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magi-researchers

Three AI models, one synthesis — Claude, Gemini & Codex cross-verify each other for rigorous multi-perspective research

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Atualizado há 6 meses

Ver no GitHub ↗ Licença: MIT

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/plugin marketplace add Axect/magi-researchers

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Ler descrição completa

Research Search Skill

Description

Searches academic literature via OpenAlex (240M+ works) and optionally web sources. Standalone skill for quick literature discovery without the full brainstorm pipeline.

Usage

/research-search "topic" [--filter "..."] [--sort cited_by_count:desc|relevance_score:desc|publication_year:desc] [--limit N] [--web] [--save path/to/output.md]

Arguments

  • $ARGUMENTS — The search topic (required) and optional flags:
  • --filter — OpenAlex filter string (default: "publication_year:>2021,type:article|review"). Valid types: article, review, preprint, book-chapter, book, dataset, dissertation, report.
  • --sort — Sort order (default: relevance_score:desc). Options: relevance_score:desc, cited_by_count:desc, publication_year:desc.
  • --limit — Number of results (default: 10, max: 50).
  • --web — Also run a WebSearch for recent developments (default: false).
  • --save — Save results to a file instead of displaying inline. Accepts a relative or absolute path.

Instructions

Shared rules: Read ${CLAUDE_PLUGIN_ROOT}/shared/rules.md before starting. §LaTeX applies to this skill.

When this skill is invoked, follow these steps:

Step 1: Parse Arguments

  1. Extract the search topic from $ARGUMENTS.
  2. Parse optional flags with defaults:
  3. filter: "publication_year:>2021,type:article|review"
  4. sort: relevance_score:desc
  5. limit: 10
  6. web: false
  7. save: null (display inline)

Step 2: Academic Literature Search (OpenAlex)

Run the bundled OpenAlex search script:

uv run python ${CLAUDE_PLUGIN_ROOT}/skills/research-brainstorm/scripts/openalex_search.py "{topic}" --filter "{filter}" --sort "{sort}" --limit {limit} --format md

If results are sparse (< 3 papers):

  1. Simplify the query: drop domain-specific jargon, keep core concepts.
  2. Retry with broader filter: --filter "publication_year:>2019,type:article|review|preprint".
  3. If still sparse, retry without type filter: --filter "publication_year:>2019".

Step 3: Web Search (optional)

If --web is specified:

WebSearch(query: "{topic} recent advances 2024 2025 2026")

Extract the top 5 web results with source URLs.

Step 4: Format and Present Results

Combine results into a structured output:

## Literature Search: "{topic}"

### Academic Papers (OpenAlex)
**Filter**: {filter} | **Sort**: {sort} | **Results**: {count}

[numbered list from openalex_search.py output]

### Recent Developments (Web)
[key findings with source URLs — only if --web]

### Summary
- Total academic papers found: {N}
- Most cited: "{title}" ({year}, {citations} citations)
- Year range: {min_year}–{max_year}
- Dominant themes: [2-3 key themes from abstracts]

Step 5: Save or Display

  • If --save is specified: write the formatted output to the given path.
  • Otherwise: display inline in the conversation.
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