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Agent Skill · 待分类

user-flow-mapping(用户流量映射)

user-flow-mapping 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

user-flow-mapping 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在待分类类的 11,209 个 Skill 里排第 1,268。

总安装 4,125周安装 126GitHub Stars 67下载 1,497核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:user-flow-mapping(用户流量映射)
来源仓库:https://github.com/seb1n/awesome-ai-agent-skills
仓库路径:skills/user-flow-mapping
安装命令:
npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill 'User Flow Mapping'
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill 'User Flow Mapping'

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

用于处理 GitHub 仓库、Issue、Pull Request 等协作信息,适合代码变更管理场景。适用于围绕仓库状态和代码变更进行整理和协作的场景。通过 npx skills add 命令从指定 GitHub 仓库安装使用。安装前需确认权限范围和维护状态,注意可能触发联网或命令执行。

user-flow-mapping 属于待分类类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/user-flow-mapping

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill 'User Fl…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

user-flow-mapping 怎么安装?

在终端执行 npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill 'User Flow Mapping…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

user-flow-mapping 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

user-flow-mapping 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

user-flow-mapping 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

user-flow-mapping 用的人多吗?

公开数据是累计安装 4,125 次,近一周安装 126 次,下载 1,497 次,GitHub 67 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

user-flow-mapping 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
user-flow-mapping

用户流量映射 · 本页

4,12567需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

User Flow Mapping

This skill enables the agent to create detailed user flow diagrams that map every step, decision point, error state, and success path a user encounters while completing a task in a product. The agent produces three types of flows — task flows (single path, no decisions), user flows (multiple paths with decision branches), and wire flows (flows annotated with screen wireframes) — using Mermaid diagram syntax for portability. Each flow includes annotations for conversion metrics, drop-off risk points, and optimization opportunities.

Workflow

  1. Define the Flow Objective and Scope: Identify the specific user goal being mapped (e.g., "Complete a purchase," "Reset a password"). Determine the entry points — how the user arrives at the start of the flow (direct link, homepage navigation, email CTA, push notification). Establish the success criteria and the scope boundary so the diagram does not expand indefinitely.
  2. Identify All Steps and Decision Points: List every screen, action, and system response in sequence. Mark decision points where the user or the system branches (e.g., "Is the user logged in?" or "Did payment succeed?"). Include error states, validation failures, and retry loops. For each step, note whether it is a user action (click, type, swipe) or a system action (redirect, API call, email sent).
  3. Map Happy Path First, Then Edge Paths: Draw the ideal path from entry to success first. Then layer in alternative paths: what happens if the user is not logged in, if validation fails, if the session times out, if the payment is declined. Each branch should terminate in either a success state, an error recovery path, or an exit point.
  4. Annotate with Metrics and Risk Points: At each step, note the relevant metric: page view count, click-through rate, form completion rate, drop-off percentage. Flag high-friction steps where users are likely to abandon (multi-field forms, account creation walls, payment pages). Suggest specific optimizations for each risk point.
  5. Generate the Mermaid Diagram: Produce a clean Mermaid graph TD diagram using consistent node shapes: rounded rectangles () for pages/screens, diamonds {} for decisions, rectangles [] for actions, and double-circles (()) for start/end states. Use descriptive labels and color annotations via style directives to highlight error paths in red and success paths in green.
  6. Review and Iterate: Walk through the diagram with the user to verify completeness. Check that every branch terminates, that no orphan nodes exist, and that the flow accurately reflects the actual product behavior including edge cases.

Usage

Provide the agent with the user goal, the product context (web app, mobile app, or cross-platform), and any known screens or steps. Optionally include analytics data (drop-off rates, conversion percentages) for the agent to annotate onto the diagram. The agent will produce a Mermaid diagram with annotations.

Examples

Example 1: E-Commerce Checkout Flow

User Request: "Map the complete checkout flow for our e-commerce site. Users can be logged in or guests. We support credit card and PayPal."

