check-stablecoins
Check all stablecoin YAML files for Twitter activity and domain availability, adding a checks section to each file
824 repo starsObserved in 1 repos
Research
Research skills
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
agent-reach
Self-contained multi-platform internet search and read skill. Zero external dependencies — calls upstream tools (curl+Jina, gh, yt-dlp, xreach, mcporter) directly and degrades gracefully when tools are missing. Covers: web pages, GitHub, YouTube, Bilibili, Reddit, Twitter/X, XiaoHongShu, Douyin, Weibo, WeChat, V2EX, LinkedIn, RSS, Exa web search. Use when agent needs to search the web, read a URL, or gather research material. Triggers: "search", "read this URL", "搜索", "查一下", "上网搜", "帮我查", "search twitter", "youtube transcript", "search reddit", "web search", "B站", "bilibili", "小红书", "微博", "V2EX", "research".
823 repo starsObserved in 1 repos
Research
git-archaeology
Investigate how code reached its current state — when a line, function, import, or whole file was changed or deleted, who removed it, and what it looked like before. Use when `git blame` came up empty, when content has been refactored away, or when you need the full evolution of a function across commits.
822 repo starsObserved in 1 repos
Research
radiology-citation
Turn manuscript text, claims, figure/table statements, abstracts, slides, or novelty/comparison assertions into verified, imaging-journal-scoped citation candidates and export one reference-manager-ready file (RIS, EndNote ENW, or BibTeX). Use when the user needs references for an imaging paper, wants supporting citations, wants a two-pass claim/citation/numerical verification gate, needs to check whether a cited source actually says the claimed thing, scope citations to radiology/imaging journals (Radiology, Radiology: AI, RadioGraphics, AJR, European Radiology, JACR, etc.), verify a DOI/PMID, or export a bibliography. Verifies identifiers before formatting and never fabricates DOIs, pages, volumes, journal metadata, or source support.
819 repo starsObserved in 1 repos
Research
radiology-deep-learning
Design and audit imaging deep-learning studies to Radiology (RSNA) / CLAIM 2024 standard, or to Nature-portfolio / FUTURE-AI trustworthy-AI standard — architecture choice (2D/2.5D/3D CNN, Transformer/ViT, segmentation/detection nets, prognostic models), transfer learning vs self-supervised pretraining vs training from scratch, how images/masks/clinical/text/molecular inputs enter the model, data splitting and augmentation, class imbalance, hyperparameter search, baselines, external validation, interpretability/explainability (Grad-CAM, SHAP, attention), uncertainty quantification (MC dropout, ensembles, conformal prediction), and robustness/OOD testing — with patient-level partition hygiene throughout. Use when the user plans or reviews a CNN/Transformer/3D/segmentation/detection/foundation/multimodal imaging model, mentions transfer learning, self-supervised, nnU-Net, ViT, data augmentation, class imbalance, explainability, uncertainty, robustness, or "影像深度学习/深度学习模型". Produces a model+training+validation design, a leakage audit, and Methods text. Never fabricates performance or training details.
819 repo starsObserved in 1 repos
Research
radiology-design
Assess whether an imaging dataset can support a study and turn it into a complete, submittable design — from feasibility triage to clinical question, target population, endpoint/estimand, minimum-viable vs stronger methods, and a validation strategy (internal resampling, temporal, geographic, fully external, multi-center, federated). Use when the user has CT/MRI/PET/US/mammography/multimodal data but is unsure what to do, asks "can this topic be done?" / "能不能做" / "帮我设计课题" / "study design" / "what can I study with this data", or needs a multi-center / external-validation plan ("多中心", "external validation", "generalisability", "center effect", "scanner effect"). Produces a study blueprint with feasibility verdict, design options, validation plan, and the limiting constraint surfaced. Never invents cohort numbers or overstates what the data can support.
819 repo starsObserved in 1 repos
Research
radiology-frontier
Find publishable frontier directions and innovation points for imaging-AI / radiomics / radiogenomics research, grounded in the publication patterns of high-impact journals (Radiology, Radiology: AI, Lancet Digital Health, Lancet Oncology, Nature Medicine, Nature Communications, npj Digital Medicine, npj Precision Oncology, eClinicalMedicine, Cell Reports Medicine). Use when the user asks for frontier directions, innovation points, hot vs suitable topics, "近三年前沿方向", "创新点", "what's novel in imaging AI", or wants to know the evidence/publication-pattern basis behind a recommendation ("有什么文献依据", "证据"). Translates trends (foundation models, self-supervised, vision-language, multimodal fusion, longitudinal, weak/federated learning, radiogenomics) into executable research questions matched to the user's actual data. Encodes publication-pattern heuristics, not a fabricated citation list — routes live verification to radiology-search and never invents PMIDs/DOIs.
819 repo starsObserved in 1 repos
Research
radiology-journal
Match a finished or near-finished imaging-AI / radiomics / radiogenomics manuscript to the right target journals and build a submission tier list (reach / target / safety) grounded in each venue's publication patterns, author-guide style profiles, and the paper's real strengths and weaknesses. Use when the user asks where to submit, "选刊/投哪个期刊", journal selection, "can this go to Nature Medicine / Science / NEJM / Lancet / Lancet Oncology / Lancet Digital Health / Radiology", fit assessment, or a submission ladder. Uses The Lancet Digital Health guide as the default Lancet-series proxy. Grades external validation, prospectivity, reader study, calibration, clinical utility, sample size, centers, novelty, and reporting compliance; returns fit, risk, strengthening priorities, and venue-style requirements. Verifies current journal scope via live search; never selects on impact factor alone.
819 repo starsObserved in 1 repos
Research
radiology-radiogenomics
Design, analyse, report, and submit imaging-multi-omics radiogenomics studies that link radiomic/deep imaging phenotypes to genomic, transcriptomic, single-cell, and spatial-omics data. Use when the user mentions radiogenomics, imaging genomics, imaging-transcriptomics, TCIA/TCGA, GEO, dbGaP, EGA, cBioPortal, multi-omics integration, MOFA, iCluster, SNF, DIABLO, scRNA-seq deconvolution, CIBERSORTx, BayesPrism, spatial transcriptomics, Visium, Xenium, imaging habitats, gene expression, mutations, pathways, biological validation, omics QC, sample-to-image mapping, radiogenomics submission packages, or reviewer comments about batch/leakage/spatial mismatch. Covers the full chain from matched-cohort feasibility and protocol/SAP to omics QC, imaging pipeline, integration, validation, reporting, submission, and rebuttal support. Never fabricates associations, cohort counts, accessions, approvals, metrics, or reviewer actions.
819 repo starsObserved in 1 repos
Research
radiology-radiomics
Design and audit a hand-crafted radiomics study end-to-end to Radiology (RSNA) / CLEAR / IBSI standard — image preprocessing (resampling, intensity normalisation, gray-level discretisation/bin width, filters), IBSI-compliant feature extraction (PyRadiomics or equivalent), reproducibility/stability filtering, leakage-safe feature selection, modelling, and internal/external validation. Use when the user plans or reviews a radiomics pipeline, mentions PyRadiomics, IBSI, feature extraction, bin width, gray-level discretisation, LASSO feature selection, radiomics signature/score, or "影像组学/放射组学". Produces a reproducible pipeline spec, runnable parameter settings, a leakage audit, and Methods text. Never fabricates feature counts or performance.
819 repo starsObserved in 1 repos
Research