Pro ChatGPT GPTs Claude Code Dify LangGraph

Deep Research Agent

A disciplined research protocol: question decomposition, source strategy, evidence table and a confidence-rated synthesis.

0 次下载 0 次浏览更新于 Oct 18, 2026 v2.2.0

下方技能配置(提示词、变量、安装步骤)保留英文原文。.aiskill 文件直接面向 AI 智能体,统一使用英文以确保跨平台行为一致。

概述

Deep Research Agent structures open-ended research the way a careful analyst does: decompose the question into sub-claims, decide what evidence would confirm or refute each, then synthesize with explicit confidence levels and citations.

Unlike a single-shot answer, the skill produces a research plan first — so you can steer scope before the expensive work happens. It maintains an evidence table separating what is established, contested and unknown.

Designed for agentic platforms (LangGraph, Dify) that can attach browsing or retrieval tools, but equally useful as a prompt scaffold in GPTs and Claude Code.

系统提示词

预览已脱敏

定义智能体角色、规则与输出格式的指令块。

system-prompt.txt

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用户提示词模板

填充变量后发送,即可运行该技能: {{variables}}

user-template.txt

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变量

名称	必填	说明	示例
{{question}}	是	The research question, as specific as possible.	Which vector DB offers the best p99 latency under 10M embeddings at $500/mo?
{{depth}}	可选	Effort level: quick scan, standard, or exhaustive.	standard
{{sources_hint}}	可选	Known starting sources, date ranges, or constraints.	prefer 2025+ benchmarks; ignore vendor-sponsored posts

完整技能配置

脱敏预览

标准 .aiskill 文件,提供 JSON 与 YAML 两种格式。

deep-research-agent.aiskill (JSON)

{
  "format": "aiskill/v1",
  "skill": {
    "name": "Deep Research Agent",
    "slug": "deep-research-agent",
    "version": "2.2.0",
    "description": "A disciplined research protocol: question decomposition, source strategy, evidence table and a confidence-rated synthesis.",
    "category": "Research",
    "tags": [
      "research",
      "agent",
      "synthesis",
      "citations"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "dify",
      "langgraph"
    ],
    "tested_environment": "GPT-5 with browsing, Claude Sonnet 4.5, LangGraph 0.2+ with retriever tools",
    "license": "CC-BY-4.0"
  },
  "prompts": {
    "system": "████████████████████████████████████████████████████████████████████████████████████████████████",
    "user_template": "████████████████████████████████████████████████████████████████████████████████████████████████"
  },
  "variables": [
    {
      "name": "question",
      "description": "The research question, as specific as possible.",
      "required": true,
      "example": "Which vector DB offers the best p99 latency under 10M embeddings at $500/mo?"
    },
    {
      "name": "depth",
      "description": "Effort level: quick scan, standard, or exhaustive.",
      "required": false,
      "example": "standard"
    },
    {
      "name": "sources_hint",
      "description": "Known starting sources, date ranges, or constraints.",
      "required": false,
      "example": "prefer 2025+ benchmarks; ignore vendor-sponsored posts"
    }
  ],
  "install": {
    "steps": [
      {
        "title": "Copy the system prompt",
        "detail": "Copy the full protocol prompt from the skill page."
      },
      {
        "title": "Deploy on an agentic platform",
        "detail": "Best with tools: enable browsing in a custom GPT, or attach a web-retrieval tool in Dify / LangGraph."
      },
      {
        "title": "Approve the plan",
        "detail": "Always review the Phase 1 plan and prune scope before letting research run."
      },
      {
        "title": "Check the evidence table",
        "detail": "Verify cited sources before external use of the synthesis."
      }
    ]
  },
  "usage": {
    "steps": [
      "Write the question with measurable terms instead of vague superlatives.",
      "Approve or trim the research plan in Phase 1.",
      "Let the agent execute the evidence pass with your tools enabled.",
      "Use the Confidence and Unknowns sections to decide follow-up research."
    ]
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/deep-research-agent",
    "exported_at": "2026-11-03T07:44:22.904Z"
  }
}

