Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
45.604 skill-uri din 2.205 depozite · actualizat acum 11 ore
45.602 skill-uri
- gke-golden-pathgoogle/skillsProvides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).OficialPlanificareFără avertismente★ 21,6 K↓ —
- gke-reliabilitygoogle/skillsImproves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for generating K8s YAML manifests (use gke-manifest-generation) or disaster recovery and cluster backups (use gke-backup-dr).OficialDevOpsFără avertismente★ 21,6 K↓ —
- genomic-intelligenceK-Dense-AI/scientific-agent-skillsPredicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.ȘtiințăFără avertismente★ 54,5 K↓ —
- day-booksickn33/agentic-awesome-skillsDaily cash, bank and digital day book: opening and closing balances per book, in/out movements, debit/credit presentation and reconciliation status. Use for daily bookkeeping.AlteleFără avertismente★ 48,5 K↓ —
- debugging-strategieswshobson/agentsMaster systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.InginerieFără avertismente★ 41,7 K↓ —
- molecular-dynamicsK-Dense-AI/scientific-agent-skillsRuns and analyzes molecular dynamics simulations with OpenMM and MDAnalysis. Sets up protein/small molecule systems, defines force fields, runs energy minimization and production MD, and analyzes trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.ȘtiințăFără avertismente★ 54,5 K↓ —
- startup-metrics-frameworkwshobson/agentsTrack, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management. Use this skill when defining a metrics framework, calculating CAC/LTV/burn multiple, benchmarking business health, or preparing metrics dashboards for investors or board reporting.FinanțeFără avertismente★ 41,7 K↓ —
- google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skillsDiscovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse instead).OficialDateFără avertismente★ 21,6 K↓ —
- startup-financial-modelingwshobson/agentsBuild comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups. Use this skill when creating financial projections, calculating burn rate or runway, modeling fundraising scenarios, or preparing investor-ready financials for a seed or Series A raise.FinanțeFără avertismente★ 41,7 K↓ —
- cloud-design-patternsgithub/awesome-copilotCloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.DesignFără avertismente★ 40,4 K↓ —
- java-testcontainersgithub/awesome-copilotWrite reliable integration tests in plain Java with Testcontainers 2.x: JUnit Jupiter lifecycle, singleton containers, reuse, generic containers, wait strategies, networks, init scripts, Docker Compose, CI and Docker/Podman setup. Use when adding or fixing Testcontainers tests, or migrating from 1.x. Framework-neutral.TestareFără avertismente★ 40,4 K↓ —
- bgpt-paper-searchK-Dense-AI/scientific-agent-skillsSearches BGPT scientific papers by topic or DOI and retrieves claim-level evidence extracted from full text, including experiments, reported statistics, scope, limitations, and provenance. Use for literature reviews, evidence synthesis, and finding experimental details beyond abstracts.ȘtiințăFără avertismente★ 54,5 K↓ —
- optimize-for-gpuK-Dense-AI/scientific-agent-skillsGPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.DateFără avertismente★ 54,5 K↓ —
- security-requirement-extractionwshobson/agentsDerive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.SecuritateFără avertismente★ 41,7 K↓ —
- secops-investigategoogle/skillsExpert guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).OficialFinanțeFără avertismente★ 21,6 K↓ —
- java-springbootgithub/awesome-copilotGet best practices for developing applications with Spring Boot.AlteleFără avertismente★ 40,4 K↓ —
- nature-dataYuan1z0825/nature-skillsDraft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata. Use for 数据可用性声明、数据共享、数据仓库选择 and dataset citations; not general data cleaning or statistical analysis.PlanificareFără avertismente★ 50,5 K↓ —
- turborepo-cachingwshobson/agentsConfigure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing distributed caching.AlteleFără avertismente★ 41,7 K↓ —
- kpi-dashboard-designwshobson/agentsDesign effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.DesignFără avertismente★ 41,7 K↓ —
- nature-writingYuan1z0825/nature-skillsDraft or restructure scientific manuscript arguments, sections, and initial-submission materials from author-provided evidence. Use for 论文写作、章节起草、论证重构、正文压缩、首次投稿材料. Use nature-polishing for language-only edits to existing prose and nature-response for post-decision correspondence.RedactareFără avertismente★ 50,5 K↓ —
- transformers-jshuggingface/skillsUse Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.OficialMediaFără avertismente★ 11,2 K↓ —Apare și în 1 alt depozit
- huggingface-besthuggingface/skillsUse when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.OficialPlanificareFără avertismente★ 11,2 K↓ —
- trl-traininghuggingface/skillsPost-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.OficialRedactareFără avertismente★ 11,2 K↓ —Apare și în 1 alt depozit
- huggingface-trackiohuggingface/skillsTrack and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.OficialInginerieFără avertismente★ 11,2 K↓ —
adaptyv
K-Dense-AI/scientific-agent-skills
DesignActualizat acum 7 zile
De la autor
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.
Instalare
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a claude-code
Se instalează în .claude/skills/adaptyv/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Doar instrucțiuniFolosește rețeauaLicență: MIT
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 95
Securitate100
Proveniență95
Întreținere100
Licență100
Adopție73
Fișiere (2)
- SKILL.md
- references/api-endpoints.md