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Research evidence

Skill sidiangongyuan/codex-skills-library/skills/research-evidence

Use when research writing, reviews, rebuttals, or related-work planning needs academic evidence. Searches literature, screens candidate papers, audits novelty and missing work, verifies DOI/BibTeX/reference metadata and citation authenticity, and checks whether sources support the stated claims.From its SKILL.md

Install
npx -y skills add sidiangongyuan/codex-skills-library --skill research-evidence

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  • 29 days oldThe repository was created 29 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

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The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.0 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

Research Evidence

Overview

Use this skill as the shared evidence layer for paper writing, related work, review, and rebuttal work. It does not replace domain judgment: it finds and checks evidence, then reports support level, gaps, and risks.

Hard Boundaries

  • Use public metadata, open-access PDFs, and user-provided local PDFs only.
  • Do not use Sci-Hub or any route that bypasses access control.
  • Do not write Markdown, JSON, CSV, or report files unless the user explicitly asks for saved artifacts.
  • Run RefChecker in non-LLM mode by default. Enable LLM-assisted extraction or hallucination checks only when the user explicitly requests it and accepts the privacy/cost tradeoff.
  • Treat tool output as evidence to inspect, not final truth. Mark weak coverage, metadata mismatches, and unsupported claims clearly.

Venue Defaults

Prefer these venues when the user does not specify otherwise:

  • Computer Vision: CVPR, ICCV, ECCV.
  • ML/AI: ICLR, NeurIPS/NIPS, ICML.
  • Autonomous driving, robotics, and collaborative perception secondary venues: CoRL, ICRA, IROS, AAAI, IJCAI, T-ITS, RA-L.

Do not let secondary venues outrank the primary top-conference set unless the user's task or subfield demands it.

Workflows

Literature Search

  1. Translate the research question into compact queries with venue/year terms when useful.
  2. Use the dedicated tool environment from references/tooling.md; start with arXiv metadata search for CV/ML topics.
  3. Return a compact candidate list with title, source, year/date, identifier, URL, and why each candidate is relevant.
  4. Label coverage risk when search sources are unavailable, too broad, too recent, or missing known top-venue work.

Recent Literature / Novelty Risk Audit

Use this workflow when the user asks about novelty, recent work, missing related work, reviewer risk, rebuttal readiness, score prediction, first/SOTA/new benchmark claims, or whether a close paper conflicts with the current claim.

  1. Decompose the claim into axes before searching: domain/input, method family, task/output, evaluation protocol, dataset/benchmark, and reliability or failure mode.
  2. Expand the search across direct terms, synonyms, neighboring tasks, older terminology, and broad recent-work queries. Do not stop at the user's exact phrasing.
  3. For high-stakes checks, cross-check at least two evidence routes: direct arXiv API or official venue metadata; local reference/, PDF, BibTeX, or LaTeX sources; and the local paper-search wrapper as a smoke or secondary check.
  4. Classify candidates after inspecting metadata and abstracts: direct competitor, claim limiter, table candidate, prose citation, background, or irrelevant.
  5. Report the search matrix, source-coverage limits, and claim-impact verdict. Never answer "not found" without stating which query families and sources were checked.

Read references/recent-literature-audit.md for the full checklist. Use scripts/arxiv_query_matrix.py when a repeatable arXiv query matrix would make the search less ad hoc.

Citation Check

  1. Prefer local PDF, LaTeX, BibTeX, or reference-list files supplied by the user.
  2. Run RefChecker without LLM flags unless explicitly requested.
  3. Summarize reference authenticity, metadata mismatches, missing fields, suspicious entries, and false-positive risk.
  4. Do not automatically rewrite references. Recommend exact follow-up checks or edits.

Claim Evidence Check

  1. Extract the claim and required support type: paper text, citation, experiment result, table/figure, code/log artifact, or user-confirmed fact.
  2. Search or verify only the minimum evidence needed for the claim.
  3. Report one of: verified, likely, unsupported, or needs user input.
  4. Do not strengthen claims based on weak evidence. Recommend weakening, adding a citation, deleting the claim, or asking the user for missing evidence.

Output Shape

Default response shape:

  • query: search or verification intent.
  • sources: tools/sources used and key records found.
  • verdict: verified, likely, unsupported, or needs user input.
  • risks: source coverage, metadata mismatch, citation error, weak venue fit, or stale evidence.
  • next action: write, weaken, cite, delete, continue search, or ask the user.

For novelty-risk audits, include:

  • search matrix: query families and evidence routes checked.
  • candidate classification: direct competitor, claim limiter, table candidate, prose citation, background, or irrelevant.
  • claim impact: keep, weaken, add citation/table row, revise positioning, or avoid first/SOTA language.
  • residual risk: what the search still may have missed.

Tooling

Read references/tooling.md before running installed commands or changing tool behavior.

What ships with it: 5 files

15.8 KB alongside SKILL.md, 1 of them executable

agents/

scripts/

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