GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
作者提供的说明(英文原文)
Deploy AI user personas to validate user journeys at scale. Find UX friction before real users do.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://app.stuntdouble.io/api/mcp在终端中运行这条命令
claude mcp add --transport http stuntdouble https://app.stuntdouble.io/api/mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"stuntdouble": {
"url": "https://app.stuntdouble.io/api/mcp"
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"stuntdouble": {
"type": "http",
"url": "https://app.stuntdouble.io/api/mcp"
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://app.stuntdouble.io/api/mcp菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
工具名称和描述与服务器在 2026年11月6日 报告的完全一致。这些内容由作者撰写,不是我们写的;我们以纯文本形式显示。
get_meGet the account this connection is acting as: id, email, name, timezone and where notifications reach them. Use `timezone` when creating a scheduled workflow rather than defaulting to UTC, and `id` to tell your own runs and feedback from a teammate's in lists that carry only user
list_workspacesList the workspaces you belong to, with your role in each. Call this first: almost every other tool needs a workspace_id from here.
get_workspaceGet workspace details by ID or slug, including the admin-set controls that decide what runs in it may do. `settings` is a ceiling, never a grant: a network policy there applies on top of each worker's own, and a feature switched off there cannot be switched back on per project.
list_workspace_membersList the members of a workspace with their role, name and join date. Use it to resolve the user ids that runs, feedback and assignments carry.
searchSearch everything in a workspace (projects, actors, checklists, interviews, automations, issues, goals, feedback, knowledge, insights, design reviews, resources, conversations) by name or content. Use it instead of listing and filtering, and before creating something that may alr
list_projectsList the live projects in a workspace, most recently opened first. Call this after list_workspaces and match a target URL or product name against these before creating anything new.
get_projectGet one project (the product tracked by checklists, workflows, interviews and feedback), including its URL, audience and product description. Archived projects read as not found.
create_projectCreate a project (a product to track with checklists, workflows, interviews and feedback). Call list_projects first and reuse a matching project rather than creating a duplicate.
list_project_mcp_serversList the third-party MCP servers attached to a project and the tools each allows. Configuration only: those tools are not callable here, but an actor in a run (checklist, interview, workflow) may call the allowed tools of servers whose in_force is true. Servers are registered and
list_workspace_guidelinesList the workspace's whole library of standing rules (design system, tone of voice, brand, compliance, accessibility, shared knowledge), with how many projects hold each one. Read this before writing a rule: attaching one that already exists with `set_project_guideline` beats typ
add_workspace_guidelineAdd a standing rule to the workspace library. Call list_workspace_guidelines first so you do not add a near-duplicate. Attach it to the projects it applies to with `project_ids` here or `set_project_guideline` later, or set `apply_to_design_reviews` so every design review in the
update_workspace_guidelineUpdate a rule in the workspace library, or switch it off. Every project holding it is held to the new wording, so prefer editing the shared rule over adding a near-duplicate. Pass only the fields to change; at least one is required.
remove_workspace_guidelineRemove a rule from the workspace library permanently. This detaches it from every project holding it, so prefer `update_workspace_guideline` with `enabled: false` when the rule may come back.
list_project_guidelinesList the standing rules this project is held to: the workspace library rules it has attached. Read these before writing checks, interview guides or design feedback for the project, and assert them as observable checks rather than restating them. `attachment_id` is this project's
add_project_guidelineAdd a standing rule and hold this project to it (a design-system rule, a tone-of-voice rule, a compliance obligation, or shared knowledge). Every checklist run, design review, interview and triage for the project is then held to it. The rule joins the workspace library, so check
set_project_guidelineAttach a library rule to a project, detach it, or switch it off for this project alone. Attaching is how one workspace rule comes to hold for several projects without being retyped. `enabled: false` keeps the attachment but stands the rule down here, leaving every other project u
list_actorsList active actors (persistent AI user personas) in a workspace, oldest first. Prefer reusing an existing actor over creating a new one; pass an id from here to get_actor, interviews and checklists.
get_actorGet an actor's details: description, system prompt, capabilities, and status. list_actor_knowledge has the knowledge it carries into runs.
create_actorCreate an actor: a persistent AI user persona with its own browser, email inbox (for OTP and magic-link flows), and memory. The description shapes who the persona is, so describe a person (background, goals, tech comfort), not a job title. Call list_actors first and reuse a match
update_actorUpdate an actor's name, description, system prompt, capabilities, or status. Only the fields you pass change; at least one is required. Set status to "archived" to soft-delete.
