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FengYM666/AI-Research-OS

Turn your AI coding agent into an academic research copilot: anti-hallucination discipline + full-stack skills for top-tier conference papers + literature search MCP.

What is AI-Research-OS?

This skill transforms any AI coding agent (e.g., Claude Code, Cursor) into a rigorous academic research assistant. Its core purpose is to enforce a strict 'no hallucination' policy when searching for and citing scholarly papers. Rather than relying on the model's training memory—which often invents plausible but incorrect authors, titles, and venues—it mandates that every recommended paper must come from actual, verifiable retrieval records. The skill provides a complete workflow: it pre-configures the paper-search MCP server (with 57 tools covering OpenAlex, arXiv, Google Scholar, Semantic Scholar, DBLP, and more), offers fallback manual curl commands for each source, and defines a step-by-step pipeline for breaking down queries, cross-referencing publication venues using DBLP or official conference websites, and checking for code repositories via Papers with Code. Output is structured as a table with the five essential citation elements (title, authors, conference/journal + year, link, relevance). This makes it invaluable for any AI-powered development tool where the user needs accurate, verifiable literature references, especially in computer science and engineering domains.

Works with~Claude Code~Codex CLI~Cursor
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Documentation

Paper Search Specification (Paper Search)

The user's primary job is academic research. When searching for papers for them, accuracy > quantity > speed.

Iron Rule: No Fabrication

  1. Every recommended paper must come from an actual record retrieved in the current session (API response or webpage content). Do not list papers from training memory—names, titles, and years from memory are extremely prone to being garbled.
  2. The five essential citation elements must all be complete and come from the same actual record: title, authors, year, publication venue, accessible link.
  3. If nothing is found, simply say "not found"; if relying on memory without verification, mark it as [Unverified] and explain why.
  4. Provide the exact search terms used so the user can reproduce and dig deeper.
  5. Distinguish between the formally published version and the arXiv preprint; if titles match, prioritize the formally published information.

Primary Tool: paper-search MCP (Configured)

ZCode is already connected to the paper-search MCP server (openags/paper-search-mcp, 57 tools). Prefer calling its tools for retrieval instead of using curl:

  • search_papers (cross-database unified search), search_arxiv, search_openalex, search_dblp, search_semantic, search_google_scholar, search_crossref, etc. — for searches across 20 sources
  • download_arxiv / read_arxiv_paper, etc. — for downloading PDFs, extracting text (each source has corresponding tools)
  • Google Scholar search is already configured with a proxy (PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL); if the proxy is not active, GS will fail—just switch to another source

When MCP is unavailable (e.g., the server is not connected), fall back to the manual curl approach below.

Sources and Connectivity (Tested Locally on 2026-09)

Directly accessible (no proxy needed from terminal):

SourcePurposeEntry Point
OpenAlex APIPrimary choice: free, no key required, covers 250M+ papers, filterable by date/venue/citationshttps://api.openalex.org/works?search=<term>&per-page=10, add &filter=from_publication_date:YYYY-01-01 to limit by year
arXiv APIPreprints, where CV/AI new papers are first publishedhttps://export.arxiv.org/api/query?search_query=all:<term>&max_results=10&sortBy=submittedDate
CVF Open AccessOfficial full text for CVPR / ICCV / ECCV, including workshopsopenaccess.thecvf.com
dblp APIThe gold standard for verifying the actual publication venue and year of a paperhttps://dblp.org/search/publ/api?q=<title>&format=json
Papers with CodePapers + official code + SOTA leaderboardspaperswithcode.com
Domain official librariesACL Anthology (NLP), proceedings.neurips.cc, proceedings.mlr.press (ICML), OpenReview (ICLR)respective official websites

Requires a proxy (first run export https_proxy=http://127.0.0.1:7892 in terminal; port may vary based on current registry):

SourcePurposeNote
Google ScholarWidest coverage, citation counts, citation tracking, "related articles" expansionPriority: use control-browser skill to open a real browser and search (system proxy takes effect automatically, can bypass CAPTCHAs); curl scraping easily triggers CAPTCHA
Semantic Scholar APICitation network analysis, TLDR summariesNo key → heavy rate limiting (easily hits 429); usable for small requests only — use as fallback

Workflow

  1. Deconstruct search terms: Break the user's request into 2–4 English search queries (academic databases mostly only recognize English; translate Chinese keywords first).
  2. Initial filter: Search OpenAlex + arXiv, select candidates based on relevance, year, and citation count.
  3. Verify venue: For papers claimed to be published at CVPR/ICCV, etc., check against dblp or the conference's official website — this is the specific step to kill "plausible-looking fake papers."
  4. Find code/SOTA: Papers with Code.
  5. Expand with classics: Google Scholar sorted by citations + "related articles" + crawl reference lists.
  6. Before output, check each entry against the five elements; if any is missing, go back and look it up.

Output Format

TitleAuthorsConference/Journal + YearLinkRelevance (one sentence)

Following the table: search terms used this session; if there is official code, include the code repository link; indicate which entries were not yet verified.

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