Install
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npx agentmarket install skill/weizhena-research-deep-581c11
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About
Use cases: research, productivity, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/research-deep
Tools it pre-approves 6
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
Bash,Read,Write,Glob,WebSearch,Task
What's inside
- Skill name
research-deep- Description
- 读取调研outline,为每个item启动独立agent进行深度调研。禁用task output。
- License
- MIT
- Allowed tools
- Bash,, Read,, Write,, Glob,, WebSearch,, Task
- Instructions
- 95 lines in SKILL.md · view on GitHub
Before installing
- Pre-approves some shell commands
Files (1)
- SKILL.md
SKILL.md (first lines)
# Research Deep - 深度调研
## 触发方式
`/research-deep`
## 执行流程
### Step 1: 自动定位Outline
在当前工作目录查找 `*/outline.yaml` 文件,读取items列表、execution配置(含items_per_agent)。
### Step 2: 断点续传检查
- 检查output_dir下已完成的JSON文件
- 跳过已完成的items
### Step 3: 分批执行
- 按batch_size分批(完成一批需要得到用户同意才可进行下一批)
- 每个agent负责items_per_agent个项目
- 启动web-search-agent(后台并行,禁用task output)
**参数获取**:
- `{topic}`: outline.yaml中的topic字段
- `{item_name}`: item的name字段
- `{item_related_info}`: item的完整yaml内容(name + category + description等)
- `{output_dir}`: outline.yaml中execution.output_dir(默认./results)
- `{fields_path}`: {topic}/fields.yaml的绝对路径
- `{output_path}`: {output_dir}/{item_name_slug}.json的绝对路径(slugify处理item_name:空格替换为_,移除特殊字符)
**硬约束**:以下prompt必须严格复述,仅替换{xxx}中的变量,禁止改写结构或措辞。
**Prompt模板**:
```python
prompt = f"""## 任务
调研 {item_related_info},输出结构化JSON到 {output_path}
## 字段定义
读取 {fields_path} 获取所有字段定义
## 输出要求
1. 按fields.yaml定义的字段输出JSON
2. 不确定的字段值标注[不确定]
3. JSON末尾添加uncertain数组,列出所有不确定的字段名
4. 所有字段值必须使用中文输出(调研过程可用英文,但最终JSON值为中文)
## 输出路径
{output_path}
## 验证
完成JSON输出后,运行验证脚本确保字段完整覆盖:
python ~/.claude/skills/research/validate_json.py -f {fields_path} -j {output_path}
验证通过后才算完成任务。
"""
```
**One-shot示例**(假设调研GitHub Copilot):
```
## 任务
调研 name: GitHub Copilot
category: 国际产品
description: Microsoft/GitHub开发,首个主流AI编程助手,市场份额约40%,输出结构化JSON到 {project_dir}/results/GitHub_Copilot.json
## 字段定义
读取 {project_dir}/fields.yaml 获取所有字段定义
## 输出要求
1. 按fields.yaml定义的字段输出JSON
2. 不确定的字段值标注[不确定]
3. JSON末尾添加uncertain数组,列出所有不确定的字段名
4. 所有字段值必须使用中文输出(调研过程可用英文,但最终JSON值为中文)
## 输出路径
{project_dir}/results/GitHub_Copilot.json
## 验证
完成JSON输出后,运行验证脚本确保字段完整覆盖:
python ~/.claude/skills/research/validate_json.py -f {project_dir}/fields.yaml -j {project_dir}/results/GitHub_Copilot.json
验证通过后才算完成任务。
```
### Step 4: 等待与监控
- 等待当前批次完成
- 启动下一批
- 显示进度
### Step 5: 汇总报告
全部完成后输出:
- 完成数量
- 失败/不确定标记的items
- 输出目录
## Agent配置
- 后台执行: 是
- Task Output: 禁用(agent完成时有明确输出文件)
- 断点续传: 是From Weizhena/Deep-Research-skills, MIT.
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Signals
- Stars
- 2,414
- Forks
- 198
- Open issues
- 2
- Last commit (this folder)
- Apr 10, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Weizhena/Deep-Research-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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对目标话题进行初步调研,生成调研outline。用于学术调研、benchmark调研、技术选型等场景。
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Cite this page
research-deep skill. Agent Mart. https://agentmart.dev/skills/weizhena-research-deep-581c11 (last updated 2026-10-24)