Scan repositories for newly disclosed CVEs in dependencies after a specific cutoff date. Takes a repository path, cutoff date (YYYY-MM-DD), and optional parameters for transitive dependencies. Parses dependency manifests (package.json, pom.xml, requirements.txt, go.mod, Cargo.toml) and lockfiles to extract exact versions. Queries vulnerability databases (OSV.dev, NVD, GitHub Advisory) to identify CVEs disclosed strictly after the cutoff date. Distinguishes between newly disclosed CVEs and previously known CVEs. Use when: (1) Performing security audits to find new vulnerabilities since last review, (2) Checking if new CVEs affect a historical codebase version, (3) Generating compliance reports showing vulnerability status at specific dates, (4) Tracking security posture changes over time. Supports npm, Maven, pip, Go modules, Cargo, and other major ecosystems.
日本語の概要は準備中です。原文の説明を表示しています。
ArabelaTso/Skills-4-SE☆ 2532026年8月21日 更新
Generate comprehensive, user-friendly README.md files for code repositories. Use when creating documentation for new projects, updating existing READMEs, or improving project onboarding. Produces READMEs with project introduction, prerequisites, environment setup, executable usage instructions, and repository structure overview. Supports application projects, libraries, and research codebases.
日本語の概要は準備中です。原文の説明を表示しています。
ArabelaTso/Skills-4-SE☆ 2532026年8月21日 更新
Identify bug-introducing commits using semantic analysis that extends traditional SZZ algorithm. Distinguishes semantic changes from refactorings or code movements using control-flow and data-flow similarity analysis. Use when analyzing bug-fix commits to trace back to bug-introducing changes, investigating software evolution, conducting empirical studies on defect prediction, or reducing false positives in bug localization. Supports git repositories and provides explanations for why commits are identified as bug-introducing.
日本語の概要は準備中です。原文の説明を表示しています。
ArabelaTso/Skills-4-SE☆ 2532026年8月21日 更新
Produces comprehensive summaries and insights about legacy codebases to help understand unfamiliar code. Use when onboarding to a new project, planning refactoring efforts, assessing code for acquisition/migration, or generating documentation for undocumented systems. Analyzes architecture, dependencies, code quality issues, and test coverage. Creates high-level overviews with architecture diagrams, key components, entry points, and actionable insights for understanding and improving legacy code.
日本語の概要は準備中です。原文の説明を表示しています。
ArabelaTso/Skills-4-SE☆ 2532026年8月21日 更新
Create institutional-quality equity research initiation reports through a 5-task workflow. Tasks must be executed individually with verified prerequisites - (1) company research, (2) financial modeling, (3) valuation analysis, (4) chart generation, (5) final report assembly. Each task produces specific deliverables (markdown docs, Excel models, charts, or DOCX reports). Tasks 3-5 have dependencies on earlier tasks.
日本語の概要は準備中です。原文の説明を表示しています。
w95/awesome-claude-corporate-skills☆ 2492026年2月27日 更新
Build polished, intentional UIs with concrete CSS/Tailwind values — typography, color, layout, spacing, dark mode, accessibility, animations, components. Encodes specific, opinionated rules with exact values, not vague advice. Covers buttons, cards, forms, tables, navigation, dashboards, landing pages, onboarding, and more. Triggers on "absolute ui", "design a UI", "style this component", "make this less like AI slop", "fix the spacing/typography/color", "dark mode", "design a pricing page", "make this look better".
