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.analyze.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.analyze)<a href="https://agentmods.dev/commands/dauquangthanh/sso-mcp-server/rainbow.analyze"><img src="https://agentmods.dev/badge/commands/dauquangthanh/sso-mcp-server/rainbow.analyze/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.analyze"><img src="https://agentmods.dev/badge/commands/dauquangthanh/sso-mcp-server/rainbow.analyze.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 | $0.00026 | $0.01659 |
| Opus 5 | $0.00013 | $0.00830 |
| Sonnet 5 | $0.00005 | $0.00332 |
| Haiku 4.5 | $0.00003 | $0.00166 |
Grade A, and why
rainbow.analyze 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.
This is a copy
97% identical to analyze — 16 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 — 204 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).
Goal
Identify inconsistencies, duplications, ambiguities, and underspecified items across the three core artifacts (spec.md, design.md, tasks.md) before implementation. This command MUST run only after /rainbow.taskify has successfully produced a complete tasks.md.
Operating Constraints
STRICTLY READ-ONLY: Do not modify any files. Output a structured analysis report. Offer an optional remediation plan (user must explicitly approve before any follow-up editing commands would be invoked manually).
Ground Rules Authority: The project ground rules (memory/ground-rules.md) are non-negotiable within this analysis scope. Ground rules conflicts are automatically CRITICAL and require adjustment of the spec, plan, or tasks—not dilution, reinterpretation, or silent ignoring of the principle. If a principle itself needs to change, that must occur in a separate, explicit ground rules update outside /rainbow.analyze.
Execution Steps
IMPORTANT: Automatically generate a 'docs:' prefixed git commit message (e.g., 'docs: add analysis report for feature-name') and commit the analysis report upon completion.
1. Initialize Analysis Context
Run .rainbow/scripts/bash/check-prerequisites.sh --json --require-tasks --include-tasks once from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS. Derive absolute paths:
- SPEC = FEATURE_DIR/spec.md
- PLAN = FEATURE_DIR/design.md
- TASKS = FEATURE_DIR/tasks.md
Abort with an error message if any required file is missing (instruct the user to run missing prerequisite command). 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").
2. Load Artifacts (Progressive Disclosure)
Load only the minimal necessary context from each artifact:
From spec.md:
- Overview/Context
- Functional Requirements
- Non-Functional Requirements
- User Stories
- Edge Cases (if present)
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 · 204 lines · 26 tokens per session scan A 642b5c524e84
rainbow.analyze is a command published in the GitHub repository DauQuangThanh/sso-mcp-server (0 stars, last pushed 8mo ago), licensed MIT. It adds 26 tokens to every session and 1,659 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 analyze, differing in 16 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.