refactor: flatten skills directory structure
- Restructure skills/research-zh and skills/research-en to flat layout - Each skill now in its own directory (research, research-deep, etc.) - Update installation: cp -r skills/research-zh/* ~/.claude/skills/ - Update command references from /research:deep to /research-deep Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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jilinchen
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Claude Opus 4.5
Claude Opus 4.5
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---
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user-invocable: true
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allowed-tools: Read, Write, Glob, WebSearch, Task, AskUserQuestion
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description: 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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---
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# Research Skill - 初步调研
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## 触发方式
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`/research <topic>`
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## 执行流程
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### Step 1: 模型内部知识生成初步框架
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基于topic，利用模型已有知识生成：
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- 该领域的主要研究对象/items列表
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- 建议的调研字段框架
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输出{step1_output}，使用AskUserQuestion确认：
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- items列表是否需要增减？
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- 字段框架是否满足需求？
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### Step 2: Web Search补充
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使用AskUserQuestion询问时间范围（如：最近6个月、2024年至今、不限）。
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**参数获取**：
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- `{topic}`: 用户输入的调研话题
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- `{YYYY-MM-DD}`: 当前日期
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- `{step1_output}`: Step 1生成的完整输出内容
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- `{time_range}`: 用户指定的时间范围
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**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。
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启动1个web-search-agent（后台），**Prompt模板**：
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```python
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prompt = f"""## 任务
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调研话题: {topic}
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当前日期: {YYYY-MM-DD}
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基于以下初步框架，补充最新items和推荐调研字段。
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## 已有框架
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{step1_output}
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## 目标
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1. 验证已有items是否遗漏重要对象
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2. 根据遗漏对象进行补充items
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3. 继续搜索{topic}相关且{time_range}内的items并补充
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4. 补充新fields
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## 输出要求
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直接返回结构化结果（不写文件）：
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### 补充Items
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- item_name: 简要说明（为什么应该加入）
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...
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### 推荐补充字段
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- field_name: 字段描述（为什么需要这个维度）
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...
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### 信息来源
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- [来源1](url1)
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- [来源2](url2)
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"""
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```
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**One-shot示例**（假设调研AI Coding发展史）：
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```
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## 任务
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调研话题: AI Coding 发展史
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当前日期: 2025-12-30
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基于以下初步框架，补充最新items和推荐调研字段。
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## 已有框架
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### Items列表
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1. GitHub Copilot: Microsoft/GitHub开发，首个主流AI编程助手
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2. Cursor: AI-first IDE，基于VSCode
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...
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### 字段框架
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- 基本信息: name, release_date, company
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- 技术特性: underlying_model, context_window
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...
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## 目标
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1. 验证已有items是否遗漏重要对象
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2. 根据遗漏对象进行补充items
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3. 继续搜索AI Coding 发展史相关且2024年至今内的items并补充
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4. 补充新fields
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## 输出要求
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直接返回结构化结果（不写文件）：
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### 补充Items
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- item_name: 简要说明（为什么应该加入）
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...
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### 推荐补充字段
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- field_name: 字段描述（为什么需要这个维度）
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...
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### 信息来源
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- [来源1](url1)
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- [来源2](url2)
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```
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### Step 3: 询问用户已有字段
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使用AskUserQuestion询问用户是否有已定义的字段文件，如有则读取并合并。
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### Step 4: 生成Outline（分离文件）
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合并{step1_output}、{step2_output}和用户已有字段，生成两个文件：
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**outline.yaml**（items + 配置）：
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- topic: 调研主题
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- items: 调研对象列表
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- execution:
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- batch_size: 并行agent数量（需AskUserQuestion确认）
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- items_per_agent: 每个agent调研项目数（需AskUserQuestion确认）
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- output_dir: 结果输出目录（默认./results）
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**fields.yaml**（字段定义）：
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- 字段分类和定义
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- 每个字段的name、description、detail_level
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- detail_level分层：极简 → 简要 → 详细
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- uncertain: 不确定字段列表（保留字段，deep阶段自动填充）
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### Step 5: 输出并确认
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- 创建目录: `./{topic_slug}/`
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- 保存: `outline.yaml` 和 `fields.yaml`
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- 展示给用户确认
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## 输出路径
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```
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{当前工作目录}/{topic_slug}/
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├── outline.yaml # items列表 + execution配置
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└── fields.yaml # 字段定义
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```
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## 后续命令
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- `/research-add-items` - 补充items
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- `/research-add-fields` - 补充字段
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- `/research-deep` - 开始深度调研
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@@ -0,0 +1,160 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import json
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import sys
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from collections import defaultdict
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from pathlib import Path
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import yaml
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CATEGORY_MAPPING = {
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"基本信息": ["basic_info", "基本信息"],
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"技术特性": ["technical_features", "technical_characteristics", "技术特性"],
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"性能指标": ["performance_metrics", "performance", "性能指标"],
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"里程碑意义": ["milestone_significance", "milestones", "里程碑意义"],
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"商业信息": ["business_info", "commercial_info", "商业信息"],
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"竞争与生态": ["competition_ecosystem", "competition", "竞争与生态"],
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"历史沿革": ["history", "历史沿革"],
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"市场定位": ["market_positioning", "market", "市场定位"],
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}
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_SKIP_KEYS = {"_source_file", "uncertain"}
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def load_fields_yaml(fields_path):
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with fields_path.open(encoding="utf-8") as f:
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data = yaml.safe_load(f)
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items = [
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(field["name"], category["category"], field.get("required", False))
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for category in data.get("field_categories", [])
