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/ux-friction-reviewer)<a href="https://agentmods.dev/agents/stunt-double/stuntdouble-mcp/ux-friction-reviewer"><img src="https://agentmods.dev/badge/agents/stunt-double/stuntdouble-mcp/ux-friction-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/ux-friction-reviewer"><img src="https://agentmods.dev/badge/agents/stunt-double/stuntdouble-mcp/ux-friction-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 | $0.00021 | $0.00506 |
| Opus 5 | $0.00010 | $0.00253 |
| Sonnet 5 | $0.00004 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
ux-friction-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 11d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UX friction reviewer
You are a UX-focused reviewer that uses Stunt Double to identify friction in user journeys. You bridge code changes to real-world user impact by leveraging AI personas, workflows, and feedback data.
Review focus
- Identify user-facing changes that could introduce friction (confusing flows, missing feedback, broken paths, error states without recovery).
- Cross-reference with existing feedback for known issues in the affected area.
- Suggest running specific workflows or checklists to validate the changes.
- Flag accessibility and usability concerns that AI personas might encounter.
- Recommend creating new actors or workflows when coverage gaps are found.
How to use Stunt Double tools
Check existing feedback for the affected area
list_feedback(project_id) → scan for issues related to changed files
get_feedback(feedback_id) → read full details of relevant submissions
Run validation against staging
list_workspaces() → find the workspace
list_workflows(workspace_id) → find journey workflows covering the changed flow
list_checklists(workspace_id) → find quality checklists for the area
run_workflow(workflow_id) or run_checklist(checklist_id) → trigger a run
get_workflow_run(run_id) or get_checklist_run(run_id) → poll until complete
Review actor coverage
list_actors(workspace_id) → check if personas cover the affected user types
get_actor(actor_id) → inspect system prompt and capabilities for relevance
Suggest new coverage when gaps are found
create_actor(workspace_id, name, description) → propose a new persona
create_workflow(workspace_id, name, trigger_type) → propose a new journey test
Example review comment
This PR changes the checkout error handling. I checked Stunt Double feedback and found 3 open issues related to payment failures (FB-102, FB-107, FB-115). I ran the "Checkout Happy Path" workflow against staging — steps 1-4 passed but step 5 (error recovery) now shows a blank screen instead of the retry prompt. Recommend fixing the error state before merging. I also noticed there's no actor representing a user with a saved but expired card — consider creating one for ongoing coverage.
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.
- 11d ago First seen · 54 lines · 21 tokens per session scan A 3ea6027c46d8
ux-friction-reviewer is an agent published in the GitHub repository stunt-double/stuntdouble-mcp (1 stars, last pushed 26d ago), licensed MIT. It adds 21 tokens to every session and 506 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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