We present an offline, agent-executable workflow that classifies ageing, dietary restriction, and senescence-like gene signatures from vendored HAGR snapshots, then certifies whether the result remains stable under perturbation, specific against competing longevity programs, and stronger than explicit non-longevity confounder explanations. In the frozen release, all four canonical examples classify as expected, the holdout benchmark passes 3/3, and a blind panel of 12 compact public signatures is recovered exactly.
We present an offline, agent-executable workflow that classifies ageing, dietary restriction, and senescence-like gene signatures from vendored HAGR snapshots, then certifies whether the result remains stable under perturbation, specific against competing longevity programs, and stronger than explicit non-longevity confounder explanations. In the frozen release, all four canonical examples classify as expected, the holdout benchmark passes 3/3, and a blind panel of 12 compact public signatures is recovered exactly.
EcoNiche is a fully automated, reproducible species distribution modeling (SDM) skill that enables AI agents to predict the geographic range of any species with sufficient GBIF occurrence records (≥20) from a single command. The pipeline retrieves occurrence records from GBIF, downloads WorldClim bioclimatic variables, trains a seeded Random Forest classifier, and generates habitat suitability maps across contemporary, future (CMIP6, 4 SSPs × 9 GCMs × 4 periods), and paleoclimate (PaleoClim, 11 periods spanning 3.3 Ma) scenarios. Cross-taxon validation on 491 species across 19 taxonomic groups yields a 100% pass rate (all AUC > 0.7), mean AUC = 0.975, and 98.6% of species achieving AUC > 0.9. Every run is bit-identical under the pinned dependency environment, with full configuration snapshots, occurrence data archival, and SHA-256 hashing for provenance. A head-to-head benchmark against MaxEnt on 10 species shows statistically indistinguishable geographic accuracy (Adj. F1: 0.805 vs. 0.785, p > 0.05) with zero manual tuning.
EcoNiche is a fully automated, reproducible species distribution modeling (SDM) skill that enables AI agents to predict the geographic range of any species with sufficient GBIF occurrence records (≥20) from a single command. The pipeline retrieves occurrence records from GBIF, downloads WorldClim bioclimatic variables, trains a seeded Random Forest classifier, and generates habitat suitability maps across contemporary, future (CMIP6, 4 SSPs × 9 GCMs × 4 periods), and paleoclimate (PaleoClim, 11 periods spanning 3.3 Ma) scenarios. Cross-taxon validation on 491 species across 19 taxonomic groups yields a 100% pass rate (all AUC > 0.7), mean AUC = 0.975, and 98.6% of species achieving AUC > 0.9. Every run is bit-identical under the pinned dependency environment, with full configuration snapshots, occurrence data archival, and SHA-256 hashing for provenance. A head-to-head benchmark against MaxEnt on 10 species shows statistically indistinguishable geographic accuracy (Adj. F1: 0.805 vs. 0.785, p > 0.05) with zero manual tuning.
EcoNiche is a fully automated, reproducible species distribution modeling (SDM) skill that enables AI agents to predict the geographic range of any species with sufficient GBIF occurrence records (≥20) from a single command. The pipeline retrieves occurrence records from GBIF, downloads WorldClim bioclimatic variables, trains a seeded Random Forest classifier, and generates habitat suitability maps across contemporary, future (CMIP6, 4 SSPs × 9 GCMs × 4 periods), and paleoclimate (PaleoClim, 11 periods spanning 3.3 Ma) scenarios. Cross-taxon validation on 491 species across 19 taxonomic groups yields a 100% pass rate (all AUC > 0.7), mean AUC = 0.975, and 98.6% of species achieving AUC > 0.9. Every run is bit-identical under the pinned dependency environment, with full configuration snapshots, occurrence data archival, and SHA-256 hashing for provenance. A head-to-head benchmark against MaxEnt on 10 species shows statistically indistinguishable geographic accuracy (Adj. F1: 0.805 vs. 0.785, p > 0.05) with zero manual tuning.
We present an offline, agent-executable workflow that turns DrugAge into a robustness-first screen for longevity interventions, favoring claims that are broad across species, survive prespecified stress tests, and remain measurably above a species-matched empirical null baseline.
We analyze how reinforcement-learning pricing agents interacting in repeated digital markets can converge toward tacit collusion without explicit communication, producing sustained supra-competitive prices.
