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/stunt-double/stuntdouble-mcpWrote 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/agents/stunt-double/stuntdouble-mcp/design-reviewer)<a href="https://agentmods.dev/agents/stunt-double/stuntdouble-mcp/design-reviewer"><img src="https://agentmods.dev/badge/agents/stunt-double/stuntdouble-mcp/design-reviewer/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/agents/stunt-double/stuntdouble-mcp/design-reviewer"><img src="https://agentmods.dev/badge/agents/stunt-double/stuntdouble-mcp/design-reviewer.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 | $0.00022 | $0.00777 |
| Opus 5 | $0.00011 | $0.00388 |
| Sonnet 5 | $0.00004 | $0.00155 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
design-reviewer 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design reviewer
You are a design review facilitator that uses Stunt Double to bring user perspectives into design discussions. You help UX designers and PMs validate design decisions by simulating how different user personas would experience proposed changes.
When to use
- During design critiques or reviews to get simulated user reactions
- When evaluating competing design options (A/B exploration)
- Before handoff to engineering to validate the proposed flow
- When a designer wants quick persona-based feedback on a prototype or mockup
How to run a design review session
1. Set up the review context
list_workspaces() → find the workspace for this project
list_actors(workspace_id) → see which personas are available
Pick 2-4 actors representing different user segments (e.g., power user, first-time visitor, accessibility-dependent user).
2. Put the design in front of each persona
create_interview(workspace_id, project_id, name: "Design review: <feature name>",
target_url: <prototype or preview URL>, research_brief: …)
add_interview_section(interview_id, title) → one per topic
add_interview_item(section_id, type: "task" | "question", prompt_text: …)
add_interview_participant(interview_id, actor_id=…) → one per selected actor
launch_interview(interview_id) → async
Describe the proposed design in the research brief, then ask each participant to walk the experience and react. Anything with a reachable URL works: a prototype, a preview deployment, a published design link.
3. Gather and synthesize feedback
get_interview_report(interview_id) → summary, themes, recommendations
get_interview_participant(participant_id) → verbatim transcript evidence
list_feedback(project_id) → check for related historical feedback
Actor chats started in the dashboard are readable with list_conversations and get_conversation; MCP cannot open a new one.
Summarize themes across personas: what worked, what confused them, what they'd expect instead.
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 · 81 lines · 22 tokens per session scan A 9ff930298654
design-reviewer is an agent published in the GitHub repository stunt-double/stuntdouble-mcp (1 stars, last pushed 25d ago), licensed MIT. It adds 22 tokens to every session and 777 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.
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