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Agent Skill · 研究检索

onboard-context-matic(板载上下文)

onboard-context-matic 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。

onboard-context-matic 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,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,674 个 Skill 里排第 7,968。

总安装 17,546周安装 746GitHub Stars 31,716下载 6,147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:onboard-context-matic(板载上下文)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/onboard-context-matic
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill onboard-context-matic
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/github/awesome-copilot --skill onboard-context-matic

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。

onboard-context-matic 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。通过 npx skills add 命令从指定仓库安装,需确认权限和维护状态。使用前建议核实是否会触发联网、命令执行或文件读写操作。

可结合原始 README 进一步验证具体用法和功能边界。

来源仓库内路径:skills/onboard-context-matic

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/github/awesome-copilot --skill onboard-context…;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

    它需要接触敏感数据,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

onboard-context-matic 怎么安装?

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

onboard-context-matic 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。

onboard-context-matic 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

onboard-context-matic 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。

onboard-context-matic 用的人多吗?

公开数据是累计安装 17,546 次,近一周安装 746 次,下载 6,147 次,GitHub 31,716 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。

onboard-context-matic 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。
  • 来源仓库有 31,716 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
onboard-context-matic

板载上下文 · 本页

17,54631,716接触敏感数据未明确通过

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

Onboarding: ContextMatic MCP

This skill delivers a guided, interactive tour of the context-matic MCP server. Follow every phase in order. Stop after each interaction point and wait for the user's reply before continuing.

Agent conduct rules — follow throughout the entire skill: - Never narrate the skill structure. Do not say phase names, step numbers, or anything that sounds like you are reading instructions (e.g., "In Phase 1 I will…", "Step 1a:", "As per the skill…"). Deliver the tour as a natural conversation. - Announce every tool call before making it. One short sentence is enough — tell the user what you are about to look up and why, then call the tool. Example: *"Let me pull up the list of available APIs for your project language."* This keeps the user informed and prevents silent, unexplained pauses.

Phase 0 — Opening statement and tool walkthrough

Begin with a brief, plain-language explanation of what the server does. Say it in your own words based on the following facts:

The context-matic MCP server solves a fundamental problem with AI-assisted coding: general models are trained on public code that is often outdated, incorrect, or missing entirely for newer SDK versions. This server acts as a live, version-aware grounding layer. Instead of the agent guessing at SDK usage from training data, it queries the server for the *exact* SDK models, endpoints, auth patterns, and runnable code samples that match the current API version and the project's programming language.

After explaining the problem the server solves, walk through each of the four tools as if introducing them to someone using the server for the first time. For each tool, explain:

  • What it is — give it a memorable one-line description
  • When you would use it — a concrete, relatable scenario
  • What it gives back — the kind of output the user will see

Use the following facts as your source, but say it conversationally — do not present a raw table:

| Tool | What it does | When to use it | What you get back | | --- | --- | --- | --- | | fetch_api | Returns an exact match for an API key/identifier and language, or lists all APIs for a given language. The key is the machine-readable identifier returned by fetch_api (for example, paypal), not the human-readable display name (for example, "PayPal Server SDK"). | "What APIs can I use?" / Starting a new project / "Do you have the PayPal SDK?" | A named list of available APIs with short descriptions (full catalog), or one exact API match when you provide its identifier/key and language | | ask | Answers integration questions with version-accurate guidance and code samples | "How do I authenticate?", "Show me the quickstart", "What's the right way to do X?" | Step-by-step guidance and runnable code samples grounded in the actual SDK version | | model_search | Looks up an SDK model/object definition and its typed properties | "What fields does an Order have?", "Is this property required?" | The model's name, description, and a full typed property list (required vs. optional, nested types) | | endpoint_search | Looks up an endpoint method, its parameters, response type, and a runnable code sample | "Show me how to call createOrder", "What does getTrack return?" | Method signature, parameter types, response type, and a copy-paste-ready code sample |

End this section by telling the user that you'll demonstrate the four core discovery and integration tools live during the tour, starting with fetch_api right now. Make it clear that this tour is focused on those core ContextMatic server tools rather than every possible helper the broader workflow might use.


Phase 1 — Show available APIs

1a. Detect the project language

Before calling fetch_api, determine the project's primary language by inspecting workspace files:

  • Look for package.json + .ts/.tsx files → typescript
  • Look for *.csproj or *.sln → csharp
  • Look for requirements.txt, pyproject.toml, or *.py → python
  • Look for pom.xml or build.gradle → java
  • Look for go.mod → go
  • Look for Gemfile or *.rb → ruby
  • Look for composer.json or *.php → php
  • If no project files are found, silently fall back to typescript.

Store the detected language — you will pass it to every subsequent tool call.

1b. Fetch available APIs

Tell the user which language you detected and that you are fetching the available APIs — for example: *"I can see this is a TypeScript project. Let me fetch the APIs available for TypeScript."*

Call fetch_api with language = the detected language and key = "" so the tool returns the full list of available APIs.

Display the results as a formatted list, showing each API's name and a one-sentence summary of its description. Do not truncate or skip any entry.

Example display format (adapt to actual results):

Here are the APIs currently available through this server:

1. PayPal Server SDK   — Payments, orders, subscriptions, and vault via PayPal REST APIs.
2. Spotify Web API     — Music/podcast discovery, playback control, and library management.
....

Phase 2 — API selection (interaction)

Ask the user:

"Which of these APIs would you like to explore? Just say the name or the number."

Wait for the user's reply before continuing.

Store the chosen API's key value from the fetch_api response — you will pass it to all subsequent tool calls. Also note the API's name for use in explanatory text.


