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Agent Skill · 研究检索

make-scenario-building(进行场景构建)

make-scenario-building 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

make-scenario-building 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 63,559。

总安装 315周安装 13GitHub Stars 23下载 103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:make-scenario-building(进行场景构建)
来源仓库:https://github.com/integromat/make-skills
仓库路径:skills/make-scenario-building
安装命令:
npx skills add https://github.com/integromat/make-skills --skill make-scenario-building
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/integromat/make-skills --skill make-scenario-building

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。make-scenario-building 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词或来源线索定位内容。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

支持 Codex、Claude 等宿主环境。通过 npx 命令从 GitHub 仓库安装。使用前应确认权限与数据来源合规性。

来源仓库内路径:skills/make-scenario-building

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/integromat/make-skills --skill make-scenario-b…;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

    它需要调用外部服务,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

make-scenario-building 怎么安装?

在终端执行 npx skills add https://github.com/integromat/make-skills --skill make-scenario-building。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

make-scenario-building 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。

make-scenario-building 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

make-scenario-building 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。

make-scenario-building 用的人多吗?

公开数据是累计安装 315 次,近一周安装 13 次,下载 103 次,GitHub 23 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。

make-scenario-building 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
make-scenario-building

进行场景构建 · 本页

31523调用外部服务未明确有提醒

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

Make Scenario Building

This skill guides building a scenario in Make. A scenario is an automated workflow composed of modules connected together. Before building anything, Phase 1 below MUST be completed.

Phase 1: Understand the Business Need & Identify Modules

Phase 1 has three steps. Do not skip or rush any of them.

Step 1: Clarify the Business Use Case

The first job is to understand exactly what the user wants to automate. Use an adaptive interview approach:

  1. Start conversational. Ask 1-2 focused questions about what they want to achieve:

- What task or process do they want to automate? - Which systems or services are involved?

  1. Drill deeper based on answers. Once the basics are clear, clarify:

- What triggers the automation? (time interval, webhook, manual execution) - What data moves between systems and in which direction? - Are there any conditions, branching logic, or error handling needs?

  1. If answers are vague, get structured. Ask explicitly:

- "What is the source system and what data are you pulling from it?" - "What is the destination system and what should happen there?" - "Should this run on a schedule, on-demand, or when an event occurs?"

CRITICAL — Provider disambiguation (MUST follow): When the user mentions a generic category OR describes a capability without naming a specific app, the agent MUST ask which provider/service they use BEFORE proceeding to Step 2. Never assume a provider.

Common ambiguous categories and their possible providers (non-exhaustive — apply the same logic to any category not listed):

  • Forms/surveys: Google Forms, Typeform, JotForm, Tally, Microsoft Forms, SurveyMonkey,...
  • AI/LLM: OpenAI, Anthropic, Google AI, Make AI, Azure OpenAI, Cohere,...
  • Email: Gmail, Outlook, SendGrid, Mailchimp, SMTP,...
  • Calendar: Google Calendar, Outlook Calendar, Calendly,...
  • Cloud storage: Google Drive, Dropbox, OneDrive, Box,...
  • CRM: Salesforce, HubSpot, Pipedrive, Zoho CRM,...
  • Databases: Airtable, Google Sheets, PostgreSQL, MySQL, MongoDB,...
  • Project management: Jira, Asana, Monday.com, Trello, ClickUp, Linear,...
  • Messaging/chat: Slack, Discord, Microsoft Teams, Telegram,...

This also applies when the user describes a capability rather than naming an app. Words like "summarize", "analyze sentiment", "feedback form", "send a notification", or "store data" describe what they want to do — not which service to use. Ask.

Bad: User says "list responses from my feedback form and post a sentiment summary into Discord." Agent assumes Google Forms and OpenAI and proceeds. Good: Same request. Agent asks: "Which form tool holds your responses — Google Forms, Typeform, JotForm, or something else? And for sentiment analysis, do you want to use OpenAI, Anthropic, Make AI, or another AI service?"

