Borrowing it
Nothing to install: this file belongs to opagani/cassini-workshop. 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/opagani/cassini-workshop/main/.claude/commands/design.mdgit clone --depth 1 https://github.com/opagani/cassini-workshopWrote 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/opagani/cassini-workshop/design)<a href="https://agentmods.dev/commands/opagani/cassini-workshop/design"><img src="https://agentmods.dev/badge/commands/opagani/cassini-workshop/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/opagani/cassini-workshop/design"><img src="https://agentmods.dev/badge/commands/opagani/cassini-workshop/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 | $0.00021 | $0.00723 |
| Opus 5.5 | $0.00008 | $0.00289 |
| Sonnet 5.5 | $0.00004 | $0.00145 |
| Haiku 4.5 | $0.00002 | $0.00072 |
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 2mo 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
100% identical to design — 0 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
design
🎯 Design for change. Every architectural decision here is judged by one question: when this changes, how big is the diff? Pick the boundaries, seams, and data shapes that make the next change small and local.
Decide how to build what /explore defined. This is a solution-space
interview. Output: an architecture, a data model, and a behavioral spec.
Scope
- IN: components & boundaries, data model, database (schema and platform), runtime, sync vs async, build-vs-buy, key tradeoffs, behavioral requirements.
- OUT: the problem/why (that's
/explore— read it, don't redo it), task breakdown (/plan), prose docs/README (/document).
Preflight
- Read
docs/PROJECT.md(the brief — design must serve it), plusdocs/ARCHITECTURE.md,docs/SPEC.md,CLAUDE.md,docs/MEMORY.md. Summarize current state in 1–2 lines. Re-entrant: refine, don't restart. - If PROJECT.md still has open scope questions, surface them — designing on
an undefined problem is wasted work; offer to bounce back to
/explore.
Skills
Pull in the relevant skill rather than improvising:
- DB schema →
postgres-dba(orsqlite-devif local/Bun + SQLite). - Module/class boundaries →
solid-principles,design-principles. - Pattern choice →
gof-patterns. TS conventions →typescript-best-practices.
Interview
Up to ~10 questions, adaptive, batched (4 at a time). Bank:
- Data model — core entities and relationships?
- Database platform, and why (durability, scale, ops)?
- Runtime / language / framework — and any locked constraints?
- Major components and their boundaries?
- Sync or async? Where are the seams?
- External services / APIs — build or buy?
- Auth & trust boundaries, if any?
- Hardest technical risk, and the fallback?
- What are we explicitly not building for now (YAGNI)?
- Non-functionals that bite: scale, latency, offline, cost ceiling?
Produce
- docs/ARCHITECTURE.md (owned): components, data flow, the data model, database platform + schema, key decisions with their rationale and the alternative rejected.
- docs/SPEC.md (owned): behavioral requirements in prose — what the system
must do, observably. This is what
/planslices into stories; keep it testable, not vague. - docs/PROJECT.md: update the decisions log only (don't rewrite it).
- docs/MEMORY.md: append dated entries for each architectural decision — the choice, the why, the rejected alternative.
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.
- 2mo ago First seen · 69 lines · 21 tokens per session scan A 6ea78fe896e7
design is a command published in the GitHub repository opagani/cassini-workshop (0 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 723 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to design, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
flaremo-setup
A one-time setup command for connecting the FlareMo memory ledger. It checks the service address, access token, connection, authentication, and placement of the related skill.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.