/standardize.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00018 | $0.03510 |
| Opus 5 | $0.00009 | $0.01755 |
| Sonnet 5 | $0.00004 | $0.00702 |
| Haiku 4.5 | $0.00002 | $0.00351 |
Grade A, and why
standardize 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- rainbow.standardize — 97% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 416 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
IMPORTANT: Automatically generate a 'docs:' prefixed git commit message (e.g., 'docs: add coding standards and conventions') and commit upon completion.
-
Setup: Run
{SCRIPT}from repo root and parse JSON for STANDARDS_DOC, DOCS_DIR, ARCH_DOC, CONSTITUTION. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot"). -
Load context: Read
memory/ground-rules.md,docs/architecture.md(if exists), and all feature specifications fromspecs/*/spec.md. Load STANDARDS_DOC template (already copied to docs/). Incorporate the essential rules for maximizing source code readability, simplicity and long-term maintainability. -
Execute standardization workflow: Follow the structure in STANDARDS_DOC template to:
- Define UI naming conventions (MANDATORY for frontend projects)
- Establish code naming conventions
- Document file and directory structure standards
- Define API design standards
- Establish database naming conventions
- Document testing standards
- Define Git workflow and commit message conventions
- Establish documentation standards
- Provide a concise, high-signal standards guide for AI agents to ensure consistent, maintainable, and secure code. Exclude overly detailed information and specific examples
-
Stop and report: Command ends after standards document completion. Report STANDARDS_DOC path and generated artifacts.
Phases
Phase 0: Standards Analysis & Best Practices Research
- Analyze project context:
- Read architecture.md to understand technology stack
- Detect UI layer presence: Check if project has frontend/UI components
- Look for: React, Vue, Angular, HTML, CSS, mobile frameworks (React Native, Flutter, SwiftUI)
- Check for: UI mockups, design specifications, frontend directories
- Determine: Frontend project, backend-only, or full-stack
- Read ground-rules.md for existing constraints
- Read feature specs to identify naming patterns
- Identify programming languages and frameworks in use
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.
- 10d ago First seen · 416 lines · 18 tokens per session scan A 2498c5e685a4
standardize is a command published in the GitHub repository DauQuangThanh/hanoi-rainbow (16 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 3,510 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.
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Command description: Run implement and QC in a continuous loop. Argument hint: [optional: feature directory or branch name] Command category: orchestration Prerequisites: spec, plan, tasks.
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Command description: Create an implementation plan from the current feature specification. Argument hint: [optional: planning constraints or focus areas] Command category: feature-delivery Prerequisites: spec.
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