Different providers have different modules, capabilities, and connection requirements — guessing wastes time and produces wrong blueprints.

Continue until the use case can be clearly articulated in one paragraph. Every app in the scenario must be explicitly identified by name — if any app is still a generic category or implied by a capability description, ask before proceeding. Do NOT proceed to Step 2 until the business need is fully understood.

Step 2: Identify Make Modules

Once the use case is clear, map it to Make modules using the MCP tools available from the Make MCP server:

  1. Find relevant apps. Use the apps_recommend tool (or a similarly named tool if unavailable) with a description of the user's need. This returns recommended Make apps for the involved systems.

- One app per call. Never batch multiple apps in a single apps_recommend call. Call separately for each distinct app/service. These calls can run in parallel.

  1. List available modules. For each relevant app, use the app_modules_list tool (or similarly named) to see what modules (triggers, actions, searches, transformers) are available. Pass the appVersion returned by apps_recommend.
  2. Get app documentation. For each app, call app_documentation_get using the exact appName value returned by apps_recommend (do not abbreviate or modify it). This returns detailed capabilities and module descriptions. Call once per app, not per module.
  3. Select modules. Pick the specific modules needed:

- Trigger module — what starts the scenario (e.g., Watch New Rows, Webhook, Schedule) - Action modules — what the scenario does (e.g., Create Record, Send Message, Update Row) - Utility modules — if needed for data transformation, iteration, aggregation, routing, or error handling

If a tool is not found by exact name, search for similarly named tools on the Make MCP server. The key capability needed is: recommending apps and listing their modules.

Module name verification: Never guess module names. Always verify via app_modules_list. Case and spelling must match exactly.

No scheduler module: There is no scheduler module in Make. Scheduling is a scenario-level setting, not a module. The scenario always starts with the first module (trigger or action). Scheduling is configured separately via the scenario_scheduling_update tool.

IMPORTANT: As modules are identified, record the following details from the tool responses — they are needed in subsequent phases:

  • App name (exact name as returned by the tool)
  • App version (exact version as returned by the tool)
  • Module name (exact technical name/slug of each module you plan to use)

Step 3: Present the Module Composition & Get Confirmation

Present the proposed module sequence to the user using flowchart notation:

Linear flow:

Trigger: Google Sheets - Watch New Rows → Slack - Send Message → Google Drive - Upload File

Branching flow (with If-Else + Merge) — mutually exclusive branches that converge:

Trigger: Webhook → HTTP - Make a Request → If-Else
  ├─ If (status = "success"): Slack - Send Message
  └─ Else: Email - Send Error
→ Merge → Google Sheets - Log Result

Branching flow (with Router) — multiple branches can fire, no convergence:

Trigger: Webhook → HTTP - Make a Request → Router
  ├─ Route A (status = success): Slack - Send Message
  └─ Route B (priority = high): Email - Send Alert

Flow with iteration:

Trigger: Schedule → Google Sheets - Search Rows → Iterator → Slack - Send Message (for each row)

For each module in the sequence, briefly note:

  • The app and module name
  • What it does in this scenario (1 sentence)

Then ask the user: "Does this module composition achieve what you need? Should I adjust any steps?"

Do NOT proceed beyond Phase 1 until the user confirms the composition is correct. The literal scenario layout and module configuration will be handled in subsequent phases.

Phase 1 Output

Once the user confirms, produce a Scenario Plan summary to carry into subsequent phases. This is the working reference — do not lose it.

## Scenario Plan

**Use case:** <one paragraph summary>
**Trigger type:** <schedule | webhook | manual | event-based>

### Modules

| # | App | App Version | Module (slug) | Module Label | Role in scenario |
|---|-----|-------------|---------------|--------------|------------------|
| 1 | ... | ...         | ...           | ...          | ...              |
| 2 | ... | ...         | ...           | ...          | ...              |

### Flow
<flowchart notation from Step 3>

This table is the source of truth for which apps and modules will be used. Subsequent phases will reference it directly.