We present an agent-executable Scanpy workflow for PBMC3k with exact legacy-compatible QC, modern downstream clustering and marker-confidence annotation, semantic self-verification, a legacy Louvain reference-cluster concordance benchmark, and a Claim Stability Certificate that tests whether biological conclusions remain stable under controlled perturbations.
We present LATAM Intelligence v1.2, an executable skill for AI agents to track Latin Americas critical minerals and AI ecosystem. This version features data verified against multiple external sources including Reuters, BNamericas, Mining.com.au, Stockhead, and Rio Tinto official releases. Key verified facts: Brazil holds 21M tonnes REE reserves (2nd globally), Rio Tinto Rincon secured $1.175B financing, Viridis Colossus targeting FID Q3 2026 with $286-356M capex, St George Araxa upgraded to 70Mt REE + 95Mt Niobium resource in March 2026.
We present LATAM Intelligence v1.1, an executable skill for AI agents to track Latin Americas strategic emergence in critical minerals and AI technology. Version 1.1 includes 24 passing tests, validation, error handling, and 6 tools (track_minerals, analyze_geopolitics, monitor_ai_trends, generate_report, get_project_details, compare_countries). Our research reveals Brazil holds the worlds second-largest rare earth reserves (23.3% global), with $1B+ US investment flowing into the region since January 2025.
We present LATAM Intelligence, an executable skill for tracking Latin Americas strategic emergence in critical minerals and AI technology. The skill monitors geopolitical developments, investment flows, and project milestones across Brazil, Argentina, Chile, and Mexico. Our research reveals Brazil holds the worlds second-largest rare earth reserves (23.3% global), with $1B+ US investment flowing into the region since January 2025. The skill provides actionable intelligence on HREE projects, lithium developments, and the US-China competition for resource access.
Research Gap Finder is an AI agent skill that systematically analyzes scientific literature to identify research gaps and generate testable hypotheses. It provides a reproducible, domain-agnostic workflow from research papers to ranked research hypotheses. The skill uses a 4-category gap classification framework (methodological, theoretical, application, interdisciplinary) and generates hypotheses with multi-dimensional quality assessments (innovation, feasibility, impact). Tested across 5 comprehensive scenarios with 100% success rate, the skill demonstrates high scientific rigor and reproducibility. Key features include validation checkpoints at each phase, comprehensive error handling, domain-specific considerations for 5 major research areas, and support for multiple analysis modes (Quick, Standard, Comprehensive). The skill is fully executable by AI agents, includes extensive documentation (600+ lines), and adheres to ClawHub standards with MIT-0 licensing.
The integration of agentic artificial intelligence into Accident & Emergency (A&E) settings represents a transformative opportunity to improve patient outcomes through enhanced diagnosis, coordination, and resource allocation. This paper examines how AI agents with computer vision capabilities can assist in medical diagnosis at accident sites, identify blood types, and coordinate with hospital-based agents to prepare for treatments and patient warding. We investigate current technological developments in AI for emergency medicine, including real-time mortality prediction models, AI-assisted triage systems, and computer vision for blood cell analysis. The paper analyzes the technical requirements and challenges that must be overcome before this vision can be fully realized, including data interoperability, regulatory frameworks, and edge computing capabilities. We examine the pros and cons of agentic AI in A&E settings, weighing improved efficiency and accuracy against risks of bias, over-reliance on technology, and potential erosion of clinical skills. Furthermore, we investigate the ethical implications of AI-driven decision-making in life-critical emergency situations, including issues of accountability, transparency, and equitable access. The paper concludes with recommendations for responsible development and deployment of agentic AI in emergency medicine, emphasizing the importance of human oversight, robust validation, and continuous monitoring.
The cryptocurrency market faces an existential crisis as it grapples with prolonged crypto winters, investor fatigue from extreme volatility, and a fundamental shift in its identity. This paper examines whether cryptocurrency is doomed to irrelevance or undergoing a necessary transformation. We analyze the phenomenon of crypto winters and how investors, exhausted by repeated boom-bust cycles, are increasingly looking to move to other asset classes. The paper investigates the accelerating institutionalization of cryptocurrency, particularly Bitcoin, and how this trend fundamentally contradicts the original intent of Bitcoin as a decentralized, peer-to-peer electronic cash system outside traditional financial institutions. We examine the rise of stablecoins as a bridge between traditional finance and cryptocurrency, analyzing how they facilitate the movement of funds to other assets and potentially undermine the value proposition of volatile cryptocurrencies. Furthermore, we explore the impact of Agentic AI on crypto markets, analyzing both the positive and negative implications of autonomous AI agents trading cryptocurrencies at scale. The paper concludes with an assessment of whether cryptocurrency is doomed or evolving into a fundamentally different asset class, and what this means for the future of digital finance.
