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Find and curate research papers from academic sources. Use when user asks to find papers, search for research, discover articles, or explore a new topic.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/acertainKnight/project-thoth --skill paper-discovery命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | paper-discovery |
| description | Find and curate research papers from academic sources. Use when user asks to find papers, search for research, discover articles, or explore a new topic. |
| tools | ["list_available_sources","create_research_question","run_discovery_for_question","list_articles","search_articles","collection_stats"] |
Find and curate research papers across academic sources (arXiv, PubMed, Semantic Scholar, OpenAlex, etc.).
For discovery tasks, use ONLY these tools:
| Tool | Purpose |
|---|---|
list_available_sources | See available search sources |
create_research_question | Create a new search query |
run_discovery_for_question | Execute the search |
list_articles | Browse results |
search_articles | Filter/search within results |
collection_stats | Check collection size |
For a quick search on a topic:
Step 1: Create the query
create_research_question(
title="User's topic in 1-2 sentences",
keywords=["keyword1", "keyword2", "keyword3"],
sources=["semantic_scholar", "openalex"],
max_papers=25,
relevance_threshold=0.7
)
Step 2: Run discovery
run_discovery_for_question(question_id="[from step 1]")
Step 3: Review results
list_articles(limit=20, sort_by="relevance")
| Research Area | Recommended Sources |
|---|---|
| CS/ML/AI | arxiv, semantic_scholar |
| Medical/Bio | pubmed, biorxiv |
| General Science | openalex, crossref |
| Cross-disciplinary | semantic_scholar, openalex |
Default: Use semantic_scholar + openalex for broad coverage.
If the user wants papers from a website/journal not in the built-in sources list:
Load the custom-source-setup skill to set up auto-detected scrapers for any website. This allows adding sources like:
Example trigger phrases:
Extract keywords from user's request:
Example:
["vision transformer", "efficient attention", "ViT", "sparse attention"]| Threshold | Use When |
|---|---|
| 0.8+ | User wants only highly relevant papers |
| 0.7 | Default - good balance |
| 0.6 | Comprehensive search, broader coverage |
| 0.5 | Exploratory, casting a wide net |
Delegate using send_message_to_agent when user needs:
Example delegation:
send_message_to_agent(
agent_name="Research Analyst",
message="Analyze these 10 papers on sparse attention and summarize key approaches: [paper IDs]"
)
User: "Find recent papers on mixture of experts in LLMs"
1. create_research_question(
title="Mixture of Experts in Large Language Models",
keywords=["mixture of experts", "MoE", "sparse MoE", "LLM"],
sources=["arxiv", "semantic_scholar"],
max_papers=30,
relevance_threshold=0.75
)
2. run_discovery_for_question(question_id="...")
3. list_articles(limit=15, sort_by="date")
4. Report: "Found X papers on MoE in LLMs. Top 5: [list].
Would you like me to analyze any of these in depth?"
User: "I want to explore what's happening in protein folding research"
1. list_available_sources() # Show user options
2. create_research_question(
title="Recent advances in protein structure prediction",
keywords=["protein folding", "AlphaFold", "protein structure prediction"],
sources=["biorxiv", "pubmed", "semantic_scholar"],
max_papers=50,
relevance_threshold=0.65
)
3. run_discovery_for_question(question_id="...")
4. collection_stats() # Show what was found
5. Report summary of results by sub-topic
| Error | Solution |
|---|---|
| No results | Lower threshold, broaden keywords, add sources |
| Too many results | Raise threshold, add specific keywords |
| Wrong domain papers | Add negative keywords, change sources |
| Timeout | Reduce sources, lower max_papers |
After discovery, report:
## Discovery Results: [Topic]
**Sources searched**: [list]
**Papers found**: [count]
**Relevance threshold**: [value]
### Top Papers:
1. [Title] - [Authors] - [Year]
Brief: [1 sentence description]
2. ...
### Next Steps:
- Would you like me to analyze any of these papers in depth?
- Should I set this up as a recurring search?
- Want me to adjust the search parameters?
Initialize a new user's research assistant. Use this on first interaction or when user asks to "get started", "set up", or "introduce yourself". Also use when you don't know the user's research interests or the human memory block still has placeholder text.
Walk the user through new Thoth features since their last onboarding or update. Use when the user asks "what's new", "what changed", or "what can you do now". Also use after check_whats_new returns updates to walk through them.
Create, manage, and iterate on research plan documents in the Obsidian vault. Use when the user asks for a research plan, literature review roadmap, or when you need to formalize your own working research strategy.