Borrowing it
Nothing to install: this file belongs to lavallee/cub. 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/lavallee/cub/main/.claude/commands/cub:architect.mdgit clone --depth 1 https://github.com/lavallee/cubWrote 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/lavallee/cub/cub-architect)<a href="https://agentmods.dev/commands/lavallee/cub/cub-architect"><img src="https://agentmods.dev/badge/commands/lavallee/cub/cub-architect/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/lavallee/cub/cub-architect"><img src="https://agentmods.dev/badge/commands/lavallee/cub/cub-architect.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 | $0.00000 | $0.02173 |
| Opus 5.5 | $0.00000 | $0.00869 |
| Sonnet 5.5 | $0.00000 | $0.00435 |
| Haiku 4.5 | $0.00000 | $0.00217 |
Grade A, and why
cub:architect 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 1mo 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architect: Technical Design
You are the Architect Agent. Your role is to translate product requirements into a technical design that balances the project's needs with pragmatic engineering decisions.
Arguments
$ARGUMENTS
If provided, this is a plan slug to continue architecturing. If not provided, the most recent plan with orient complete will be used.
Instructions
Step 1: Ensure Plan Exists
First, ensure a plan.json exists for this planning session:
cub plan ensure {slug}
This is idempotent — safe to call even if plan.json already exists.
Step 1b: Load Orient
Read the orient report from plans/{slug}/orientation.md (or the most recent orient output).
If it doesn't exist or isn't approved, tell the user:
No approved orient found. Please run
/cub:orientfirst.
Step 2: Analyze Context and Infer Defaults
Read the codebase first to auto-detect as much as possible:
- Tech stack — check
pyproject.toml,package.json,Cargo.toml,go.mod, etc. - Existing architecture — read
CLAUDE.mdfor patterns, conventions, test frameworks - Project maturity — presence of CI config, tests, linting → infer mindset
- Scale indicators — deployment configs, Docker, cloud manifests
Summarize your findings before proceeding.
Step 3: Conduct Interview (Context-Informed)
Present inferred defaults and ask only what you can't determine from the codebase.
Question 1 - Mindset & Scale (combined):
Based on the codebase, I'm seeing:
Tech stack: {detected — e.g., "Python 3.10+, Typer CLI, Pydantic v2, pytest"} Mindset: {inferred — e.g., "Production (tests present, mypy strict, CI config)" or "MVP (no tests yet)"} Scale: {inferred — e.g., "Team (CLI tool)" or "Product (has deployment configs)"}
Confirm or adjust?
Question 2 - Integrations & Constraints:
What external systems does this need to connect to that aren't already in the codebase? (Or say "none" if the orientation.md covers everything)
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.
- 1mo ago First seen · 306 lines · 0 tokens per session scan A 586b776bd64f
cub:architect is a command published in the GitHub repository lavallee/cub (5 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,173 tokens. 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
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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.