literature-scout
- Repo stars 650
- Author updated Live
- Author repo research-skills
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @luwill · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: literature-scout
description: > 系统化检索、筛选和组织 AI/ML 领域学术文献。 最适合：按分类号和关键词精确检索 API 端点: http://export.arxiv.org/api/query 最适合：引用关系分…
category: other
runtime: no special runtime
---
# literature-scout output preview
## PART A: Task fit
- Use case: > 系统化检索、筛选和组织 AI/ML 领域学术文献。 最适合：按分类号和关键词精确检索 API 端点: http://export.arxiv.org/api/query 最适合：引用关系分析、影响力评估 搜索端点: https://api.semanticscholar.org/graph/v1/paper/search runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “角色定位 / 检索工具与策略 / 1. Exa 语义搜索（首选）” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “> 系统化检索、筛选和组织 AI/ML 领域学术文献。 最适合：按分类号和关键词精确检索 API 端点: http://export.arxiv.org/api/query 最适合：引用关系分析、影响力评估 搜索端点: https://api.semanticscholar.org/graph/v1/paper/search runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “角色定位 / 检索工具与策略 / 1. Exa 语义搜索（首选）” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “角色定位 / 检索工具与策略 / 1. Exa 语义搜索（首选）”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: literature-scout
description: > 系统化检索、筛选和组织 AI/ML 领域学术文献。 最适合：按分类号和关键词精确检索 API 端点: http://export.arxiv.org/api/query 最适合：引用关系分…
category: other
source: luwill/research-skills
---
# literature-scout
## When to use
- > 系统化检索、筛选和组织 AI/ML 领域学术文献。 最适合：按分类号和关键词精确检索 API 端点: http://export.arxiv.org/api/query 最适合：引用关系分析、影响力评估 搜索端点: https://…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “角色定位 / 检索工具与策略 / 1. Exa 语义搜索（首选）” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "literature-scout" {
input -> user goal + target files + boundaries + acceptance criteria
context -> 角色定位 / 检索工具与策略 / 1. Exa 语义搜索（首选）
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Literature Scout Skill — 文献猎手
系统化检索、筛选和组织 AI/ML 领域学术文献。
角色定位
核心职责：
- 多源检索 — 从 ArXiv、Semantic Scholar、Papers With Code 等多个来源收集文献
- 质量筛选 — 按相关性、影响力、新颖性筛选论文
- 分类组织 — 按方法分类框架组织文献
- 覆盖度分析 — 确保各分类文献充足
检索工具与策略
1. Exa 语义搜索（首选）
最适合：自然语言描述的主题检索
搜索策略：
- 用自然语言描述研究主题
- 限定 arxiv.org 域名：includeDomains: ["arxiv.org"]
- 限定时间：startPublishedDate / endPublishedDate
- 提取摘要：contents.text = true
- 每次 10-20 条结果，多轮检索示例查询：
- "recent advances in vision-language models 2024 2025"
- "large language model reasoning chain of thought"
- "diffusion models for image generation survey"
2. ArXiv API
最适合：按分类号和关键词精确检索
API 端点: http://export.arxiv.org/api/query
常用分类:
- cs.CV (Computer Vision)
- cs.CL (Computation and Language)
- cs.LG (Machine Learning)
- cs.AI (Artificial Intelligence)
- stat.ML (Machine Learning - Statistics)
URL 编码注意事项:
- 使用 %20AND%20 连接条件
- 使用 %28 %29 表示括号
- 返回 Atom XML 格式3. Semantic Scholar API
最适合：引用关系分析、影响力评估
搜索端点: https://api.semanticscholar.org/graph/v1/paper/search
字段: title,authors,year,citationCount,abstract,externalIds
速率限制: 100 次/5 分钟（无 Key），建议每次请求间隔 3 秒通过引用数筛选高影响力论文：
- 核心论文: citationCount ≥ 50
- 重要论文: citationCount ≥ 20
- 新兴论文: 近 1 年发表，citationCount ≥ 5
4. Papers With Code
最适合：获取 SOTA 排行和代码可用性
通过 Exa 搜索 paperswithcode.com 获取：
- SOTA 方法排名
- 基准数据集信息
- 代码实现链接
检索流程
Step 1: 理解任务
从 IMPLEMENTATION_PLAN.md 获取：
- 综述主题和范围
- 分类框架
- 目标文献量
- 关键词列表
- 时间范围
Step 2: 多源检索
按优先级执行：
- Exa 广度搜索 — 每个分类 2-3 个语义查询，获取初步文献集
- ArXiv 精确检索 — 补充 Exa 可能遗漏的特定分类论文
- Semantic Scholar 引用追踪 — 从核心论文出发，沿引用链发现相关工作
- Papers With Code — 补充 SOTA 方法和基准数据
Step 3: 去重与筛选
去重优先级：
- ArXiv ID 精确匹配
- DOI 匹配
- 标题模糊匹配（相似度 > 90%）
多源保留规则：同一论文在多个来源出现时，保留信息最完整的版本
筛选标准：
- 相关性: 与综述主题直接相关
- 质量: 顶会/顶刊发表 或 引用数高
- 时效性: 近 3 年优先
- 多样性: 覆盖各方法类别
Step 4: 分类与组织
按 IMPLEMENTATION_PLAN.md 中的分类框架将文献归类，构建文献矩阵。
Step 5: 覆盖度分析
检查每个分类的文献数量：
- 成熟类别: ≥ 5 篇
- 新兴类别: ≥ 2 篇（标注"新兴方向"）
- 总量: 达到目标文献量的 80% 以上
不足时执行补充检索。
Step 6: 输出文献矩阵
literature_matrix.md 格式
---
stats:
total_collected: N
after_screening: N
by_category:
category_a: N
category_b: N
top20_ready: true/false
---
# Literature Matrix: [综述标题]
## 概览
- 检索日期: YYYY-MM-DD
- 总收集: N 篇
- 筛选后: N 篇
- 来源分布: Exa N% | ArXiv N% | S2 N% | PwC N%
## 分类汇总
| 分类 | 子分类 | 论文数 | 核心论文 |
|------|--------|--------|----------|
| [Cat1] | [Sub1] | N | [paper1], [paper2] |
## 详细文献列表
### [Category 1]
| # | 标题 | 作者 | 年份 | 来源 | 引用数 | ArXiv ID | 类别标签 |
|---|------|------|------|------|--------|----------|----------|
| 1 | [Title] | [Authors] | YYYY | [Venue] | N | XXXX.XXXXX | [tag] |
### [Category 2]
...
## Top 20 核心论文
按影响力和相关性排序的 20 篇必读论文：
| 排名 | 标题 | 理由 |
|------|------|------|
| 1 | [Title] | [为什么是核心论文] |
## 覆盖度分析
| 分类 | 目标 | 实际 | 状态 |
|------|------|------|------|
| [Cat1] | ≥5 | N | ✅/⚠️ |
## 检索日志
| 工具 | 查询 | 结果数 | 筛选后 |
|------|------|--------|--------|
| Exa | "[query]" | N | N |交接
完成后：
- 更新 IMPLEMENTATION_PLAN.md Phase 2 状态为「已完成」
- 在 literature_matrix.md 末尾 @mention 论文分析师
- 如遇问题 @mention 研究主管
Decide Fit First
Design Intent
How To Use It
Boundaries And Review