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/design)<a href="https://agentmods.dev/commands/dauquangthanh/hanoi-rainbow/design"><img src="https://agentmods.dev/badge/commands/dauquangthanh/hanoi-rainbow/design/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/design"><img src="https://agentmods.dev/badge/commands/dauquangthanh/hanoi-rainbow/design.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 | $0.00014 | $0.01054 |
| Opus 5 | $0.00007 | $0.00527 |
| Sonnet 5 | $0.00003 | $0.00211 |
| Haiku 4.5 | $0.00001 | $0.00105 |
Grade A, and why
design 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.design — 89% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 110 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 implementation plan for feature-name') and commit design.md, research.md, data-model.md, and contracts/ upon completion.
-
Setup: Run
{SCRIPT}from repo root and parse JSON for FEATURE_SPEC, FEATURE_DESIGN, SPECS_DIR, BRANCH. 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 FEATURE_SPEC,
memory/ground-rules.md, anddocs/architecture.md(if it exists). Load FEATURE_DESIGN template (already copied). Adhere to the principles for maximizing system clarity, structural simplicity, and long-term maintainability. -
Execute plan workflow: Follow the structure in FEATURE_DESIGN template to:
- Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
- Fill Ground-rules Check section from ground-rules
- Align with architecture decisions from architecture.md (if available)
- Evaluate gates (ERROR if violations unjustified)
- Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
- Phase 1: Generate data-model.md, contracts/, quickstart.md
- Phase 1: Update agent context by running the agent script
- Re-evaluate Ground-rules Check post-design
-
Stop and report: Command ends after Phase 2 planning. Report branch, FEATURE_DESIGN path, and generated artifacts.
Phases
Phase 0: Outline & Research
-
Extract unknowns from Technical Context above:
- For each NEEDS CLARIFICATION → research task
- For each dependency → best practices task
- For each integration → patterns task
- Review architecture.md (if exists) for relevant architectural decisions and patterns
-
Generate and dispatch research agents:
For each unknown in Technical Context: Task: "Research {unknown} for {feature context}" For each technology choice: Task: "Find best practices for {tech} in {domain}" If architecture.md exists: Review: Architectural patterns, ADRs, and quality strategies relevant to this feature
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 · 110 lines · 14 tokens per session scan A 6da9c0d9cb22
design is a command published in the GitHub repository DauQuangThanh/hanoi-rainbow (16 stars, last pushed 7mo ago), licensed MIT. It adds 14 tokens to every session and 1,054 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
forge
Codev uses forge concept commands to interact with your repository hosting platform. By default, all concepts use the GitHub CLI (gh). Projects using GitLab, Gitea, or other forges can override these commands.
team
The team command manages team members and messages for your Codev project. Team data is stored in codev/team/ and displayed in the Tower dashboard.
overview
Codev provides three CLI tools for AI-assisted software development.
sddp-implement-qc-loop
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.
sddp-plan
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.
sddp-qc
Command description: Run quality control against the implemented feature. Argument hint: [optional: testing focus such as unit tests, security audit, requirements sync] Command category: feature-delivery Prerequisites: spec, plan, tasks, implementation:complete.