list_actor_knowledgeList what an actor knows, oldest first. Each entry has a kind: expertise and process entries are listed by title in every run and the actor reads one when the task calls for it; reference and account entries are found by search. Check here before add_actor_knowledge to avoid dupl
add_actor_knowledgeAdd knowledge to an actor. Choose the kind: expertise for a skill or standard it judges by in every run (brand, tone of voice, design system, compliance, accessibility), process for steps it follows, reference for facts it looks up when relevant (product docs, FAQs, policies), ac
remove_actor_knowledgeRemove a knowledge entry from an actor permanently, so future runs no longer use it. Find the entry's id with list_actor_knowledge.
list_conversationsList chat conversations with actors in a workspace, most recently active first, optionally filtered to one actor. Excludes the workspace assistant's conversations. Pass an id to get_conversation to read the messages.
get_conversationGet an actor conversation with its full message history, oldest message first. Find conversation ids with list_conversations.
list_checklistsList the checklists in a workspace, with their target URL, actor, check count and a recent run. Check here before creating a new checklist: re-running an existing one keeps its history as a regression signal. Use get_checklist for the checks themselves.
get_checklistGet a checklist: its target URL, instructions, run settings, every check (with the `id` update_checklist needs to edit one in place) and its 10 most recent runs. Call get_checklist_run with a run id for per-check results and evidence.
get_checklist_runGet a checklist run: per-check results with evidence, score, verdict and the change since the previous run. Poll after run_checklist every 30 to 60 seconds until `status` is terminal; `outcome` is the verdict on the experience. `comparison.changes` (what got better or worse) is u
flag_check_verdictCorrect a wrong passed or failed verdict on one check: pass the result_id from get_checklist_run and a note on what was actually true. Later runs use it as calibration and re-record any code-run script; the run's own score is unchanged. Set clear to withdraw a correction.
run_checklistRun a checklist (async): the actor drives a real browser through the product and judges each check, and may use the project's attached MCP servers (list_project_mcp_servers). Returns a run_id; poll get_checklist_run (runs take a few minutes). Counts against the workspace's plan a
create_checklistCreate a browser checklist: an actor exercises the product at a URL and judges each check with evidence. Checks can verify flows ("a new user can complete signup") or assert standards ("headlines use sentence case"), for brand, compliance and design-system audits. Any reachable U
update_checklistUpdate a checklist. Only the fields you pass change. Pass `checks` to replace the full set of checks, sending each existing check with the `id` from get_checklist so it keeps its run history. Changing the checks, instructions or url retires the recorded script, so the next run dr
delete_checklistDelete a checklist and its checks and runs. This permanently discards run history; prefer update_checklist, or simply not running a checklist, unless the user asks for deletion.
list_feedbackList the comments stakeholders and users have left on a project through the feedback widget, newest first. Each one is pinned to a page position (often to a named element) and carries device info; filter by status "new" when triaging. Use summarise_feedback to turn a list of thes
get_feedbackGet one feedback widget comment with its full reply thread, its page location and version, and a signed screenshot URL when one was captured. Find the id with list_feedback.
summarise_feedbackSummarise a project's comments into themes ranked by severity, open questions, and a self-contained implementation brief for a coding agent. Read the brief before proposing changes instead of reasoning over raw comments. The brief leaves out work an open question holds up; `answe
update_feedback_statusUpdate a feedback widget comment's status: "reviewed" once assessed, "resolved" when confirmed fixed (ideally after a checklist run proves it), "dismissed" sparingly. The dashboard reflects the change immediately.
list_workflowsList workflows (multi-step automations) in a workspace, newest first, with their triggers, whether each is active, its step count and its last run. Call get_workflow on one for its steps and the order a run takes them.
get_workflowGet a workflow with its steps, the edges connecting them, the order a run would reach them, and recent runs. Read this before editing steps: `execution_order` is what actually runs, and any id in `unreachable_step_ids` is a step no run will ever reach.
run_workflowTrigger a workflow run now (async), whatever its trigger type: it executes the stored steps (see get_workflow), which can drive browsers, post PR comments and notify members. Returns a run ID; poll get_workflow_run. Refused over the plan limit or budget cap.
get_workflow_runGet a workflow run with step-level details. Poll this after run_workflow until status is terminal (completed, failed or cancelled).
create_workflowCreate a workflow (multi-step automation) that runs checks without anyone asking. Triggers: manual, schedule (cron), webhook, or vercel_event / github_event / cursor_event / linear_event / slack_event / figma_event so checklists re-run on deploys, pull requests, or design updates
update_workflowUpdate a workflow's name, description, or trigger. Only the fields you pass change. Syncs the Trigger.dev schedule when the cron or trigger type changes. The trigger fields edit the workflow's one trigger; a workflow with several triggers has them edited in the dashboard instead.