CRITICAL — Plan adherence: Once the user confirms the Scenario Plan, treat it as a binding contract. If during Phase 2 a reason emerges to change the module composition, flow structure, or branching pattern (e.g., switching from If-Else + Merge to Router, adding or removing modules, changing the trigger type), STOP and present the proposed change to the user with a clear explanation of why, along with an updated plan summary highlighting what has changed. Do NOT silently deviate from the confirmed plan.


Phase 2: Configure, Deploy & Verify

Once the user confirms the module composition from Phase 1, proceed through these steps:

Step 1: Secure Connections (REQUIRED — never auto-select)

CRITICAL — STOP and ask before proceeding. This is an interactive checkpoint. For EVERY app that needs a connection — even if only one matching connection exists — list the options and ask the user which connection to use. Do NOT auto-select a connection. Do NOT call any module-specific RPCs (rpc_execute for spreadsheet lists, channel lists, folder lists, etc.) until the user has explicitly confirmed a connection for every app. This is a hard gate: no confirmation, no RPCs.

  1. Extract connection requirements. Before checking connections, call extract_blueprint_components with the unconfigured blueprint (all modules placed, parameters empty). This returns the authoritative list of: which modules need connections, the connection type for each, and the required OAuth scopes. Use this output — not manual inspection of the Scenario Plan — as the definitive checklist. Builtin modules (builtin:BasicRouter, builtin:BasicFeeder, json:ParseJSON, etc.) do NOT need connections. AI agent modules (ai-local-agent:RunLocalAIAgent) are NOT builtin — they require an AI provider connection via makeConnectionId.
  2. Check existing connections (with scope verification). Call connections_list with the target teamId. List all connections without a type filter first, then match by accountName in the results — the type filter matches accountName, NOT the Make app name (e.g., Google Sheets uses "google", Gmail uses "google-email", Slack uses "slack2" or "slack3"). Scope verification (CRITICAL for OAuth connections): For each matching connection, compare its scopes against the required scopes from extract_blueprint_components. A connection that authenticates successfully but lacks a required scope will cause 403/permission errors at runtime. If a matching connection exists but its scopes are insufficient, do NOT attempt to use it — either expand its scopes (see make-module-configuring skill, connections reference, Step 3a) or create a new connection with the correct scopes.
  3. Ask the user to pick. For each app, present a numbered list and WAIT for the user's reply:

- If matching connections exist (even just one), list ALL of them with name, ID, metadata (email, workspace), and scope status (sufficient / insufficient). Always include "Create a new connection" as the last option: I found these existing Google connections: 1. "Google - Marketing" (ID: 12345, email: marketing@acme.com) — scopes: sufficient 2. "Google - Personal" (ID: 12346, email: me@gmail.com) — scopes: insufficient (missing Google Drive file access) 3. Create a new connection Which one should I use for Google Sheets? Even if there is only one match, still ask. Connections with insufficient scopes should be listed but flagged — offer scope expansion or new connection creation for those. - If no matching connection exists, inform the user and create a credential request via credential_requests_create, including the required scopes from extract_blueprint_components.

  1. Confirm all connections are ready. Do NOT proceed to Step 2 until every required connection has a user-confirmed connection ID.

Step 2: Configure Each Module

Configure modules left to right (upstream to downstream) following the make-module-configuring skill:

  1. Read the module interface (app-module_get with instructions format)
  2. Load dynamic field options via RPCs (now that connections are confirmed)
  3. Fill parameters and mapper
  4. Validate each module individually (validate_module_configuration)

Step 3: Validate the Blueprint

Call validate_blueprint_schema on the complete blueprint JSON to catch structural issues before submission.

Step 4: Create the Scenario

Call scenarios_create with the validated blueprint. The blueprint must include a top-level metadata object — see Blueprint Construction — Deployment Checklist for the required structure.

