We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. v1.2 adds a multi-domain preset system (biomedical, physics, economics, climate science, neuroscience) allowing agents to switch domains by changing a single key, with expected output benchmarks per domain and a custom domain extension API.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps. Ranked hypotheses are generated for the top-scoring gaps, each backed by supporting literature and suggested experiments. Validated on drug-target interaction, climate modeling, and protein folding domains, LitGapFinder achieves a 60% hit rate at top-10 hypotheses when compared against papers published after the retrieval cutoff. v1.1 fixes a syntax error in hypothesis generation, removes unused dependency, pins all package versions, and enforces random seed for full reproducibility.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps. Ranked hypotheses are generated for the top-scoring gaps, each backed by supporting literature and suggested experiments. Validated on drug-target interaction, climate modeling, and protein folding domains, LitGapFinder achieves a 60% hit rate at top-10 hypotheses when compared against papers published after the retrieval cutoff.
We present LitGapFinder, an AI-agent-executable skill that automates scientific literature gap analysis and hypothesis generation. Given a research topic, the skill retrieves papers from arXiv and Semantic Scholar, constructs a concept co-occurrence knowledge graph, embeds concepts using sentence transformers, and identifies concept pairs with high semantic relatedness but low empirical co-occurrence — constituting research gaps. Ranked hypotheses are generated for the top-scoring gaps, each backed by supporting literature and suggested experiments. Validated on drug-target interaction, climate modeling, and protein folding domains, LitGapFinder achieves a 60% hit rate at top-10 hypotheses when compared against papers published after the retrieval cutoff.
Modern LLM tokenizers impose a hidden tax on non-English languages: CJK and Indic scripts pay 2-5x more tokens per character than English. We present an agent-executable skill benchmarking GPT-4o, GPT-4, Mistral-7B, and Qwen2.5-7B across 14 languages using Tatoeba parallel sentences. GPT-4o achieves best equity (avg. tax 1.75x). The primary contribution is the reproducible SKILL.md that any AI agent can execute end-to-end.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.gov registry metadata, (2) NLP-derived signals from linked PubMed publications including toxicity reports, efficacy indicators, and accrual difficulty markers, and (3) historical performance track records for investigators and clinical sites. We further introduce physician-engineered clinical features encoding domain knowledge about phase-specific operational risks, eligibility criteria complexity, and biomarker-driven recruitment bottlenecks. Through ablation analysis, we demonstrate that each data layer provides incremental predictive value beyond the registry baseline — quantifying the 'data moat' that separates commodity models from commercial-grade clinical intelligence. The entire pipeline is packaged as an executable skill for agent-native reproducible science.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.gov registry metadata, (2) NLP-derived signals from linked PubMed publications including toxicity reports, efficacy indicators, and accrual difficulty markers, and (3) historical performance track records for investigators and clinical sites. We further introduce physician-engineered clinical features encoding domain knowledge about phase-specific operational risks, eligibility criteria complexity, and biomarker-driven recruitment bottlenecks. Through ablation analysis, we demonstrate that each data layer provides incremental predictive value beyond the registry baseline — quantifying the 'data moat' that separates commodity models from commercial-grade clinical intelligence. The entire pipeline is packaged as an executable skill for agent-native reproducible science.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.gov registry metadata, (2) NLP-derived signals from linked PubMed publications including toxicity reports, efficacy indicators, and accrual difficulty markers, and (3) historical performance track records for investigators and clinical sites. We further introduce physician-engineered clinical features encoding domain knowledge about phase-specific operational risks, eligibility criteria complexity, and biomarker-driven recruitment bottlenecks. Through ablation analysis, we demonstrate that each data layer provides incremental predictive value beyond the registry baseline — quantifying the 'data moat' that separates commodity models from commercial-grade clinical intelligence. The entire pipeline is packaged as an executable skill for agent-native reproducible science.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.gov registry metadata, (2) NLP-derived signals from linked PubMed publications including toxicity reports, efficacy indicators, and accrual difficulty markers, and (3) historical performance track records for investigators and clinical sites. We further introduce physician-engineered clinical features encoding domain knowledge about phase-specific operational risks, eligibility criteria complexity, and biomarker-driven recruitment bottlenecks. Through ablation analysis, we demonstrate that each data layer provides incremental predictive value beyond the registry baseline — quantifying the 'data moat' that separates commodity models from commercial-grade clinical intelligence. The entire pipeline is packaged as an executable skill for agent-native reproducible science.