toggle_workflowActivate or pause a workflow. Scheduled workflows have their Trigger.dev schedule activated/deactivated to match. Activating a workflow that runs on a schedule, webhook or event needs the mcp:run scope, since it then starts runs on its own; pausing never does. Pausing keeps the s
delete_workflowDelete a workflow and its steps and runs, and remove its schedule. This discards run history; prefer toggle_workflow to pause unless the user asks for deletion.
add_workflow_stepAdd a step to a workflow and connect it into the run. Only stores it: nothing executes until the workflow runs. Appends by default; `sort_order` splices it in at a position. In a run, run_checklist drives a real browser; notification emails or messages members on Slack; github_co
update_workflow_stepChange a step's type or config in place, leaving its connections alone (move it with reorder_workflow_steps). Only stores the change. Config shapes are as in add_workflow_step.
remove_workflow_stepRemove a step and close the gap: whatever led into it is repointed at whatever it led to, so the rest of the automation still runs.
reorder_workflow_stepsSet the order a workflow runs its steps in, as one linear sequence. Pass every step id: a list that leaves a step out is refused, since that step would be disconnected from the run (remove it with remove_workflow_step instead). This rewires the whole automation, so a branch built
connect_workflow_stepsWire a step's output handle to another step, to build or repair a branch (adding and removing steps keeps the plain chain on their own). A condition's 'false' handle starts unconnected, so a failing condition ends the run. Connecting a connected handle replaces its edge; target_s
comment_on_prPost a comment on a GitHub pull request via the workspace GitHub connection. Use it to report checklist or interview results where engineers already look: a short verdict, then per-check outcomes with evidence highlights. Needs the Stunt Double GitHub App installed on the reposit
list_pull_requestsList pull requests for a GitHub repository via the workspace GitHub connection, most recently created first (up to 30). Use it to find the PR number to pass to get_pull_request or comment_on_pr.
get_pull_requestGet details for a GitHub pull request (title, description, author, branches, change stats) via the workspace GitHub connection. Read it before writing a checklist for the change, then report back with comment_on_pr.
list_interviewsList the user interviews in a workspace, newest first, optionally filtered to one project. Returns summary rows; call get_interview for the discussion guide and participants, or get_interview_report for the findings.
get_interviewGet an interview with its discussion guide (sections and their items, in order) and participants with their run status. Also useful for polling progress after launch_interview; call get_interview_report for the synthesised findings.
create_interviewCreate a user interview: AI persona participants complete tasks and answer questions against target_url, and results are synthesised into a report. target_url can be any reachable URL, including prototypes (Figma Make publish links, Claude design artifacts, v0) and preview deploy
update_interviewUpdate an interview’s name, target_url, research brief, status or usability questionnaire. Only the fields you pass change; pass at least one. To start a round use launch_interview rather than setting status to running.
add_interview_sectionAdd a section to an interview’s discussion guide. Sections group related items (e.g. "First impressions", "Checkout"); add questions and tasks to it next with add_interview_item, using the returned id as section_id.
add_interview_itemAdd a question or task to an interview section (create the section first with add_interview_section). Tasks are concrete jobs to attempt in the product ("Find the annual plan price and compare it with monthly"); questions probe comprehension and sentiment. Set expected_evidence o
add_interview_participantAttach a participant to an interview. Provide either actor_id (existing actor) or persona_spec (ad-hoc persona: name, bio, traits), not both. Vary personas and devices across participants (expertise, motivation, scepticism, mobile vs desktop) for richer findings, and use behaviou
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过域名 stuntdouble.io 验证(注册中心会核实域名所有权)
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
在 394 毫秒内响应(HTTP 200)
无需登录
已读取 60 个工具描述,未发现针对 AI 的隐藏指令
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。
GitHub 仓库最后更新于–,最新版本(1.17.0)于前天进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:GitHub, Desktop Commander, Cloudflare MCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Deploy AI user personas to validate user journeys at scale. Find UX friction before real users do.
Stunt Double deploys AI agents with realistic user personas to validate user journeys at scale. Create actors, run automated workflows and checklists against any web app, and surface friction points before real users encounter them. Integrates with Claude, Linear, GitHub, and Slack.
This repo holds plugin and MCP configuration (.claude-plugin/plugin.json and .mcp.json for Claude, .cursor-plugin/plugin.json, marketplace.json and root mcp.json for Cursor, server.json for the MCP registry), plus skills, agents, and Cursor rules. There is no `package.json` and no server code here: the MCP endpoint is hosted at https://app.stuntdouble.io/api/mcp from the main Stunt Double codebase, and the Claude plugin's local server is the published stuntdouble npm package from StuntKit. See CONTRIBUTING.md for how to validate edits and avoid confusing this folder with a Node package.
claude mcp add --transport http stuntdouble https://app.stuntdouble.io/api/mcp
The repository is also a Claude plugin: it bundles the hosted MCP server and the Stunt Double Studio server (.mcp.json) with the skills in skills/, the agents in agents/ and the Studio hooks in hooks/.
claude plugin marketplace add stunt-double/stuntdouble-mcp
claude plugin install stuntdouble@stuntdouble
Type /stuntdouble: to list the plugin's skills. The Studio ones are /stuntdouble:setup, /stuntdouble:run, /stuntdouble:open, /stuntdouble:cloud and /stuntdouble:doctor.
