Imported from jiahao-shao1/sjh-skills (skills/scholar-agent/SKILL.md). Install upstream with npx skills add jiahao-shao1/sjh-skills --skill scholar-agent. Copyright stays with the author.

Scholar Agent

End-to-end automation: paper discovery → filtering → deep reading → feedback.

Two modes:

• Basic Mode: Pure CLI — fetch, filter, rate papers via REST API (no browser needed)
• Enhanced Mode: CLI + NotebookLM — deep-read papers with source-grounded answers from Gemini

Subcommands

Prerequisites

• Basic Mode only requires playwright-cli (for Scholar Inbox login)
• Enhanced Mode additionally requires notebooklm-py with Google auth completed (notebooklm login)

Why notebooklm-py?

Scholar Agent 的深度阅读功能通过 notebooklm CLI 实现：自动创建笔记本、批量添加 arXiv 论文为 source、查询 Gemini 获取 source-grounded 回答。notebooklm-py 使用 Google 内部 RPC API（非浏览器自动化），稳定性远高于 DOM 操作。

安装 notebooklm-py 后，你也可以独立使用它管理任意 NotebookLM 笔记本（不限于论文）：

notebooklm list                                    # 列出所有笔记本
notebooklm use <id>                                # 选择笔记本
notebooklm source add "https://arxiv.org/abs/..."  # 添加 source
notebooklm ask "summarize the key findings"        # 提问

Setup

One-click environment check and login:

PYTHONPATH=<skill-path> python3 -m scholar_inbox setup

Checks: Python → playwright-cli → Scholar Inbox login → notebooklm CLI

Manual install steps:

# 1. Scholar Inbox browser login (required)
npm install -g @anthropic-ai/playwright-cli

# 2. NotebookLM API (required for Enhanced Mode)
pipx install "notebooklm-py[browser]"

# 3. NotebookLM Google login (first time only — opens browser)
notebooklm login

Filtering Configuration

Config files at ~/.config/scholar-inbox/:

When /scholar-inbox is invoked, the corresponding project config is loaded based on the current working directory name. If a project config exists, papers are filtered by keywords and institution tiers, and classified into NotebookLM notebooks according to the rules.

First-Time Setup

On first /scholar-inbox invocation, check if ~/.config/scholar-inbox/context.md exists:

• Exists → Load config, proceed to normal flow
• Missing → Interactively collect preferences via AskUserQuestion, then generate config

Round 1: Research Preferences (ask 3 questions simultaneously)

1. Research Interests

   • header: "Research interest keywords"
   • options: "RL, VLM, visual reasoning" / "NLP, LLM, alignment" / Other (custom)
   • preview: Used for relevance ranking during paper filtering\nExample: "reinforcement learning, vision-language model, tool use"

2. Institution Preference

   • header: "Institution tiering"
   • options: "Tier-aware (top > well-known > other)" / "No preference"
   • preview: When enabled: OpenAI/DeepMind/META etc. shown first

3. Daily Paper Count

   • header: "Papers per day"
   • options: "5" / "10" / "15"

Round 2: Classification + Project (ask 2 questions simultaneously)

4. NotebookLM Classification

   • header: "Notebook classification dimension"
   • options: "Auto-classify by research topic" / "By method type (RL / SFT / Data / Eval)" / "All in one notebook"

5. Project-Level Config

   • header: "Enable project-level filtering?"
   • options: "Yes (only show project-relevant papers in specific project directories)" / "No"
   • If "Yes", follow up with the current project's core keywords

Config Generation

Based on user answers, generate ~/.config/scholar-inbox/context.md:

# Scholar Inbox Global Config

## Research Interests
keywords: RL, VLM, visual reasoning, tool use

## Filtering Preferences
daily_limit: 10
institution_tier: true  # whether to tier institutions

## NotebookLM Classification
mode: auto_topic  # auto_topic / method_type / single_notebook

If project-level config is enabled, also generate ~/.config/scholar-inbox/<project>.md:

# <project> Project Config

## Project Keywords
keywords: agentic reasoning, image editing, multi-turn tool use

## Filtering Rules
Only show papers matching project keywords; demote others but don't hide them.

Config files can be manually edited afterwards.

CLI Quick Reference

Running the CLI: If scholar-inbox is not on PATH:

PYTHONPATH=<skill-path> python3 -m scholar_inbox <command>

Authentication

Session cookie stored at ~/.config/scholar-inbox/session.json (~7 day expiry).

scholar-inbox login              # auto-extract from Playwright profile
scholar-inbox login --browser    # open browser, auto-extract cookie on login
scholar-inbox login --cookie VAL # manual paste from DevTools
scholar-inbox status             # check if session is valid

Execution Flow

Mode 1: /scholar-inbox (Daily Paper Filtering + Reading)

Phase A: Collect + Filter + Ingest [Dispatch Subagent in Background]

Dispatch a subagent with incremental output — subagent must write results to a temp file after each step, so partial work survives if killed.

Subagent prompt must include:

将每一步的结果增量写入 /tmp/scholar_inbox_results.json。
每处理完一篇论文就更新文件，确保文件随时是有效 JSON。
格式：{"papers": [...], "status": "in_progress|done", "last_updated": "ISO8601"}

Step A1: Fetch Papers from Scholar Inbox (REST API)

scholar-inbox digest --json --limit 20
scholar-inbox config  # get user's research interests

Step A2: AI Filtering

Filter top 5-10 most relevant papers based on user's research interests. Skip already-rated/read papers. If interests are not configured, sort by score and take top 10.

Step A3: Dynamic Classification

Auto-classify papers into NotebookLM notebooks based on title and keywords. Category names are dynamically generated from paper content — no hardcoded categories.