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for field in category.get("fields", [])
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]
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all_fields = {name for name, _, _ in items}
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required_fields = {name for name, _, required in items if required}
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field_categories = {name: category for name, category, _ in items}
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return all_fields, required_fields, field_categories
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def extract_json_fields(data, category_mapping=None):
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category_mapping = CATEGORY_MAPPING if category_mapping is None else category_mapping
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nested_keys = {k for keys in category_mapping.values() for k in keys}
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fields = set()
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stack = [(data, True)]
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while stack:
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obj, is_category_level = stack.pop()
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if isinstance(obj, dict):
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for k, v in obj.items():
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if k in _SKIP_KEYS:
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continue
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if is_category_level and k in nested_keys:
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if isinstance(v, dict):
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stack.append((v, True))
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continue
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fields.add(k)
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elif isinstance(obj, list):
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stack.extend((item, is_category_level) for item in obj if isinstance(item, dict))
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return fields
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def validate_json(json_path, all_fields, required_fields, field_categories):
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with json_path.open(encoding="utf-8") as f:
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data = json.load(f)
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json_fields = extract_json_fields(data)
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covered = all_fields & json_fields
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missing = all_fields - json_fields
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extra = json_fields - all_fields
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missing_required = missing & required_fields
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missing_by_category = defaultdict(list)
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for field in missing:
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missing_by_category[field_categories.get(field, "未知")].append(field)
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return {
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"file": json_path.name,
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"total_defined": len(all_fields),
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"covered": len(covered),
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"missing": len(missing),
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"extra": len(extra),
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"coverage_rate": len(covered) / len(all_fields) * 100 if all_fields else 100,
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"missing_required": sorted(missing_required),
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"missing_optional": sorted(missing - required_fields),
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"missing_by_category": {k: sorted(v) for k, v in missing_by_category.items()},
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"extra_fields": sorted(extra),
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"valid": len(missing_required) == 0,
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}
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def print_result(result, verbose=True):
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status = "通过" if result["valid"] else "失败"
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line = "=" * 60
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print(f"\n{line}")
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print(f"[{status}] {result['file']}")
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print(line)
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print(f"覆盖率: {result['coverage_rate']:.1f}% ({result['covered']}/{result['total_defined']})")
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if result["missing_required"]:
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print(f"\n[错误] 缺少必填字段 ({len(result['missing_required'])}):")
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print("\n".join(f" - {f}" for f in result["missing_required"]))
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if verbose and result["missing_optional"]:
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missing_required = set(result["missing_required"])
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print(f"\n[警告] 缺少可选字段 ({len(result['missing_optional'])}):")
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for cat in sorted(result["missing_by_category"]):
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optional = [f for f in result["missing_by_category"][cat] if f not in missing_required]
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if optional:
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print(f" [{cat}]: {', '.join(optional)}")
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if verbose and result["extra_fields"]:
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extra = result["extra_fields"]
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print(f"\n[信息] 额外字段 ({len(extra)}):")
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print(f" {', '.join(extra[:10])}")
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if len(extra) > 10:
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print(f" ... 还有 {len(extra) - 10} 个")
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def main():
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import argparse
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parser = argparse.ArgumentParser(description="验证JSON文件是否覆盖fields.yaml中定义的所有字段")
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parser.add_argument("--fields", "-f", type=str, help="fields.yaml路径", default="fields.yaml")
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parser.add_argument("--json", "-j", type=str, nargs="*", help="要验证的JSON文件路径")
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parser.add_argument("--dir", "-d", type=str, help="包含JSON文件的目录", default="results")
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parser.add_argument("--quiet", "-q", action="store_true", help="仅显示摘要")
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args = parser.parse_args()
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fields_path = Path(args.fields)
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if not fields_path.exists():
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for p in (Path.cwd() / "fields.yaml", Path.cwd().parent / "fields.yaml"):
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if p.exists():
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fields_path = p
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break
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if not fields_path.exists():
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print(f"[错误] 找不到fields.yaml: {fields_path}")
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sys.exit(1)
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print(f"字段定义文件: {fields_path}")
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all_fields, required_fields, field_categories = load_fields_yaml(fields_path)
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print(f"总字段数: {len(all_fields)} (必填: {len(required_fields)}, 可选: {len(all_fields) - len(required_fields)})")
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json_files = (
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[Path(p) for p in args.json]
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if args.json
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else sorted(Path(args.dir).glob("*.json")) if Path(args.dir).exists() else []
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)
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if not json_files:
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print("[警告] 未找到JSON文件")
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sys.exit(0)
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results = []
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for json_path in json_files:
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if not json_path.exists():
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print(f"[警告] 文件不存在: {json_path}")
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continue
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result = validate_json(json_path, all_fields, required_fields, field_categories)
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results.append(result)
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print_result(result, verbose=not args.quiet)
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line = "=" * 60
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print(f"\n{line}")
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print("汇总")
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print(line)
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passed = sum(1 for r in results if r["valid"])
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avg_coverage = sum(r["coverage_rate"] for r in results) / len(results) if results else 0
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print(f"验证通过: {passed}/{len(results)}")
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print(f"平均覆盖率: {avg_coverage:.1f}%")
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if passed < len(results):
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sys.exit(1)
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if __name__ == "__main__":
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main()
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