跳到正文
FunCoding

搜索

搜索文档、Skill 和 MCP

02-bib-verify

Verify a BibTeX file for hallucinated or fabricated references by cross-checking every entry against CrossRef, arXiv, and DBLP. Reports each reference as verified, suspect, or not found, with field-level mismatch details (title, authors, year, DOI). Use when the user wants to check a .bib file for fake citations, validate references in a paper, or audit bibliography entries for accuracy.

科研868skills/academic-eval/02-bib-verify/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/agentscope-ai/openjudge/02-bib-verify/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

BibTeX Verification Skill

Check every entry in a .bib file against real academic databases using the OpenJudge PaperReviewPipeline in BibTeX-only mode:

  1. Parse — extract all entries from the .bib file
  2. Lookup — query CrossRef, arXiv, and DBLP for each reference
  3. Match — compare title, authors, year, and DOI
  4. Report — flag each entry as verified, suspect, or not_found

Prerequisites

pip install py-openjudge litellm

Gather from user before running

InfoRequired?Notes
BibTeX file pathYes.bib file to verify
CrossRef emailNoImproves CrossRef API rate limits

Quick start

# Verify a standalone .bib file
python -m cookbooks.paper_review --bib_only references.bib

# With CrossRef email for better rate limits
python -m cookbooks.paper_review --bib_only references.bib --email [email protected]

# Save report to a custom path
python -m cookbooks.paper_review --bib_only references.bib \
  --email [email protected] --output bib_report.md

Relevant options

FlagDefaultDescription
--bib_only—Path to .bib file (required for standalone verification)
--email—CrossRef mailto — improves rate limits, recommended
--outputautoOutput .md report path
--languageenReport language: en or zh

Interpreting results

Each reference entry is assigned one of three statuses:

StatusMeaning
verifiedFound in CrossRef / arXiv / DBLP with matching fields
suspectTitle or authors do not match any real paper — likely hallucinated or mis-cited
not_foundNo match in any database — treat as fabricated

Field-level details are shown for suspect entries:

  • title_match — whether the title matches a real paper
  • author_match — whether the author list matches
  • year_match — whether the publication year is correct
  • doi_match — whether the DOI resolves to the right paper

Additional resources

相似的 Skill

lead-research-assistant
ComposioHQ/awesome-claude-skills77k

lead-research-assistant

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

科研

13c-metabolic-flux
K-Dense-AI/scientific-agent-skills48k

13c-metabolic-flux

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

科研

datamol
K-Dense-AI/scientific-agent-skills48k

datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

科研

biopython
K-Dense-AI/scientific-agent-skills48k

biopython

Provides Biopython workflows for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Supports batch processing, custom molecular-biology pipelines, BLAST automation, structure analysis, and motif analysis.

科研

bulk-rnaseq
K-Dense-AI/scientific-agent-skills48k

bulk-rnaseq

Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.

科研

alphagenome
K-Dense-AI/scientific-agent-skills48k

alphagenome

Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

科研