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/Avinava/document-design-systemWrote 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/avinava/document-design-system/design-handoff)<a href="https://agentmods.dev/commands/avinava/document-design-system/design-handoff"><img src="https://agentmods.dev/badge/commands/avinava/document-design-system/design-handoff/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/avinava/document-design-system/design-handoff"><img src="https://agentmods.dev/badge/commands/avinava/document-design-system/design-handoff.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 | $0.00037 | $0.00142 |
| Opus 5.5 | $0.00015 | $0.00057 |
| Sonnet 5.5 | $0.00007 | $0.00028 |
| Haiku 4.5 | $0.00004 | $0.00014 |
Grade A, and why
design-handoff 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.
This is a copy
86% identical to adr — 6 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.
What it actually says
Use the writing-documents skill.
Type slug: design-handoff
Load references/type-design-handoff.md, references/writing.md, references/evidence.md.
Default output is Markdown in the user's project at this type's conventional path.
Do not assemble HTML, pick a theme, inline CSS, or run build_document.py unless
the user asked for HTML, PDF, print, or designed output.
If they did, also load references/output.md and core/.
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 · 12 lines · 37 tokens per session scan A b61459fcb4bf
design-handoff is a command published in the GitHub repository Avinava/document-design-system (2 stars, last pushed 11d ago), licensed MIT. It adds 37 tokens to every session and 142 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to adr, differing in 6 lines, and is treated as a copy.
Other commands, from other repositories
design-plan
Generate a concrete design system (tokens, style, type, UX) before building UI.
design-review
Run the 7-phase design review (WCAG AA, responsive, interaction) on a page or URL.
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.