Each category maps to a NotebookLM notebook. Search for existing notebooks:

notebooklm list  # list all notebooks, find matching one by title

If no matching notebook exists, auto-create one:

notebooklm create "<topic>"
# Note the notebook ID from output, then set as active:
notebooklm use <notebook_id>

Step A4: Batch Add Sources to NotebookLM

Add arXiv URLs as sources to the target notebook:

notebooklm use <notebook_id>
notebooklm source add "https://arxiv.org/abs/XXXX.XXXXX"
notebooklm source add "https://arxiv.org/abs/YYYY.YYYYY"
# ... repeat for each paper

Check source status before querying (ensure status is "ready"):

notebooklm source list  # check all sources are "ready" before asking questions

Subagent returns: filtered paper list + classifications + ingestion status

If subagent is killed: Read /tmp/scholar_inbox_results.json for partial results. Continue from where it stopped — don't restart from scratch.

Phase B: Deep Reading [Main Context]

After receiving the paper list from the subagent, query NotebookLM:

notebooklm use <notebook_id>

# Overview
notebooklm ask "Summarize each paper's core contribution (2-3 sentences), label with paper title"

# Method comparison
notebooklm ask "Compare the methodological innovations, technical approaches, and baselines across papers"

# Relevance to user's research
notebooklm ask "How do these papers relate to [user interests]? Which findings are most actionable?"

Follow-up is important: Each notebooklm ask continues the conversation by default. If the answer is incomplete or raises new questions, keep asking. Use --new to start a fresh conversation.

Phase C: Output Reading Report

## YYYY-MM-DD Paper Reading Report (N new papers)

### Category: RL Reward Design

#### 1. Paper Title | Author et al. (Institution)
- **Paper ID**: 4626954 | **Score**: 0.880
- **arXiv**: https://arxiv.org/abs/XXXX.XXXXX
- **Key Findings**: [from NotebookLM, with citation]
- **Method**: [key technical details]
- **Project Relevance**: [how it connects to user's work]

#### 2. ...

---
Upvote: `/scholar-inbox like 1,3`
Downvote: `scholar-inbox rate-batch down <id1> <id2>`

Mode 2: /scholar-inbox <arXiv ID>

1. Fetch paper info with scholar-inbox paper <id> (if paper_id)
2. Dynamically classify into the appropriate notebook by title keywords
3. Add arXiv URL: notebooklm use <notebook_id> && notebooklm source add "https://arxiv.org/abs/XXXX.XXXXX"
4. Wait for indexing: notebooklm source wait
5. Deep-read: notebooklm ask "Summarize this paper's core contribution, method, and key findings"
6. Output single-paper reading report

Mode 3: /scholar-inbox ask "question"

Directly query NotebookLM:

notebooklm ask "question"  # uses current notebook context
# or specify notebook:
notebooklm ask -n <notebook_id> "question"

If no notebook is active, use notebooklm use <id> first or pass -n.

Mode 4: /scholar-inbox like 1,3,5

Extract paper_ids from the most recent reading report by index, then batch upvote via REST API:

scholar-inbox rate-batch up <id1> <id2> <id3>

Basic Mode (No NotebookLM)

For quick browsing when deep reading isn't needed:

scholar-inbox digest --limit 10          # today's paper list
scholar-inbox digest --min-score 0.8     # high-score papers
scholar-inbox paper <id>                 # paper details (with Scholar Inbox AI summary)
scholar-inbox trending --days 7          # trending in past 7 days
scholar-inbox rate <id> up               # upvote
scholar-inbox rate-batch down 111 222    # batch downvote

When displaying papers: Show title, paper_id, score, keywords, one-line contribution, arXiv link.

Notebook Lifecycle

• Notebooks accumulate knowledge across sessions — papers added today can be queried tomorrow
• Source limit: 50/notebook. Check with notebooklm source list. At 40+, warn user; at 50, create "Topic v2"
• Process at most 10 new papers per run

Resilient Parallel Research

当需要多 agent 并行调研（如文献综述、领域调研）时，遵循以下模式防止全军覆没：

1. 最多 2-3 个并行 agent，不要 5+（越多越容易被 kill）
2. 每个 agent 必须增量写文件：/tmp/research_{agent_name}.md，每处理完一个 source 就更新
3. 文件随时可用：写入的 markdown 必须是完整的（不是半截 JSON），即使中断也能直接读
4. merge step 兼容 partial：最终合并时读所有 /tmp/research_*.md，有多少用多少
5. 记录缺口：如果某个 agent 没产出，在报告中标注"未覆盖：XXX 方向"

示例 agent prompt：

研究 [方向]。每分析完一篇论文，立即追加到 /tmp/research_[方向].md。
文件格式：每篇论文一个 ## 标题，包含要点和相关性评估。
确保文件随时是完整可读的 markdown。

Constraints

Verified Behaviors

The following have been verified in production:

• scholar-inbox status / digest / paper / rate / trending / collections
• scholar-inbox doctor --online
• notebooklm list / create / use / ask
• notebooklm source add <url> (single + batch)
• notebooklm auth check --test

Still recommended to test:

• scholar-inbox rate-batch
• scholar-inbox collect
• Larger batch NotebookLM source imports (10+ papers)
• NotebookLM multi-turn follow-up conversations

Error Handling

Run diagnostics:

scholar-inbox doctor              # Scholar Inbox login + basic checks
notebooklm auth check --test      # NotebookLM auth + cookie health

When to Use Browser Instead

• Scholar Maps — interactive visualization
• Full PDF inline — scholar-inbox.com's PDF viewer