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/codeready-toolchain/tarsyWrote 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/codeready-toolchain/tarsy/design-with-questions)<a href="https://agentmods.dev/commands/codeready-toolchain/tarsy/design-with-questions"><img src="https://agentmods.dev/badge/commands/codeready-toolchain/tarsy/design-with-questions/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/codeready-toolchain/tarsy/design-with-questions"><img src="https://agentmods.dev/badge/commands/codeready-toolchain/tarsy/design-with-questions.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 | $0.01178 |
| Opus 5 | $0.00000 | $0.00589 |
| Sonnet 5 | $0.00000 | $0.00236 |
| Haiku 4.5 | $0.00000 | $0.00118 |
Grade A, and why
design-with-questions 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.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/design-with-questions
You are in design mode. Generate two documents and then walk through the questions together.
Before you start
Do a thorough analysis before generating anything. A good design requires deep understanding of the existing system.
- Explore the codebase: understand the structure, relevant packages, existing interfaces, patterns, and conventions
- Read existing documentation in
docs/— sketches, prior designs, proposals, anything related to this feature - Look at existing tests to understand expected behavior and boundaries
- If the feature involves external technologies, libraries, or patterns you're not confident about, research them first
Only start generating documents once you have enough context to produce an accurate, well-grounded design.
Phase 1: Generate two documents
1. docs/proposals/{name}-design.md — Design Document
A best-guess draft of the full design. Complete enough to reason about, but with open question markers wherever a decision is still needed.
Required sections:
- Status header:
Draft — pending decisions from [{name}-questions.md]({name}-questions.md) - Overview — what this is and why it exists
- Design Principles — key constraints and guiding goals
- Architecture / How It Works — components, relationships, data flows (use diagrams where helpful)
- Core Concepts — key abstractions and their roles
- Implementation Plan — PR-sized steps (see below)
- Open Questions — summary list cross-referencing the questions doc
Implementation Plan
Write it for an AI implementer, not a human project plan. Split code into PRs; non-code steps (research, configuration-only, docs-only) are fine when they are not a PR.
Each PR:
- Leaves the product fully working. New behavior may land incrementally and only be “complete” in a later PR. Do not break existing functionality unless the payoff is clearly worth it — call that out for discussion.
- Is one logical chunk: not a dump, not a drive-by. Split or merge only when it makes implementation easier.
- Ships tests with the change. Deferring tests (e.g. e2e until the feature is whole) is OK if the plan says what is deferred, to which PR, and why.
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 · 128 lines · 0 tokens per session scan A 741330121835
design-with-questions is a command published in the GitHub repository codeready-toolchain/tarsy (10 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,178 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.
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