Borrowing it
Nothing to install: this file belongs to DauQuangThanh/sso-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DauQuangThanh/sso-mcp-server/main/.claude/commands/rainbow.standardize.mdgit clone --depth 1 https://github.com/DauQuangThanh/sso-mcp-serverWrote 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/sso-mcp-server/rainbow.standardize)<a href="https://agentmods.dev/commands/dauquangthanh/sso-mcp-server/rainbow.standardize"><img src="https://agentmods.dev/badge/commands/dauquangthanh/sso-mcp-server/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/sso-mcp-server/rainbow.standardize"><img src="https://agentmods.dev/badge/commands/dauquangthanh/sso-mcp-server/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 | $0.03408 |
| Opus 5 | $0.00009 | $0.01704 |
| Sonnet 5 | $0.00004 | $0.00682 |
| Haiku 4.5 | $0.00002 | $0.00341 |
Grade A, and why
rainbow.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 9d 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.
This is a copy
97% identical to standardize — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 409 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
.rainbow/scripts/bash/setup-standardize.sh --jsonfrom 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/). -
Execute standardization workflow: Follow the structure in STANDARDS_DOC template to:
- Define UI naming conventions (MANDATORY)
- 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
-
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
-
Research best practices for each technology:
- UI naming conventions for the UI framework (React, Vue, Angular, etc.)
- Code naming conventions for the backend language (Python, Java, TypeScript, etc.)
- Database naming conventions for the database system
- API design standards (REST, GraphQL, gRPC, etc.)
- Testing conventions for the test frameworks
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.
- 9d ago First seen · 409 lines · 18 tokens per session scan A 8f38a19a01c6
rainbow.standardize is a command published in the GitHub repository DauQuangThanh/sso-mcp-server (0 stars, last pushed 8mo ago), licensed MIT. It adds 18 tokens to every session and 3,408 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to standardize, differing in 19 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.