# Scholar Monitoring > This skill should be used when the user asks to "monitor a scholar", Use when this capability is needed. - Skill: `tomevault-io/scholar-monitoring` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/scholar-monitoring` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/scholar-monitoring/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/scholar-monitoring --- # Scholar Monitoring Workflow Monitor a researcher's recent publication activity: fetch their profile and papers, optionally analyze output trends, and save a monitoring note. ## Workflow ### Step 1: Check scholar activity Call `check_scholar` with the researcher's name. - Parameters: `scholar_name` (required; use the researcher's full name as commonly published), `max_papers` (default 10; increase to 20–30 for career-wide coverage) - Returns: dict with: - `scholar`: profile dict with `name`, `hIndex`, `citationCount`, `affiliations`, `paperCount`, `url` - `recent_papers`: list of paper dicts (title, abstract, year, venue, citation count) - `candidates`: list of top-3 candidate matches (inspect if the top result is wrong) - If `degraded=True`, the scholar was not found on Semantic Scholar or the API is unavailable ### Step 2: Analyze paper trends (optional) If `recent_papers` is non-empty and the user wants thematic analysis, call `analyze_trends`. - Parameters: `topic` (use the scholar's name or primary research area as the topic), `papers` (the `recent_papers` list from Step 1) - Returns: dict with `trend_analysis` (natural language narrative of the scholar's research focus and evolution) - Skip this step if the user only needs raw paper metadata (no LLM API key required for Step 1 alone) ### Step 3: Save monitoring note Call `save_to_memory` with a summary of the scholar's recent activity. - Parameters: `content` (monitoring summary — include scholar name, hIndex, recent paper titles, and trend analysis if available), `kind="note"`, `user_id` (default `"default"`), `scope_type="global"`, `confidence` (0.0–1.0; suggest 0.9 for factual publication data) - Returns: dict with `created` or `skipped` status ## Note on Scholar Lookup `check_scholar` searches Semantic Scholar by name. Common issues: - **Name diacritics:** Names with accents (e.g., "Müller", "Bengio") may need the ASCII variant ("Muller", "Yoshua Bengio") if exact-match fails - **New researchers:** Very new researchers may have limited or no Semantic Scholar records — check `paperCount` in the returned profile - **Name ambiguity:** The `candidates` field in the response lists the top 3 matches; inspect these if the top result appears to be the wrong person (wrong affiliation, wrong research area) - **Name format:** Use "First Last" format; middle names are generally not needed but can help disambiguate common names ## Degraded Mode `analyze_trends` (Step 2) requires a configured LLM API key. `check_scholar` (Step 1) and `save_to_memory` (Step 3) do not require LLM. When `analyze_trends` returns `degraded=True`: - Set `OPENAI_API_KEY` or `ANTHROPIC_API_KEY` and restart the MCP server - Skip Step 2 and proceed directly to Step 3 with a summary based on raw paper metadata When `check_scholar` itself returns `degraded=True`: - This indicates the scholar was not found or the Semantic Scholar API is unavailable - Try an alternate name spelling or abbreviation - Check `candidates` in the response for close matches --- > Source: [jerry609/PaperBot](https://github.com/jerry609/PaperBot) — distributed by [TomeVault](https://tomevault.io).