Scheduling type for webhook/instant trigger scenarios: When the first module is a webhook or instant trigger (listener: true), always use {"type": "immediately"} as the scheduling type when calling scenario_scheduling_update. Never use "indefinitely" for webhook scenarios — it causes scenario activation to fail with "Invalid interval." Scheduled (polling) scenarios should use "indefinitely" with an interval; webhook scenarios must use "immediately".

Step 5: Activate the Scenario

Newly created scenarios are inactive by default. Call scenarios_activate before attempting to run. Skipping this step causes scenarios_run to fail.

Step 6: Run & Verify

Run the scenario and confirm it succeeds before handing off to the user.

  1. Execute. Call scenarios_run to trigger an immediate run.
  2. Check the result. Call executions_list for the scenario, then executions_get on the most recent execution. Inspect the status field:

- 1 = success — proceed to Step 7. - 3 = error — continue to step 3.

  1. Diagnose the failure. Read error.message and error.causeModule from the execution result. Common runtime issues that pass schema validation:

- Mapped fields resolving to undefined or null at runtime (e.g., {{2.mimeType}} when the upstream module produced no output for that field) - Type conversion errors on optional parameters left at defaults - Missing or expired connection tokens

  1. Fix and retry. To update the scenario after diagnosis:

- Call scenarios_deactivate on the scenario - Call scenarios_update with the corrected blueprint - Call scenarios_activate to re-enable - Call scenarios_run again and repeat from step 2

Repeat the diagnose-fix-retry cycle until the execution succeeds or the issue requires user intervention (e.g., missing input data, external service unavailable). If user action is needed, explain the error and what to do before retrying.

Step 7: Provide the Scenario URL

Always give the user the scenario URL after creation: https://<zone>.make.com/<teamId>/scenarios/<scenarioId> (uses team ID, not organization ID).


Core Concepts Reference

When composing scenarios, consult these feature docs to understand how Make's building blocks work. Read the relevant files before using these features in a module composition.

Foundational

  • Bundles — The unit of data flowing between modules. Understand bundle multiplicity before composing flows.
  • Mapping — Connecting data between modules. Field mapping, data types, collections, arrays, functions/formulas.
  • Connections — Authenticating modules with external services. OAuth, API keys, connection reuse.

Triggers & Scenario Composition

  • Scheduling & Triggers — How scenarios start: instant triggers, polling triggers, schedules, manual/on-demand.
  • Webhooks — Instant triggers via HTTP endpoints. Custom webhooks and app-specific webhooks.
  • Subscenarios — Parent/child scenario composition. Sync and async calls, inputs/outputs, reuse.

Data Flow Patterns

  • Iterations — Processing arrays item-by-item. Implicit iterators, explicit Iterator, specialized iterators, Repeater.
  • Aggregations — Collapsing multiple bundles into one. Array, Text, Numeric, and Table aggregators.
  • Data Stores — Persistent key-value storage across scenario runs. Deduplication, state, cross-scenario data sharing.

Flow Control

  • Routing — Router module: multiple routes, multiple can fire, cannot merge back. Fallback routes.
  • Branching — If-Else module: mutually exclusive branches, can merge back.
  • Merging — Merge module: converges If-Else branches into single flow.
  • Filtering — Input filters: pass/block bundles on conditions. Includes filter-vs-router decision guide.
Router vs If-Else Decision Guide

Choose If-Else + Merge when:

  • Branches are mutually exclusive (only one should run per bundle)
  • Branches need to converge into shared downstream modules (e.g., update a record, send a confirmation)
  • The logic follows an "if A, do X; else if B, do Y; else do Z" pattern

Choose Router when:

  • Multiple routes can fire for the same bundle (e.g., log to Sheets AND alert on Slack)
  • Routes are independent endpoints with no shared follow-up steps
  • You need parallel processing paths that don't converge

Advanced

  • AI Agents — Make AI Agents (New) with tool-calling. Module tools, scenario tools, MCP tools. Non-deterministic logic.
  • Error Handling — Error handlers per module (Break, Commit, Ignore, Resume, Rollback). Throw module. Only suggest when user explicitly asks.
  • Blueprint Construction — Guidelines for building scenario blueprints programmatically via MCP.
  • Quick Patterns — Compressed MCP call chains for common one-shot scenarios (Slack message, Google Sheets, Airtable, email).

