Obsidian-vault-agent paper-discover

<Purpose> Search academic databases (Semantic Scholar) for papers relevant to a topic or related to an existing vault note. Returns ranked results with relevance assessment against vault content. Optionally creates formatted paper notes in the vault's inbox for processing. </Purpose>

<Use_When>

• User asks to find papers on a topic ("find papers about scaling laws")
• User wants papers related to an existing note ("what papers connect to this?")
• User is /processing a paper and wants to find related work
• User asks "what should I read about X?"
• User provides a DOI or paper title and wants similar papers
• User wants to fill gaps in a knowledge domain </Use_When>

<Do_Not_Use_When>

• User has a paper file to analyze (use /paper or /book-analyzer)
• User wants to process an existing vault note (use /process)
• User wants general web research, not academic papers (use /research) </Do_Not_Use_When>

<Execution_Policy>

• Search first, present results, then create notes only if user approves
• Always check vault for existing paper notes before creating duplicates
• Rank by vault relevance, not just citation count
• Cap at 10 results per search — quality over quantity
• Respect Semantic Scholar rate limits (100 req/5min) </Execution_Policy>

<Steps>

Stage 1: PARSE QUERY AND CONTEXT

Determine the search mode from user input:

Mode A — Topic search (default): User provides a topic or question. Extract search terms.

"find papers about scaling laws for LLMs" → query: "scaling laws large language models"

Mode B — Similar papers: User references an existing vault note or provides a paper ID/DOI.

1. Read the referenced note to extract title, key concepts
2. Use the paper's Semantic Scholar ID or DOI for recommendations
3. Fall back to keyword search if no ID available

Mode C — Gap filling: User asks about a domain. Search vault first to identify what's covered, then search for papers on uncovered subtopics.

Stage 2: SEARCH

Run the search script:

SKILL_DIR="${CLAUDE_SKILL_DIR}"

# Topic search
python3 "$SKILL_DIR/scripts/search_papers.py" "QUERY" --limit 20 --min-citations 5

# With year filter
python3 "$SKILL_DIR/scripts/search_papers.py" "QUERY" --limit 20 --year-from 2020

# Get recommendations from a paper
python3 "$SKILL_DIR/scripts/search_papers.py" "" --recommend-from "DOI:10.xxxx/xxxxx"

# Look up specific paper
python3 "$SKILL_DIR/scripts/search_papers.py" "" --paper-id "DOI:10.xxxx/xxxxx"

Script returns JSON array of papers with: title, authors, year, abstract, tldr, citation_count, influential_citations, url, doi, arxiv_id, fields_of_study.

Stage 3: RANK AND PRESENT

Read the agent definition from

agents/paper-writer.md

in the skill directory.

Search the vault for existing paper notes on this topic using the MCP tool:

search_notes(query="KEYWORD", limit=20)

Or fall back to Grep if MCP is unavailable:

Grep(pattern="KEYWORD", path="notes/", glob="*.md", head_limit=20)

Launch the paper-writer agent to rank and present:

Agent(
  subagent_type="general-purpose",
  model="sonnet",
  run_in_background=false,
  prompt="You are Paper Writer. Follow these instructions exactly:

  [INSERT FULL CONTENT OF agents/paper-writer.md HERE]

  SEARCH CONTEXT:
  - User query: [original query]
  - Search mode: [topic/similar/gap]
  - Vault topics: [detected from search/tags]

  EXISTING VAULT PAPERS:
  [List any matching paper notes already in the vault]

  SEARCH RESULTS:
  [INSERT JSON FROM STAGE 2 HERE]

  Rank these papers by relevance to the vault's existing knowledge.
  Present the top 10 with your assessment. Do NOT create notes yet —
  just present the ranked list for user triage."
)

Stage 4: CREATE NOTES (on user approval)

After user selects which papers to add:

For each selected paper, create a vault note:

1. Generate timestamp ID:

   date +%Y%m%d%H%M%S

2. Create the note file with proper frontmatter and body
3. Check for duplicate titles before creating

Note template:

---
id: YYYYMMDDHHMMSS
created_date: YYYY-MM-DD
updated_date: YYYY-MM-DD
type: paper
category:
link: [paper URL]
processing_status: inbox
---

# Title
- **🏷️Tags** : #paper #topic-tag #MM-YYYY

[ ](#anki-card)

## Abstract
- [Synthesized abstract bullets]

## Notes

## Questions

## Related links
- [Paper URL](url)
- [[(Type) Related Vault Note]]

Place the note in

notes/paper/

(or appropriate topic folder).

</Steps>

<Tool_Usage>

• Bash: Run search_papers.py script, generate timestamps
• Read: Read agent definition, read existing vault notes for context
• Write/Edit: Create paper notes in vault
• Agent: Delegate ranking/presentation to paper-writer (sonnet)
• Grep/Glob: Search vault for existing papers and related notes
• TodoWrite: Track progress through stages </Tool_Usage>

<Examples> <Good> User: "find papers about scaling laws for neural networks" 1. Search → 20 results from Semantic Scholar 2. Check vault → found 3 existing scaling law papers 3. Agent ranks → filters to 8 new papers, ranked by vault relevance 4. Present: "Found 8 papers you don't have. Top pick: 'Chinchilla' (2022, 4200 citations) — connects to your [[Scaling Laws]] and [[Compute-Optimal Training]]" 5. User picks 3 → create 3 paper notes in notes/paper/ with inbox status </Good> <Good> User: "what papers are related to this one?" (while viewing a paper note) 1. Read current note → extract title, DOI 2. Get recommendations via Semantic Scholar API 3. Check vault → filter out papers already present 4. Present ranked list with connections to existing notes </Good> <Bad> User: "find papers about ML" - Too broad — should ask: "ML is a wide field. What aspect? Scaling laws, reinforcement learning, attention mechanisms, optimization...?" </Bad> </Examples>

<Escalation_And_Stop_Conditions>

• No results: Try broader query, suggest alternative terms
• API rate limited: Wait and retry (script handles this automatically)
• Query too broad: Ask user to narrow down the topic
• All results already in vault: Report "your vault already covers this well"
• Network error: Inform user, suggest trying again later </Escalation_And_Stop_Conditions>

$ARGUMENTS