日本語の概要は準備中です。原文の説明を表示しています。
maddhruv/absolute☆ 2202026年7月6日 更新
Think and work like an expert Food Engineer. Use when a task calls for Food Engineer judgment. Reasons from water activity, thermal microbiology, transport-coupled reaction, and rheology through heat-penetration studies, F0/D/z lethality integration, HACCP with prerequisite programs, and CFR Title 21 LACF/acidified-food rules while treating cold-point under-processing, aw and pH drift, post-process contamination, and unvalidated scale-up as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2082026年10月3日 更新
Think and work like an expert Forestry Scientist. Use when a task calls for Forestry Scientist judgment. Reasons from silvicultural systems, site index, DGH and DBH increment, and FIA cruise design through FVS/ORGANON calibration, LiDAR area-based inventory with support matching, and IPCC carbon pools while treating site-index misassignment, plot edge effects, and change-of-spatial-support bias as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2082026年10月3日 更新
Think and work like an expert Electrophysiologist. Use when a task calls for Electrophysiologist judgment. Reasons from membrane voltage, conductance kinetics, series resistance, and filter settings through pClamp/Multiclamp acquisition, pharmacological channel isolation (TTX, NBQX/APV, picrotoxin), Hodgkin-Huxley/Markov gating fits, and NWB-standardized reporting while treating Rs drift, dialysis run-down, space clamp, and polysynaptic contamination as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2082026年10月3日 更新
Think and work like an expert Control Systems Engineer. Use when a task calls for Control Systems Engineer judgment. Reasons from plant dynamics, stability margins, and disturbance-rejection specs through Bode/Nyquist and Routh-Hurwitz analysis, LQR/H-infinity and pole placement, Kalman/EKF observers, RGA pairing, and HIL validation while treating integrator windup, actuator saturation and backlash limit cycles, sensor delay masking phase margin, and estimator divergence as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2082026年10月3日 更新
Think and work like an expert Connectomics Scientist. Use when a task calls for Connectomics Scientist judgment. Reasons from vEM acquisition and petascale alignment through FFN/RoboEM segmentation, FlyWire/neuPrint/MICrONS/H01 graphs, and synapse-level QC while treating split/merge errors, alignment tears, false synapses, and release-version drift as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2082026年10月3日 更新
Think and work like an expert Astrostatistician. Use when a task calls for Astrostatistician judgment. Reasons from selection functions, censored flux limits, and look-elsewhere trial factors through Cobaya/emcee/dynesty cosmology, GP-coupled exoplanet inference, Landy–Szalay clustering, photo-z σNMAD calibration, and SBI (sbi/LtU-ILI) while treating Malmquist bias, prior-driven tensions, and detrend-then-fit transit bias as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
K-Dense-AI/scientific-agents☆ 2082026年10月3日 更新
LBO model, leveraged buyout, private equity acquisition, sources and uses, debt schedule, returns waterfall, sponsor IRR, MOIC calculation, PE exit analysis, LBO valuation
日本語の概要は準備中です。原文の説明を表示しています。
agentii-ai/agentii-investment-intelligence☆ 2062026年9月29日 更新
Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Inference1/clarify-intent-and-establish-shared-understanding☆ 2042026年10月10日 更新
Direct and operate Luma Ray video generation through the current Luma Agents API and distinguish it from the consumer Luma App and legacy Dream Machine API. Use for Ray 3.2 text/image/multi-keyframe video, edits, extends, reframes, HDR/EXR, pricing and approvals, async delivery, migration, rights, privacy, and production QA.
日本語の概要は準備中です。原文の説明を表示しています。
calesthio/generative-media-skills☆ 2032026年7月14日 更新
Use Resemble AI Chatterbox for text-to-speech and voice-cloning workflows, including local open-weight Chatterbox, Chatterbox Multilingual, Chatterbox Turbo, and Resemble-hosted Chatterbox API routes. Apply when an agent must choose a Chatterbox variant, prepare reference-voice inputs, control emotion/paralinguistic delivery, plan multilingual TTS, handle PerTh watermarking, or make production decisions about consent, privacy, licensing, latency, and audio QA.
日本語の概要は準備中です。原文の説明を表示しています。
calesthio/generative-media-skills☆ 2032026年7月14日 更新
Comprehensive domain analysis combining WHOIS ownership, DNS infrastructure, SSL security, SEO performance, tech stack, site structure, and backlink profile. One-stop domain intelligence for acquisition research, security assessment, and competitive analysis.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2022026年9月26日 更新
Analyze estimated website traffic, engagement, rankings, channel mix, and geographic distribution through SandBase. Use for domain benchmarking, acquisition-channel analysis, or traffic trend research; do not present third-party estimates as first-party analytics.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2022026年9月26日 更新
Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2012026年7月2日 更新
Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2012026年7月2日 更新
Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2012026年7月2日 更新
Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2012026年7月2日 更新
Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2012026年7月2日 更新
Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2012026年7月2日 更新