Common App Gotchas

High-frequency configuration mistakes that cause silent failures or hard-to-diagnose runtime errors. Check these before finalizing module configuration in Phase 2 Step 2.

Google Sheets: valueInputOption Required for Write Modules

addRow and updateRow always require "valueInputOption": "USER_ENTERED" in the mapper (not parameters). Without it, the API returns 400: INVALID_ARGUMENT — 'valueInputOption' is required. There is no default — the field must be present:

"mapper": {
  "valueInputOption": "USER_ENTERED",
  "values": { "0": "{{1.name}}", "1": "{{1.email}}" }
}

validate_module_configuration will catch this if called — this is exactly why validation is mandatory per module.

Google Sheets: Spreadsheet IDs from listSpreadsheets RPC

IDs returned by the listSpreadsheets RPC (e.g., 1abc123def456) must be prefixed with / when placed in the spreadsheetId parameter for mode: "select" / from: "drive" modules:

  • Correct: "/1abc123def456"
  • Wrong: "1abc123def456"

Only applies to select-mode. Map-mode accepts the raw ID.

Webhook Scenarios: Scheduling Type

When the first module is a webhook (gateway:CustomWebHook or any instant trigger), always use {"type": "immediately"} for scheduling. Using "indefinitely" causes scenario activation to fail with "Invalid interval." See Step 4 above.

Gmail / Google Email: accountName Is "google-email", Not "google"

The google-email app (Gmail) uses a different connection type than Google Sheets, Calendar, and Drive. When filtering connections_list:

  • Google Sheets / Calendar / Drive: accountName: "google"
  • Gmail (google-email): accountName: "google-email"

A generic "google" OAuth connection will NOT work for Gmail modules (google-email:sendAnEmail, google-email:TriggerNewEmail). It lacks the required Gmail scopes and uses a different connection type entirely. Always verify via extract_blueprint_components that you have the correct connection type — do not assume all Google apps share one connection.

IML Date Boundaries: No endOfDay() / startOfDay() Functions

IML does not have endOfDay(), startOfDay(), beginningOfDay(), or similar boundary functions. Attempting to use them produces an "Unknown function" error. To construct day boundaries, use formatDate to extract the date portion and concatenate a literal time:

Start of day: {{formatDate(now; "YYYY-MM-DD")}}T00:00:00Z
End of day:   {{formatDate(now; "YYYY-MM-DD")}}T23:59:59Z

This is the one valid use of date + literal time concatenation. The general rule "never concatenate separate date and time strings" (see IML Expressions) applies to full ISO 8601 datetimes where both parts are dynamic — it does not prohibit combining a formatDate date-only result with a fixed literal time component.

Make AI Tools (ai-tools:Ask): Model Is Required, No Default

The model parameter in ai-tools:Ask (and other Make AI Toolkit modules) is required — there is no default value. Omitting it causes a 400 error at runtime. When using Make's AI Provider (ai-provider connection), use tier names: "low", "medium", or "high". Do not use provider-specific model IDs (e.g., "gpt-4o-mini") with the Make AI Provider — they are not valid tier names and will fail.

No Make AI Provider connection? If the user has no ai-provider connection and cannot create one, check connections_list for alternative AI provider connections (openai-gpt-3, anthropic-claude, gemini-ai-*) and use the corresponding app-specific module instead of ai-tools:Ask. These modules accept provider-specific model IDs. See Blueprint Construction — AI Tools for details.

Official Documentation

Related Skills

  • make-module-configuring — HOW to configure each module: parameters, connections, mapping, webhooks, data stores, IML expressions, validation
  • make-mcp-reference — MCP server configuration, scopes, access control, and troubleshooting

平台分布

Codex

34.63%
按下载量换算36

Claude

30.32%
按下载量换算31

Cursor

16.34%
按下载量换算17

Gemini CLI

9.54%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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