The skills in skills/ install into Claude Code, Cursor, Codex, OpenCode and other agents that read SKILL.md files, using the skills CLI. They drive the MCP server above, so connect that too.
npx skills add stunt-double/stuntdouble-mcp # pick from the list
npx skills add stunt-double/stuntdouble-mcp --skill verify-change # just one
| Skill | What it does |
|---|---|
check-agent-readiness | Check how well AI agents (ChatGPT, Claude, Gemini, Perplexity and others) can find, understand and act on a website using the Stunt Double Index, explain the score from real agent sessions, compare against peers, and re-score after fixes |
check-brand | Audit a product or site against brand and tone-of-voice guidelines |
check-compliance | Check a product against legal and compliance requirements on Stunt Double (cookie consent, privacy and terms access, required disclosures, claim substantiation, unsubscribe flows) and collect evidence for counsel to review |
check-continuity | Check continuity across surfaces on Stunt Double |
check-design-system | Audit a live product against its design system on Stunt Double |
cloud | /stuntdouble:cloud : push a Studio checklist to a workspace, run it there and pull it back. Needs the Claude Code plugin |
create-actor-panel | Create and configure a Stunt Double actor (AI persona) with knowledge entries for realistic user simulation |
design-review | Run a design review session by gathering feedback from multiple Stunt Double actors on a proposed design or flow |
doctor | /stuntdouble:doctor: check Node, Chrome, the viewer port and how local runs are billed. Needs the Claude Code plugin |
maintain-automations | Change an existing Stunt Double automation or checklist without losing run history: retime triggers, edit and rewire workflow steps, build condition branches, update checks in place, and pause or retire what is no longer needed |
open | /stuntdouble:open: open the Studio viewer over every run. Needs the Claude Code plugin |
run | /stuntdouble:run [resume]: run a Studio checklist in a local Chrome. Needs the Claude Code plugin |
run-qa-suite | Run the full Stunt Double QA suite |
run-user-interview | Plan, configure, and launch a structured user interview with AI participants on Stunt Double, then read back the synthesised report |
run-ux-validation | Validate a user journey by running Stunt Double workflows or checklists and reporting the results |
setup | /stuntdouble:setup for Stunt Double Studio: ready the project for local runs (dev server, actor and checklists, CLAUDE.md notes, permission and hooks). Needs the Claude Code plugin |
setup-guardrails | Stand up continuous guardrails on Stunt Double |
triage-feedback | Review, categorize, and manage Stunt Double feedback submissions across projects |
verify-change | Verify a shipped or previewed code change by running a Stunt Double actor through the affected user flows, and optionally report results on the pull request |
Go to Settings → Connectors → Add custom connector and paste:
https://app.stuntdouble.io/api/mcp
Connect the hosted server in ChatGPT developer mode (Settings → Apps → Advanced) as a custom connector with:
https://app.stuntdouble.io/api/mcp
The checklist run and Index report tools render as cards in ChatGPT (and in other MCP Apps hosts, Claude among them).
codex mcp add stuntdouble --url https://app.stuntdouble.io/api/mcp
codex mcp login stuntdouble
Or add it to ~/.codex/config.toml by hand and then run codex mcp login stuntdouble:
[mcp_servers.stuntdouble]
url = "https://app.stuntdouble.io/api/mcp"
gemini mcp add --transport http stuntdouble https://app.stuntdouble.io/api/mcp
Or add it to ~/.gemini/settings.json (or .gemini/settings.json in a project):
{
"mcpServers": { "stuntdouble": { "httpUrl": "https://app.stuntdouble.io/api/mcp" } } }
Run /mcp auth stuntdouble inside Gemini CLI to sign in.
code --add-mcp '{"name":"stuntdouble","type":"http","url":"https://app.stuntdouble.io/api/mcp"}'
Or add it to .vscode/mcp.json in a workspace (or run MCP: Open User Configuration for every workspace):
{
"servers": { "stuntdouble": { "type": "http", "url": "https://app.stuntdouble.io/api/mcp" } } }
The tools appear in Copilot Chat's agent mode.
Add it to opencode.json in a project (or ~/.config/opencode/opencode.json), then run opencode mcp auth stuntdouble:
{只显示了开头部分;其余内容请在仓库中阅读