Best use case
auto-researcher is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
自主研究助手 - 深度调研、交叉验证、生成引用报告
Teams using auto-researcher should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/auto-researcher/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How auto-researcher Compares
| Feature / Agent | auto-researcher | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
自主研究助手 - 深度调研、交叉验证、生成引用报告
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Auto Researcher - 自主研究助手
## 🎯 核心功能
自主深度研究任何主题:
- 多源交叉验证
- CRAAP 可信度评估
- 生成引用报告
- 知识图谱构建
- 持续监控更新
**灵感来源**: OpenFang Researcher Hand
---
## 📚 研究方法
### 5 阶段研究流程
```
1. 定义 (Define)
- 澄清问题
- 识别已知/未知
- 设定范围
2. 搜索 (Search)
- 多策略搜索
- 多样化来源
- 查询优化
3. 评估 (Evaluate)
- CRAAP 框架
- 提取数据
- 记录限制
4. 综合 (Synthesize)
- 整合发现
- 解决矛盾
- 识别不确定性
5. 验证 (Verify)
- 交叉检查
- 标注置信度
```
---
## 🔍 CRAAP 评估框架
### Currency (时效性)
- [ ] 何时发布/更新?
- [ ] 信息是否仍然当前?
- [ ] 链接是否有效?
- [ ] 技术主题>2 年可能过时
### Relevance (相关性)
- [ ] 是否直接回答问题?
- [ ] 目标受众是谁?
- [ ] 详细程度是否合适?
- [ ] 是否愿意引用?
### Authority (权威性)
- [ ] 作者资质?
- [ ] 出版机构?
- [ ] 联系信息?
- [ ] 域名类型?(.gov/.edu/组织)
### Accuracy (准确性)
- [ ] 是否有证据支持?
- [ ] 是否经过审核?
- [ ] 能否从其他来源验证?
- [ ] 是否有事实错误?
### Purpose (目的)
- [ ] 为什么存在?
- [ ] 信息/商业/说服/娱乐?
- [ ] 偏见是否明显?
- [ ] 作者是否受益?
### 评分标准
```
A (权威): 通过全部 5 项
B (可靠): 通过 4/5 项
C (有用): 通过 3/5 项,需谨慎
D (弱): 通过≤2/5 项
F (不可靠): 失败,不要引用
```
---
## 🔎 搜索优化技巧
### 查询构建
| 技巧 | 语法 | 示例 |
|------|------|------|
| 精确短语 | `"..."` | `"AI Agent 操作系统"` |
| 站内搜索 | `site:...` | `site:zhihu.com OpenClaw` |
| 排除 | `-` | `AI -artificial` |
| 文件类型 | `filetype:...` | `filetype:pdf 报告` |
| 时间范围 | `after:...` | `after:2025-01-01` |
| OR 操作符 | `OR` | `(OpenClaw OR OpenFang)` |
| 通配符 | `*` | `"如何用*赚钱"` |
### 多策略搜索模式
```
主题:OpenClaw 赚钱方法
搜索查询组合:
1. "OpenClaw 赚钱" site:zhihu.com
2. "OpenClaw 变现" after:2025-01-01
3. "OpenClaw Skill" 开发 外包
4. "ClawHub" 技能市场 收入
5. OpenClaw vs OpenFang 对比
6. "AI Agent" 副业 2025
7. site:github.com openclaw skills
8. filetype:pdf openclaw 文档
```
---
## 📊 报告生成
### 输出格式
```markdown
# 研究报告:[主题]
## 执行摘要
[200 字核心发现]
## 研究方法
- 搜索查询:[列出使用的查询]
- 来源数量:[N 个]
- 研究时间:[日期范围]
## 核心发现
### 发现 1: [标题]
**内容**: [详细描述]
**来源**: [引用,CRAAP 评分]
**置信度**: [高/中/低]
### 发现 2: [标题]
...
## 相互矛盾的信息
[列出不同来源的矛盾点,分析原因]
## 知识缺口
[识别尚未解答的问题]
## 参考文献
[APA 格式引用列表]
## 附录
- 完整搜索结果
- 原始数据
- 方法论细节
```
---
## 🧠 知识图谱构建
### 实体类型
```
- 人物 (Person)
- 组织 (Organization)
- 概念 (Concept)
- 产品 (Product)
- 事件 (Event)
- 地点 (Location)
```
### 关系类型
```
- 属于 (belongs_to)
- 创建 (created_by)
- 使用 (uses)
- 竞争 (competes_with)
- 影响 (influences)
- 引用 (cites)
```
### 存储格式
```json
{
"entities": [
{
"id": "openclaw",
"type": "Product",
"name": "OpenClaw",
"attributes": {
"description": "开源 AI 助手",
"language": "TypeScript",
"creator": "Peter Steinberger"
}
}
],
"relations": [
{
"from": "openclaw",
"to": "peter_steinberger",
"type": "created_by"
}
]
}
```
---
## 📋 使用示例
### 激活研究
```
帮我研究一下"知乎盐选投稿指南",要详细的
```
### 查看进度
```
研究进行得怎么样了?
```
### 获取报告
```
把研究结果整理成报告,要 APA 引用格式
```
### 持续监控
```
持续监控"OpenClaw 新功能",有更新告诉我
```
---
## 🔧 配置选项
```toml
# 研究深度
research_depth = "deep" # basic/standard/deep
max_sources = 20 # 最多引用来源数
min_craap_score = "B" # 最低可信度
# 输出设置
output_format = "markdown" # markdown/pdf/html
citation_style = "APA" # APA/MLA/Chicago
# 监控设置
monitor_enabled = false # 是否持续监控
monitor_frequency = "daily" # daily/weekly
```
---
## 📊 仪表盘指标
```json
{
"researcher_reports_generated": 0,
"researcher_sources_evaluated": 0,
"researcher_entities_stored": 0,
"researcher_relations_stored": 0,
"researcher_last_report_date": null,
"researcher_avg_credibility_score": 0
}
```
---
## 🎯 应用场景
### 场景 1: 市场调研
```
研究"小红书 AI 工具博主"的变现方式
- 分析 Top 10 博主
- 统计变现模式
- 估算收入范围
- 给出进入建议
```
### 场景 2: 竞品分析
```
研究 OpenClaw 的竞品
- OpenFang vs ZeroClaw vs CrewAI
- 功能对比
- 性能基准
- 市场份额
```
### 场景 3: 技术调研
```
研究"AI Agent 自主赚钱"的可行性
- 现有案例
- 技术栈
- 法律风险
- 实操步骤
```
### 场景 4: 持续监控
```
监控"知乎盐选过稿率"变化
- 每周收集数据
- 追踪趋势
- 异常 alert
- 生成月报
```
---
## 📝 从 OpenFang 借鉴的功能
1. ✅ CRAAP 评估框架 (直接采用)
2. ✅ 5 阶段研究流程 (优化适配)
3. ✅ 知识图谱存储 (简化实现)
4. ✅ 多源交叉验证 (完整保留)
5. ✅ 引用报告生成 (APA/MLA 支持)
---
## 🔧 OpenClaw 适配
| OpenFang 功能 | OpenClaw 实现 |
|--------------|--------------|
| `shell_exec` | `exec` 工具 |
| `knowledge_add_entity` | `memory/store` JSON |
| `web_search` | `searxng` skill |
| `web_fetch` | `web_fetch` 工具 |
| `schedule_create` | `qqbot-cron` |
| `dashboard` | `SESSION-STATE.md` |
---
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