stuntdouble-basics

stuntdouble-basics is a cursor rule for Cursor from stunt-double/stuntdouble-mcp. It costs 1,445 tokens per session, scanned A, original, MIT.

Core instructions for using the Stunt Double MCP server, a service that tests user journeys with AI user personas.

In plain words
What is it for?
Use them when searching Stunt Double resources, choosing a workspace, and creating or working with AI personas.
Why use it?
They explain how to find and manage Stunt Double workspaces, projects, actors, workflows, and other resources without creating duplicates.

Cursor rule for Cursor

Written for Cursor: a Cursor rule (.mdc).

Good fit Use them when searching Stunt Double resources, choosing a workspace, and creating…

Compare 6 cursor rules from other repositories ↓
Install with agentmods
npx agentmods add rules/stunt-double/stuntdouble-mcp/stuntdouble-basics
Install

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.

Clone the repo
git clone --depth 1 https://github.com/stunt-double/stuntdouble-mcp

Made for: Cursor.

Wrote 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.

agentmods badge for stuntdouble-basics

README.md
[![agentmods](https://agentmods.dev/badge/rules/stunt-double/stuntdouble-mcp/stuntdouble-basics.svg)](https://agentmods.dev/rules/stunt-double/stuntdouble-mcp/stuntdouble-basics)
Your own site
<a href="https://agentmods.dev/rules/stunt-double/stuntdouble-mcp/stuntdouble-basics"><img src="https://agentmods.dev/badge/rules/stunt-double/stuntdouble-mcp/stuntdouble-basics.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,445 This file is loaded in full into every session.
When invoked 1,445 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.01445 $0.01445
Opus 5 $0.00723 $0.00723
Sonnet 5 $0.00289 $0.00289
Haiku 4.5 $0.00145 $0.00145

Measured 6d ago against content hash 8762a1404d84, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

stuntdouble-basics 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 6d 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.

rules/stuntdouble-basics.mdc · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

stuntdouble-basics:

  • Stunt Double deploys AI agents with realistic user personas to validate user journeys at scale.
  • Authentication is handled automatically via OAuth 2.1 with PKCE. No API keys or tokens are needed.
  • Always specify a workspace when creating or listing resources. Use list_workspaces to find available workspaces.

Finding things:

  • search is the fastest way to locate anything in a workspace: projects, actors, checklists, interviews, automations, issues, goals, feedback, actor knowledge, insights, design reviews, project resources and conversations, all in one ranked call.
  • Prefer it over listing an entity type and filtering the list yourself. Narrow with types and project_id when you already know the shape of what you want.
  • Search before you create. It is how you find the actor, checklist, or workflow that already covers the job instead of adding a near-duplicate.
  • Search only ever returns entities from workspaces you are a member of; the boundary is enforced server-side, not by the filters you pass.

Actors:

  • Actors represent AI user personas. Create actors with clear names (format: "FirstName - Role"), descriptions, and system prompts.
  • Give actors knowledge via add_actor_knowledge so they understand your product context.
  • Conversations are read-only over MCP (list_conversations, get_conversation); an actor chat is started from the dashboard. To probe actors for research, design reviews or concept testing, run an interview: it puts every persona through the same guide and synthesises the answers, which one-off chats cannot do.
  • Create diverse actors covering different segments: new users, power users, enterprise, accessibility, mobile.

Workflows and checklists:

  • Use workflows for multi-step user journey validation (signup, checkout, onboarding flows).
  • Use checklists for point-in-time quality checks (accessibility, performance, content).
  • Workflow and checklist runs are asynchronous. After triggering a run, poll get_workflow_run or get_checklist_run for results.
  • Use create_workflow with trigger_type "schedule" or "webhook" for automated continuous validation.

Feedback:

  • Review feedback items surfaced by Stunt Double and update their status as issues are triaged or resolved.
  • Status lifecycle: new → reviewed → resolved (or dismissed for false positives).
  • Cross-reference feedback with actors and workflows to identify patterns and coverage gaps.

Interviews:

  • Interviews are structured user research rounds: a discussion guide (sections of questions and tasks) is run against a target URL by AI participants (existing actors or ad-hoc personas).
  • Build the guide with add_interview_section and add_interview_item before launching. Each section should focus on one topic and contain ordered question or task items.
  • Add participants with add_interview_participant — pass actor_id to reuse an existing actor, or persona_spec (name, bio, traits) for an ad-hoc persona.
  • launch_interview is async — it returns a trigger run ID and flips the interview to running. Participants update independently; use get_interview and get_interview_participant to follow progress.
  • Once participants complete, the synthesis task produces a report (summary, themes, recommendations, per-question rollup). Fetch it with get_interview_report, or call regenerate_interview_report after editing transcripts or re-running participants.

Guidelines (the standards a workspace holds itself to):

  • A guideline is a standing rule (design system, tone of voice, brand, content, accessibility, compliance, security, performance, shared knowledge). It is owned by the workspace and attached to the projects it applies to, so one rule holds for several projects without being retyped or edited in several places.
  • Whatever is in force for a project is rendered into every checklist run, design review, interview and triage for it. Read list_project_guidelines before writing checks, interview questions or design feedback, and assert the rules rather than restating or contradicting them.
  • Two switches decide whether a rule is in force: the library's and the project attachment's. list_project_guidelines folds them into one enabled.
  • Write a rule once with add_project_guideline (library plus this project) or add_workspace_guideline (library only), then hold other projects to the same rule with set_project_guideline rather than adding a near-duplicate. Check list_workspace_guidelines first.
  • update_workspace_guideline with apply_to_design_reviews carries a rule to every design review in the workspace, including the Slack and Linear reviews that have no project to attach it through.
  • Guidelines are project-wide standards; add_actor_knowledge is for what one actor needs to remember.

Read the full file on GitHub · 64 lines

Changes

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.

  1. 6d ago First seen · 64 lines · 1,445 tokens per session scan A 8762a1404d84

Subscribe to this mod's changes

stuntdouble-basics is a cursor rule published in the GitHub repository stunt-double/stuntdouble-mcp (1 stars, last pushed 21d ago), licensed MIT. It adds 1,445 tokens to every session, about $0.0072 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.