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Cite-Assist Research

Semantic search across Zotero library using cite-assist v3 API for academic research

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SKILL.md

Full skill instructions

Cite-Assist Research Skill

Domain: Academic research and citation retrieval Version: 1.0.0 Last Updated: 2025-12-15

Overview

Cite-assist provides semantic search across a Zotero library with ~224 documents and ~41k text chunks. It uses ModernBERT-large embeddings with MPS GPU acceleration for fast, accurate retrieval of relevant passages and document summaries.

API Endpoint

POST http://localhost:8000/​api/​v3/​search

Request Format

{
  "query": "prosecutorial discretion",
  "library_id": 5673253,
  "max_results": 10,
  "min_score": 0.3,
  "output_mode": "auto",
  "weights": {"chunk": 0.8, "summary": 0.2}
}

Parameters

ParameterRequiredDefaultDescription
queryYes-Natural language search text
library_idYes5673253Zotero library ID
max_resultsNo10Maximum results to return (1-50)
min_scoreNo0.3Minimum similarity threshold (0.0-1.0)
output_modeNoautoResult format (see Output Modes)
weightsNo{chunk: 0.8, summary: 0.2}Score weighting between chunk and summary matches

Output Modes

ModeReturnsBest For
autoChunks OR summaries (whichever scores higher)General research, exploratory searches
chunksOnly text chunks with contextFinding quotable passages, specific evidence
summariesOnly document summariesDocument-level matching, literature review
bothBoth chunk + summary per resultComprehensive research, need context + evidence

Mode Selection Guide

Use auto when:

  • Starting research on a new topic
  • Unsure whether you need passages or document overviews
  • Want the system to optimize for relevance

Use chunks when:

  • Need specific quotable passages
  • Looking for particular arguments or evidence
  • Building support for a specific claim

Use summaries when:

  • Conducting literature review
  • Need to understand what documents cover a topic
  • Looking for documents to read in full

Use both when:

  • Need comprehensive research results
  • Want to see both the specific passage and document context
  • Evaluating multiple sources for depth

Response Format

{
  "results": [
    {
      "id": "SCC7P3FV",
      "title": "Progressive Prosecution",
      "result_type": "chunk",
      "score": 0.85,
      "chunk_text": "Prosecutorial discretion allows...",
      "chunk_index": 5,
      "summary": "This article examines...",
      "chunk_score": 0.85,
      "summary_score": 0.60
    }
  ]
}

Response Fields by Mode

Fieldautochunkssummariesboth
idYesYesYesYes
titleYesYesYesYes
result_typeYes"chunk""summary""both"
scoreYesYesYesYes
chunk_textMaybeYesNoYes
chunk_indexMaybeYesNoYes
summaryMaybeNoYesYes
chunk_scoreMaybeYesNoYes
summary_scoreMaybeNoYesYes

Performance Characteristics

MetricValue
Mean response time~372ms
Embedding generation~200-300ms (MPS GPU)
Qdrant search~100ms
First request warmup2-3s (model loading)

Research Workflow

1. Exploratory Search

Start broad with auto mode to understand what's available:

{
  "query": "criminal justice reform",
  "output_mode": "auto",
  "max_results": 15
}

2. Targeted Evidence Search

Narrow to specific passages with chunks mode:

{
  "query": "prosecutorial discretion reduces racial disparities",
  "output_mode": "chunks",
  "min_score": 0.5,
  "max_results": 10
}

3. Literature Review

Use summaries mode to map the field:

{
  "query": "progressive prosecution movement",
  "output_mode": "summaries",
  "max_results": 20
}

4. Comprehensive Research

Use both mode for thorough analysis:

{
  "query": "plea bargaining coercion",
  "output_mode": "both",
  "max_results": 10
}

Adjusting Weights

The weights parameter controls how chunk vs. summary scores contribute to final ranking:

{
  "weights": {"chunk": 0.8, "summary": 0.2}
}

High chunk weight (default): Prioritizes specific passage matches High summary weight: Prioritizes document-level relevance

Adjust for your research needs:

  • {"chunk": 1.0, "summary": 0.0} - Pure passage search
  • {"chunk": 0.5, "summary": 0.5} - Balanced search
  • {"chunk": 0.2, "summary": 0.8} - Document-focused search

Integration with Bluebook

After retrieving sources, format citations using the Academic Bluebook skill:

See `.claude/​skills/​academic-bluebook/​SKILL.md` for citation formatting rules.

Key integration points:

  • Use document title for article titles (italicize in academic format)
  • Use id (Zotero key) to retrieve full metadata if needed
  • Apply appropriate signals based on how source supports your argument

Collections Reference

CollectionContentsUse
text_chunks~41k semantic chunksPassage search
document_sync_status224 documents + summariesDocument search

Error Handling

ErrorCauseResolution
Connection refusedcite-assist server not runningStart server on port 8000
Empty resultsQuery too specific or min_score too highBroaden query, lower min_score
TimeoutFirst request loading modelWait for warmup, retry

Available Workflows

  • workflows/​exploratory-search.md - Initial research on a topic
  • workflows/​evidence-gathering.md - Finding supporting passages
  • workflows/​literature-review.md - Mapping sources on a topic

Cite-assist enables semantic research across your academic library.

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