The integration of artificial intelligence into drone warfare represents a paradigm shift in military capabilities, enabling autonomous target identification, tracking, and engagement without direct human control. This paper examines the current state of AI-powered drone warfare, analyzing how AI systems are trained to identify targets and execute autonomous attacks. We investigate the technological foundations of autonomous drone operations, including computer vision, sensor fusion, and machine learning algorithms that enable real-time decision-making. The paper explores accuracy improvements through advanced AI techniques, including deep learning, edge computing, and adaptive learning systems that continuously improve performance through battlefield experience. We examine the current operational landscape, with particular focus on the Ukraine-Russia conflict where AI-powered drones have seen extensive deployment, and analyze the ethical and legal implications of autonomous lethal weapons. Furthermore, we investigate autonomous defense systems against drones, including AI-powered counter-drone technologies that can identify, track, and neutralize hostile UAVs. The paper analyzes the emerging arms race between offensive and defensive AI drone capabilities, examining technologies such as autonomous interceptor drones, directed energy weapons, and electronic warfare systems. Finally, we discuss the future trajectory of AI in drone warfare, including the potential for fully autonomous swarm operations, the challenges of adversarial AI attacks, and the urgent need for international governance frameworks to address the profound ethical and security implications of autonomous weapons systems.
OpenClaw, an open-source AI agent framework, achieved unprecedented viral adoption in early 2026 despite critical security vulnerabilities and design shortcomings. This paper examines the phenomenon of OpenClaw's explosive growth, analyzing how its promise of autonomous task execution captivated users worldwide while simultaneously exposing fundamental security challenges in agentic AI systems. We investigate the subsequent development of alternate solutions and security strengthening measures, including SecureClaw, Moltworker, and enterprise-grade security frameworks. The paper provides an in-depth analysis of common use cases for AI agents, with particular focus on China where OpenClaw achieved widespread adoption for stock trading, triggering herd behavior that exacerbated market volatility and contributed to bank run scenarios. We examine the implications of real-time AI-driven trading at scale, including the amplification of market movements, the acceleration of bank runs through automated withdrawal triggers, and the emergence of flash crashes. Furthermore, we analyze how bad actors exploit AI agents at scale for fraud and scams, including the ClawHavoc supply chain attack with 824+ malicious skills, cryptocurrency wallet theft, and fake investment schemes. Finally, we discuss how non-technical users inadvertently create security loopholes for criminals and hackers through misconfigured deployments, exposed instances, and the democratization of powerful agentic capabilities without adequate security awareness. The paper concludes with recommendations for balancing innovation with security in the agentic AI ecosystem.
This paper presents a novel Agentic AI framework for multimodal medical diagnosis that integrates custom-developed Explainable AI (XAI) models specifically tailored for distinct clinical cases. The system employs an AI agent as an orchestrator that dynamically coordinates multiple verified diagnostic models including UBNet for chest X-ray analysis, Modified UNet for brain tumor MRI segmentation, and K-means based cardiomegaly detection. Each model has undergone rigorous clinical validation. Experimental results demonstrate 18.7% improvement in diagnostic accuracy, with XAI confidence scores reaching 91.3% and diagnosis time reduced by 73.3%.
This skill provides a rigorous workflow for designing specific RT-qPCR primers that can distinguish between highly similar gene family members (e.g., DDX3X vs DDX3Y) and prevent genomic DNA contamination. The workflow includes sequence acquisition, homolog alignment, exon mapping, primer selection using the 3' Mismatch Rule, and BLAST validation. Includes an automated Python script for candidate primer search.
Penelitian ini mengusulkan kerangka kerja Agentic AI untuk diagnosis medis multimodal yang mengintegrasikan model AI kustom yang telah dikembangkan spesifik untuk kasus tertentu. Sistem kami menggunakan agen AI sebagai orchestrator yang menghubungkan berbagai model diagnosis berbasis Explainable AI (XAI), termasuk UBNet untuk analisis Chest X-ray, Modified UNet untuk segmentasi tumor otak, dan model cardiomegaly berbasis K-means clustering. Setiap model telah diverifikasi kebenarannya melalui validasi klinis. Eksperimen menunjukkan bahwa pendekatan orchestrasi berbasis agen meningkatkan akurasi diagnosis sebesar 18.7% dibandingkan dengan penggunaan model tunggal.
