什么是 iGPT 邮件搜索？

iGPT 邮件搜索是一款强大的检索工具，专为希望在 Openclaw Skills 库中添加深度收件箱智能的开发者而构建。它利用 iGPT API 提供一种结合了语义向量搜索与传统关键词匹配的混合搜索引擎。这使得 AI 智能体能够理解搜索背后的意图，根据含义和上下文查找相关的邮件会话，而不仅仅是匹配字符。通过对完整邮件历史记录进行索引，它克服了特定服务提供商标准搜索的局限性，为所有连接的邮件数据源提供统一的接口。

下载入口:https://github.com/openclaw/skills/tree/main/skills/sammy-spk/igpt-email-intelligence

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

iGPT 邮件搜索 应用场景

• 无需精确关键词即可定位特定的项目更新或董事会会议记录。
• 在精确的日期范围内检索历史信函，用于审计或背景信息收集。
• 识别来自特定人员或部门（如法律部门）的所有相关通信。
• 将高度相关的邮件内容输入到后续的智能体步骤中，进行摘要提取或任务提取。

1. 智能体使用分配给 Openclaw Skills 环境的安全 API 密钥初始化 iGPT Python SDK。
2. 用户完成一次性 OAuth 授权，以连接其 Gmail、Outlook 或 IMAP 账户。
3. 向 iGPT recall 搜索端点发送搜索查询，该端点执行混合语义关键词查找。
4. 系统从隔离的用户索引中检索排序后的结果，确保不存在跨用户的数据访问。
5. 该技能返回结构化的邮件数据，包括会话内容和附件引用，供智能体使用。

iGPT 邮件搜索 配置指南

首先，安装所需的 Python 包：

pip install igptai

然后，通过将 API 密钥设置为环境变量来配置身份验证：

export IGPT_API_KEY="your-api-key-here"

确保目标用户已通过 iGPT 仪表板或 API 授权其邮件数据源。

iGPT 邮件搜索 数据架构与分类体系

该技能处理搜索请求并返回排序后的消息对象。以下参数定义了搜索模式：

name: igpt-email-search
description: >
  Secure, per-user-isolated semantic email search via the iGPT API. Hybrid semantic + keyword
  retrieval across a user's full Gmail, Outlook, or IMAP inbox history — no shell access, no
  filesystem access, API-key scoped only. Returns relevant messages and threads ranked by meaning,
  not just keyword overlap. Use when the user needs to find specific emails, threads, or
  conversations by topic, participant, date range, or content. Retrieval only — for reasoning,
  summaries, or structured extraction, use the companion skill igpt-email-ask.
homepage: https://igpt.ai/hub/playground/
metadata: {"clawdbot":{"emoji":"??","requires":{"env":["IGPT_API_KEY"]},"primaryEnv":"IGPT_API_KEY"},"author":"igptai","version":"1.0.0","license":"MIT","tags":["email","search","retrieval","semantic-search","context","productivity"]}

iGPT Email Search

Search a user's email by meaning, not just keywords. Hybrid semantic + keyword retrieval across their entire inbox history.

What This Skill Does

This skill queries iGPT's recall/search endpoint to find relevant emails and threads from a user's connected inbox. The search engine:

• Combines semantic vector search (understands meaning) with keyword matching (catches exact terms)
• Searches across the user's full indexed email history (not limited to 90 days like some providers)
• Supports date range filtering for time-bounded queries
• Returns ranked results with relevance scoring
• Includes attachment references when present

This is retrieval only. It finds and returns email content. It does not reason over it, summarize it, or extract structured data. For that, use igpt-email-ask — the companion skill that runs iGPT's Context Engine for analysis, summarization, and structured extraction.

When to Use This Skill

• Find emails about a specific topic, project, or person
• Locate threads within a date range
• Retrieve raw email content for further processing
• Feed email context into another tool or agent step
• Check what was discussed about a topic before taking action
• Pull recent correspondence with a specific contact or company

When to Use igpt-email-ask Instead

If you need summarized or synthesized answers, structured data extraction (tasks, decisions, contacts), sentiment analysis, reasoning across multiple threads, or questions that require understanding rather than finding — use igpt-email-ask, not search.

