Survey 技能验证
- 作者仓库星标 90
- 作者仓库 agent-research-skills
Survey Generation
Generate complete academic survey papers with structured outline, RAG-based writing, and citation validation.
Input
$0— Survey topic or research area
Scripts
Literature search
python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
--query "relevant search query" --max-results 50
References
- Survey prompts (outline, writing, citation, coherence):
~/.claude/skills/survey-generation/references/survey-prompts.md
Workflow (from AutoSurvey)
Step 1: Collect Papers
- Search Semantic Scholar / arXiv for papers on the topic
- Collect 50-200 relevant papers with titles and abstracts
- Filter by relevance and citation count
Step 2: Generate Outline (Multi-LLM Parallel)
- Generate N rough outlines independently (parallel)
- Merge outlines into a single comprehensive outline
- Expand each section into subsections
- Edit final outline to remove redundancies
Step 3: Write Subsections (RAG-Based)
For each subsection:
- Retrieve relevant papers for the subsection topic
- Generate content with inline citations
[paper_title] - Enforce minimum word count per subsection
- Only cite papers from the provided list
Step 4: Validate Citations
For each subsection:
- Check that cited paper titles are correct
- Verify citations support the claims made
- Remove or correct unsupported citations
- Use NLI (Natural Language Inference) for claim-source faithfulness
Step 5: Enhance Local Coherence
For each subsection:
- Read previous and following subsections
- Refine transitions and flow
- Preserve core content and citations
- Ensure smooth reading experience
Step 6: Convert Citations to BibTeX
- Replace
[paper_title]with\cite{key} - Generate BibTeX entries for all cited papers
- Validate all citation keys exist in .bib file
Output Structure
survey/
├── main.tex # Complete survey paper
├── references.bib # All citations
├── outline.json # Survey outline
└── sections/ # Individual section files
Rules
- Only cite papers from the collected paper list — never hallucinate citations
- Each subsection must meet minimum word count
- No duplicate subsections across sections
- Citation validation is mandatory before final output
- Local coherence enhancement must preserve all citations
- The survey should be comprehensive and logically organized
Related Skills
- Upstream: deep-research, literature-search, literature-review
- See also: related-work-writing
- 流狐分类
- 写作
- 作者声明 Agent
- 未找到明确声明;不据此推断已兼容或已测试
- 静态检查
- 88 / 100 · 启发式扫描,不代表运行安全
- 作者 / 版本 / 许可
- @lingzhi227 · 未声明 license
- 流狐 Token 估算
- 低消耗
- 流狐接入估算
- 即装即用
- 是否需要外部 API Key
- 未发现要求
- 检测到的系统要求
- 未声明
- 底层运行要求
- Python
- 检测到的文件与系统行为
-
- 只读
- 允许写入 / 修改
- 检测到的网络行为
- 仅限本地
- 安装命令数
- 无(仅作为资料)
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
作者没有在当前 SKILL.md 中定义固定输出样例。 Workflow (from AutoSurvey)
Search Semantic Scholar / arXiv for papers on the topic Collect 50-200 relevant papers with titles and abstracts Filter by relevance and citation count
Generate N rough outlines independently (parallel) Merge outlines into a single comprehensive outline Expand each section into subsections
For each subsection: Retrieve relevant papers for the subsection topic Generate content with inline citations [papertitle]
For each subsection: Check that cited paper titles are correct Verify citations support the claims made
For each subsection: Read previous and following subsections Refine transitions and flow
# Survey Generation
Generate complete academic survey papers with structured outline, RAG-based writing, and citation validation.
## Input
- `$0` — Survey topic or research area
## Scripts
### Literature search
```bash
python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
--query "relevant search query" --max-results 50
```
## References
- Survey prompts (outline, writing, citation, coherence): `~/.claude/skills/survey-generation/references/survey-prompts.md`
## Workflow (from AutoSurvey)
### Step 1: Collect Papers
1. Search Semantic Scholar / arXiv for papers on the topic
2. Collect 50-200 relevant papers with titles and abstracts
3. Filter by relevance and citation count
### Step 2: Generate Outline (Multi-LLM Parallel)
1. Generate N rough outlines independently (parallel)
2. Merge outlines into a single comprehensive outline
3. Expand each section into subsections
4. Edit final outline to remove redundancies
### Step 3: Write Subsections (RAG-Based)
For each subsection:
1. Retrieve relevant papers for the subsection topic
2. Generate content with inline citations `[paper_title]`
3. Enforce minimum word count per subsection
4. Only cite papers from the provided list
### Step 4: Validate Citations
For each subsection:
1. Check that cited paper titles are correct
2. Verify citations support the claims made
3. Remove or correct unsupported citations
4. Use NLI (Natural Language Inference) for claim-source faithfulness
### Step 5: Enhance Local Coherence
For each subsection:
1. Read previous and following subsections
2. Refine transitions and flow
3. Preserve core content and citations
4. Ensure smooth reading experience
### Step 6: Convert Citations to BibTeX
1. Replace `[paper_title]` with `\cite{key}`
… 作者原文负责流程事实;流狐只索引当前章节、要点、文件与命令。
章节 -> Input → Scripts → Literature search → References → Workflow (from AutoSurvey) → Step 1: Collect Papers
要点 -> Generate complete academic survey papers with structured outline, RAG-based writing, and citation validation.
文件/命令 -> ~/.claude/skills/survey-generation/references/survey-prompts.md · [papertitle] · \cite{key} · .claude/skills/deep-research/scripts/searchsemanticscholar.py · .claude/skills/survey-generation/references/survey-prompts.md · outline.js · ../deep-research · ../literature-search
内容 SHA-256 -> 66a169fa69cc
方法与流程
适用与边界
原文中的明确线索
~/.claude/skills/survey-generation/references/survey-prompts.md、[papertitle]、\cite{key}、.claude/skills/deep-research/scripts/searchsemanticscholar.py、.claude/skills/survey-generation/references/survey-prompts.md、outline.js、../deep-research、../literature-search