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Discover recent arXiv papers matching a research profile and generate Obsidian-compatible Markdown notes. Use this skill when the user wants to find new papers, refresh their literature inbox, or search arXiv based on their interests.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when a researcher is choosing, framing, refining, or stress-testing a research question, hypothesis, thesis topic, project idea, grant direction, paper angle, or stalled research direction.
精读文献。快速泛读请用paper-quick-read。
泛读：快速概览Zotero库中的文献，单轮LLM生成摘要级Markdown报告，并上传为Zotero Note。深度精读请用paper-deep-reader。
Intelligently organize and categorize Markdown notes within an Obsidian vault. This skill leverages Obsidian MCP tools to analyze the vault's current folder structure and note content, ensuring new or misplaced notes are moved to their most contextually appropriate locations (e.g., PARA, Zettelkasten, or custom taxonomies).
Autonomously scans specific directories (00, 10, 20, 30) in an Obsidian vault, analyzes note content, and intelligently merges AI-generated YAML frontmatter (tags, summary, aliases) into the notes.
Distills a raw, text-based discussion (e.g., meeting notes, user-AI conversations in Markdown) into one or more structured, permanent 'evergreen' knowledge notes.
| name | paper-finder |
| description | Discover recent arXiv papers matching a research profile and generate Obsidian-compatible Markdown notes. Use this skill when the user wants to find new papers, refresh their literature inbox, or search arXiv based on their interests. |
Use this skill when you want to discover recent arXiv papers for a specific research profile and write the results as Obsidian-compatible Markdown notes.
Run the CLI from the repository root:
python skills/paper-finder/find_papers.py \
--profile path/to/research-interest.json \
--output path/to/obsidian/inbox
Optional semantic ranking:
python skills/paper-finder/find_papers.py \
--profile path/to/research-interest.json \
--output path/to/obsidian/inbox \
--semantic
Semantic settings are read from stable-jarvis/.env:
EMBEDDING_BASE_URLEMBEDDING_API_KEYEMBEDDING_MODELYou can pass --semantic-config path/to/config.json for advanced overrides.
If the user does not already have a profile JSON, use Zotero MCP read tools to collect evidence first:
zotero_list_collectionszotero_profile_evidenceThen write a JSON profile matching the same structure used by config/research-interest.example.json:
profile_idprofile_namezotero_basisretrieval_defaultsinterests[]Prefer short method_keywords and only a small number of query_aliases per interest.
Write it in temp/paper-finder/research-interest.json, if there is no such directory, create it.
After the Python retrieval run finishes, read the generated Obsidian note and use Zotero MCP read tools to gather nearby library evidence. Then update the note's frontmatter and the Why It Matters, Quick Takeaways, and Caveats sections using the prompt in skills/paper-finder/prompts/enrich-candidate.prompt.txt.