Rule of thumb: if the prompt is a question, use ask. If the prompt is a lookup, use search.

Prerequisites

1. An iGPT API key (get one at https://igpt.ai/hub/apikeys/)
2. A connected email datasource — the user must have completed OAuth authorization via connectors/authorize before search will return results
3. Python >= 3.8 with the igptai package installed

Setup

pip install igptai

Set your API key as an environment variable:

export IGPT_API_KEY="your-api-key-here"

Usage

Basic: Search by topic

from igptai import IGPT
import os

igpt = IGPT(api_key=os.environ["IGPT_API_KEY"], user="user_123")

results = igpt.recall.search(query="board meeting notes")
print(results)

Returns a ranked list of relevant emails and threads matching the query, ordered by relevance.

Search with date range

Narrow results to a specific time window:

results = igpt.recall.search(
    query="budget allocation",
    date_from="2026-01-01",
    date_to="2026-01-31"
)
print(results)

Limit number of results

results = igpt.recall.search(
    query="partnership proposals",
    max_results=10
)
print(results)

Search for a specific person's emails

The semantic engine understands participant context:

results = igpt.recall.search(
    query="emails from Sarah about the product launch",
    date_from="2026-01-01"
)
print(results)

Combine with ask for a two-step workflow

A common pattern: search first to see what's there, then ask for analysis:

# Step 1: Find relevant threads
results = igpt.recall.search(
    query="Acme Corp contract negotiation",
    max_results=20
)
print(f"Found {len(results)} relevant threads")

# Step 2: Ask for structured analysis
analysis = igpt.recall.ask(
    input="Summarize the current status of the Acme Corp contract negotiation. What are the open issues and who owns them?",
    output_format="json"
)
print(analysis)

Parameters

Error Handling

The SDK uses a no-throw pattern. Errors are returned as values, not exceptions:

results = igpt.recall.search(query="Q4 planning")

if isinstance(results, dict) and results.get("error"):
    error = results["error"]
    if error == "auth":
        print("Check your API key")
    elif error == "params":
        print("Check your request parameters")
    elif error == "network_error":
        print("Network issue, retry")
else:
    for result in results:
        print(result)

External Endpoints

This skill communicates exclusively with:

• https://api.igpt.ai/v1/recall/search — the search endpoint
• https://api.igpt.ai/v1/connectors/authorize — only during initial datasource connection setup

No other external endpoints are contacted. No data is sent to any third-party service. The igptai PyPI package source is available at https://github.com/igptai/igptai-python.

Security & Privacy

• API-key scoped: All requests authenticate via IGPT_API_KEY sent as a Bearer token over HTTPS. No shell access, no filesystem access, no system commands.
• Per-user isolation: Every query is scoped to a specific user identifier. User A cannot access User B's email data. Isolation is enforced at the index and execution level, not as a filter layer.
• OAuth read-only: The email datasource connection uses OAuth with read-only scopes. The skill does not send, modify, or delete emails.
• No data retention: Prompts are discarded after execution. Memory is reconstructed on-demand, not stored.
• Transport encryption: All communication occurs over HTTPS. No plaintext endpoints.
• No local persistence: This skill does not write to disk, modify environment files, or create persistent configuration outside of the standard IGPT_API_KEY environment variable.

For the full security model, see https://docs.igpt.ai/docs/security/model.

How It Differs from Basic Email Search

Example Queries

These all work as natural language:

• "board meeting notes" — finds emails about board meetings even if they don't contain that exact phrase
• "emails about the product launch timeline" — semantic understanding of the topic
• "anything from legal about compliance" — understands department and topic context
• "invoices from Q4 2025" — combines topic with implicit date context
• "conversations where deadlines were mentioned" — conceptual search

Companion Skills

Both skills use the same IGPT_API_KEY and connected datasources. Install both for the full search → analyze workflow.

Resources

• Get API Key: https://igpt.ai/hub/apikeys/
• Documentation: https://docs.igpt.ai
• API Reference: https://docs.igpt.ai/docs/api-reference/search
• Playground: https://igpt.ai/hub/playground/
• Python SDK: https://pypi.org/project/igptai/
• Node.js SDK: https://www.npmjs.com/package/igptai
• GitHub: https://github.com/igptai/igptai-python