Agent Response:

graph TD
    START(("Start: Cart Page")) --> REVIEW["Review Cart Items"]
    REVIEW --> EMPTY{Cart empty?}
    EMPTY -- Yes --> BROWSE["Redirect to Shop Page"]
    EMPTY -- No --> AUTH{User logged in?}

    AUTH -- Yes --> SHIPPING["Enter Shipping Address"]
    AUTH -- No --> CHOICE{Guest or Sign In?}
    CHOICE -- Sign In --> LOGIN["Login Page"]
    LOGIN --> LOGIN_OK{Login successful?}
    LOGIN_OK -- Yes --> SHIPPING
    LOGIN_OK -- No --> LOGIN_ERR["Show Error, Retry"]
    LOGIN_ERR --> LOGIN
    CHOICE -- Guest --> GUEST_EMAIL["Enter Email Address"]
    GUEST_EMAIL --> SHIPPING

    SHIPPING --> VALIDATE_ADDR{Address valid?}
    VALIDATE_ADDR -- No --> ADDR_ERR["Show Validation Errors"]
    ADDR_ERR --> SHIPPING
    VALIDATE_ADDR -- Yes --> DELIVERY["Select Delivery Method"]

    DELIVERY --> PAYMENT{Payment method?}
    PAYMENT -- Credit Card --> CC["Enter Card Details"]
    CC --> CC_VALID{Card valid?}
    CC_VALID -- No --> CC_ERR["Show Card Error"]
    CC_ERR --> CC
    CC_VALID -- Yes --> PROCESS

    PAYMENT -- PayPal --> PP["Redirect to PayPal"]
    PP --> PP_OK{PayPal authorized?}
    PP_OK -- No --> PP_ERR["PayPal Cancelled"]
    PP_ERR --> PAYMENT
    PP_OK -- Yes --> PROCESS

    PROCESS["Process Payment"] --> SUCCESS{Payment successful?}
    SUCCESS -- Yes --> CONFIRM["Order Confirmation Page"]
    CONFIRM --> END(("End: Order Complete"))
    SUCCESS -- No --> DECLINE["Show Decline Message"]
    DECLINE --> PAYMENT

    style BROWSE fill:#fef3c7,stroke:#f59e0b
    style LOGIN_ERR fill:#fee2e2,stroke:#ef4444
    style ADDR_ERR fill:#fee2e2,stroke:#ef4444
    style CC_ERR fill:#fee2e2,stroke:#ef4444
    style PP_ERR fill:#fee2e2,stroke:#ef4444
    style DECLINE fill:#fee2e2,stroke:#ef4444
    style CONFIRM fill:#d1fae5,stroke:#10b981
    style END fill:#d1fae5,stroke:#10b981

Metric annotations:

  • Cart → Shipping: ~72% proceed (28% abandon after viewing cart)
  • Shipping → Payment: ~85% proceed (address validation is low friction)
  • Payment → Confirmation: ~64% proceed (highest drop-off; consider saved payment methods)
  • Guest vs Sign In split: ~55% choose guest checkout (reduce friction by keeping guest option prominent)

Example 2: SaaS Onboarding Flow with Decision Points

User Request: "Map the onboarding flow for our project management tool. New users sign up, verify email, choose a plan, create their first project, and invite team members."

Agent Response:

graph TD
    START(("Entry: Landing Page")) --> SIGNUP["Sign Up Form"]
    SIGNUP --> VALIDATE{Form valid?}
    VALIDATE -- No --> FORM_ERR["Show Validation Errors"]
    FORM_ERR --> SIGNUP
    VALIDATE -- Yes --> EMAIL_SENT["Verification Email Sent"]

    EMAIL_SENT --> VERIFIED{Email verified?}
    VERIFIED -- No, 24h+ --> RESEND["Resend Verification"]
    RESEND --> VERIFIED
    VERIFIED -- Yes --> PLAN{Choose Plan}