Phase 3 — Explain the chosen API

Before calling, say something like: *"Great choice — let me get an overview of [API name] for you."*

Call ask with:

  • key = chosen API's key
  • language = detected language
  • query = "Give me a high-level overview of this API: what it does, what the main controllers or modules are, how authentication works, and what the first step to start using it is."

Present the response conversationally. Highlight:

  • What the API can do (use cases)
  • How authentication works (credentials, OAuth flows, etc.)
  • The main SDK controllers or namespaces
  • The NPM/pip/NuGet/etc. package name to install

Phase 4 — Integration in the project language (interaction)

Ask the user:

"Is there a specific part of the [API name] you want to learn how to use — for example, creating an order, searching tracks, or managing subscriptions? Or should I show you the complete integration quickstart?"

Wait for the user's reply.

Before calling, say something like: *"On it — let me look that up."* or *"Sure, let me pull up the quickstart."*

Call ask with:

  • key = chosen API's key
  • language = detected language
  • query = the user's stated goal, or "Show me a complete integration quickstart: install the SDK, configure credentials, and make the first API call." if they asked for the full guide.

Present the response, including any code samples exactly as returned.


Phase 5 — Demonstrate model_search

Tell the user:

"Now let me show you how model_search works. This tool lets you look up any SDK model or object definition — its typed properties, which are required vs. optional, and what types they use. It works with partial, case-sensitive names."

Before calling, say something like: *"Let me search for the [model name] model so you can see what the result looks like."*

Pick a representative model from the chosen API (examples below) and call model_search with:

  • key = the previously chosen API key (for example, paypal or spotify)
  • language = the detected project language
  • query = the representative model name you picked
API keyGood demo query
paypalOrder
spotifyTrackObject

Display the result, pointing out:

  • The exact model name and its description
  • A few interesting typed properties (highlight optional vs. required)
  • Any nested model references (e.g., PurchaseUnit[] | undefined)

Tell the user:

"You can search any model by name — partial matches work too. Try asking me to look up a specific model from [API name] whenever you need to know its shape."

Phase 6 — Demonstrate endpoint_search

Tell the user:

"Similarly, endpoint_search looks up any SDK method — the exact parameters, their types, the response type, and a fully runnable code sample you can drop straight into your project."

Before calling, say something like: *"Let me fetch the [endpoint name] endpoint so you can see the parameters and a live code sample."*

Pick a representative endpoint for the chosen API and call endpoint_search with an explicit argument object:

  • key = the API key you are demonstrating (for example, paypal or spotify)
  • query = the endpoint / SDK method name you want to look up (for example, createOrder or getTrack)
  • language = the user's project language (for example, "typescript" or "python")

For example:

API key (key)Endpoint name (query)Example language
paypalcreateOrderuser's project language
spotifygetTrackuser's project language
Display the result, pointing out:
  • The method name and description
  • The request parameters and their types
  • The response type
  • The full code sample (present exactly as returned)

Tell the user:

"Notice that the code sample is ready to use — it imports from the correct SDK, initialises the client, calls the endpoint, and handles errors. You can search for any endpoint by its method name or a partial case-sensitive fragment."

Phase 7 — Closing: what you can ask

End the tour with a summary list of things the user can now ask the agent to do. Present this as a formatted menu:


What you can do with this MCP

Quickstart: your first API call

/integrate-context-matic Set up the Spotify TypeScript SDK and fetch my top 5 tracks.
Show me the complete client initialization and the API call.
/integrate-context-matic How do I authenticate with the Twilio API and send an SMS?
Give me the full PHP setup including the SDK client and the send call.
/integrate-context-matic Walk me through initializing the Slack API client in a Python script and posting a message to a channel.

Framework-specific integration

/integrate-context-matic I'm building a Next.js app. Integrate the Google Maps Places API
to search for nearby restaurants and display them on a page. Use the TypeScript SDK.
/integrate-context-matic I'm using Laravel. Show me how to send a Twilio SMS when a user
registers. Include the PHP SDK setup, client initialization, and the controller code.
/integrate-context-matic I have an ASP.NET Core app. Add Twilio webhook handling so I can receive delivery status callbacks when an SMS is sent.

Chaining tools for full integrations

/integrate-context-matic I want to add real-time order shipping notifications to my
Next.js store. Use Twilio to send an SMS when the order status changes to "shipped". Show me
the full integration: SDK setup, the correct endpoint and its parameters, and the TypeScript code.
/integrate-context-matic I need to post a Slack message every time a Spotify track changes
in my playlist monitoring app. Walk me through integrating both APIs in TypeScript — start by
discovering what's available, then show me the auth setup and the exact API calls.
/integrate-context-matic In my ASP.NET Core app, I want to geocode user addresses using
Google Maps and cache the results. Look up the geocode endpoint and response model, then
generate the C# code including error handling.

Debugging and error handling

/integrate-context-matic My Spotify API call is returning 401. What OAuth flow should I
be using and how does the TypeScript SDK handle token refresh automatically?
/integrate-context-matic My Slack message posts are failing intermittently with rate limit
errors. How does the Python SDK expose rate limit information and what's the recommended retry
pattern?

"That's the tour! Ask me any of the above or just tell me what you want to build — I'll use this server to give you accurate, version-specific guidance."

Notes for the agent

  • If the user picks an API that is not in the fetch_api results, tell them it is not currently available and offer to continue the tour with one that is.
  • All tool calls in this skill are read-only — they do not modify the project, install packages, or write files unless the user explicitly asks you to proceed with integration.
  • When showing code samples from endpoint_search or ask, present them in fenced code blocks with the correct language tag.

平台分布

Codex

37.02%
按下载量换算2,276

Claude

27.81%
按下载量换算1,709

Cursor

19.98%
按下载量换算1,228

Gemini CLI

8.54%
按下载量换算525

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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