deep-research-agent.aiskill (YAML)

format: aiskill/v1
skill:
  name: Deep Research Agent
  slug: deep-research-agent
  version: 2.2.0
  description: >-
    A disciplined research protocol: question decomposition, source strategy, evidence table and a
    confidence-rated synthesis.
  category: Research
  tags:
    - research
    - agent
    - synthesis
    - citations
  platforms:
    - chatgpt
    - claude-code
    - dify
    - langgraph
  tested_environment: GPT-5 with browsing, Claude Sonnet 4.5, LangGraph 0.2+ with retriever tools
  license: CC-BY-4.0
prompts:
  system: ████████████████████████████████████████████████████████████████████████████████████████████████
  user_template: ████████████████████████████████████████████████████████████████████████████████████████████████
variables:
  - name: question
    description: The research question, as specific as possible.
    required: true
    example: Which vector DB offers the best p99 latency under 10M embeddings at $500/mo?
  - name: depth
    description: "Effort level: quick scan, standard, or exhaustive."
    required: false
    example: standard
  - name: sources_hint
    description: Known starting sources, date ranges, or constraints.
    required: false
    example: prefer 2025+ benchmarks; ignore vendor-sponsored posts
install:
  steps:
    - title: Copy the system prompt
      detail: Copy the full protocol prompt from the skill page.
    - title: Deploy on an agentic platform
      detail: >-
        Best with tools: enable browsing in a custom GPT, or attach a web-retrieval tool in Dify /
        LangGraph.
    - title: Approve the plan
      detail: Always review the Phase 1 plan and prune scope before letting research run.
    - title: Check the evidence table
      detail: Verify cited sources before external use of the synthesis.
usage:
  steps:
    - Write the question with measurable terms instead of vague superlatives.
    - Approve or trim the research plan in Phase 1.
    - Let the agent execute the evidence pass with your tools enabled.
    - Use the Confidence and Unknowns sections to decide follow-up research.
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/deep-research-agent
  exported_at: "2026-11-03T07:44:22.904Z"

安装指南

1

Copy the system prompt

Copy the full protocol prompt from the skill page.
2

Deploy on an agentic platform

Best with tools: enable browsing in a custom GPT, or attach a web-retrieval tool in Dify / LangGraph.
3

Approve the plan

Always review the Phase 1 plan and prune scope before letting research run.
4

Check the evidence table

Verify cited sources before external use of the synthesis.

使用方法

Write the question with measurable terms instead of vague superlatives.
Approve or trim the research plan in Phase 1.
Let the agent execute the evidence pass with your tools enabled.
Use the Confidence and Unknowns sections to decide follow-up research.

平台兼容性

ChatGPT GPTs

OpenAI

将系统提示词粘贴到 GPT 的「Instructions」字段,并把用户模板作为起始消息使用。

官方文档

Claude Code

Anthropic

将导出的 .aiskill JSON 保存为项目中的技能文件,或将系统提示词粘贴进 CLAUDE.md / 斜杠命令 Markdown 文件。

官方文档

Dify

LangGenius

新建一个智能体/应用,将系统提示词粘贴到系统提示词面板,并把模板变量注册为输入字段。

官方文档

LangGraph

LangChain

将 JSON 配置加载进图状态:prompts.system 用作系统消息,prompts.user_template 作为可运行模板。

官方文档

预览图集

Deep Research Agent

概述

系统提示词

用户提示词模板

变量

完整技能配置

安装指南

Copy the system prompt

Deploy on an agentic platform

Approve the plan

Check the evidence table

使用方法

平台兼容性

ChatGPT GPTs

Claude Code

Dify

LangGraph

预览图集

相关技能

bgpt-paper-search

citation-management

infographics