    PLAN -- Free --> FREE["Free Plan Selected"]
    PLAN -- Pro --> BILLING["Enter Billing Info"]
    BILLING --> BILLING_OK{Payment valid?}
    BILLING_OK -- No --> BILL_ERR["Show Billing Error"]
    BILL_ERR --> BILLING
    BILLING_OK -- Yes --> PRO["Pro Plan Activated"]

    FREE --> PROFILE["Complete Profile"]
    PRO --> PROFILE

    PROFILE --> CREATE["Create First Project"]
    CREATE --> INVITE{Invite team members?}
    INVITE -- Yes --> TEAM["Enter Team Emails"]
    TEAM --> INVITES_SENT["Invitations Sent"]
    INVITES_SENT --> DASHBOARD
    INVITE -- Skip --> DASHBOARD["Dashboard - Onboarding Complete"]
    DASHBOARD --> END(("End: Active User"))

    style FORM_ERR fill:#fee2e2,stroke:#ef4444
    style BILL_ERR fill:#fee2e2,stroke:#ef4444
    style RESEND fill:#fef3c7,stroke:#f59e0b
    style DASHBOARD fill:#d1fae5,stroke:#10b981
    style END fill:#d1fae5,stroke:#10b981

Metric annotations and optimization notes:

  • Sign Up → Email Verified: ~68% verify within 1 hour. Send a reminder at 24 hours. Consider allowing limited access before verification to reduce early churn.
  • Plan Selection: ~80% choose Free initially. Offer a 14-day Pro trial without requiring billing info to increase Pro conversion.
  • Create First Project: Critical activation step. If the user does not create a project within 48 hours, trigger a guided tutorial email. Target: 60% activation within first session.
  • Invite Team: ~35% skip this step. Show the value of collaboration (e.g., "Teams complete projects 3x faster") to increase invite rates.

Best Practices

  • Always map the happy path before adding branches: Starting with the ideal path keeps the diagram readable. Layer in error states, edge cases, and alternative paths incrementally.
  • Use consistent node shapes: Reserve diamonds for decision points, rounded rectangles for screens/pages, and rectangles for user or system actions. This visual grammar makes flows scannable at a glance.
  • Terminate every branch: Every path in the diagram must end at a defined state — success, error recovery, or explicit exit. Orphan nodes indicate missing logic in the product.
  • Annotate with real data when available: Drop-off rates, conversion percentages, and session duration at each step transform a flow diagram from a planning artifact into an optimization tool.
  • Keep flows to one user goal per diagram: Combining "sign up," "purchase," and "manage account" in one diagram creates an unreadable mess. Map each goal separately, then link diagrams at shared entry/exit points.

Edge Cases

  • Circular flows (retry loops): Payment retries, form re-submissions, and re-authentication can create infinite loops in the diagram. Add a maximum retry count annotation (e.g., "Max 3 attempts, then redirect to support") and a terminal exit node for exhausted retries.
  • External system redirects (OAuth, PayPal, 3D Secure): When the user leaves the product for third-party authentication, mark the transition explicitly and account for three outcomes: success return, cancellation return, and timeout/no-return. Include a "user never returns" exit node.
  • A/B test variants: When the flow differs between test groups, create a decision node at the variant split labeled with the test name and variant identifiers, then map both paths to their respective outcomes.
  • Flows that span multiple sessions: Password reset (email → click link hours later) or email verification may not complete in one session. Mark the session boundary explicitly and indicate what triggers re-entry (email link, push notification, return visit).
  • Offline or degraded states in mobile apps: If the product works offline, map what happens when connectivity drops mid-flow: queued actions, error messages, and sync-on-reconnect behavior.

平台分布

Codex

32.52%
按下载量换算487

Claude

30.32%
按下载量换算454

Cursor

17.82%
按下载量换算267

Gemini CLI

10.02%
按下载量换算150

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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