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.
48.622 de skill-uri din 2.355 de depozite · actualizat acum 14 ore
48.622 de skill-uri
- voice-of-customercbrock84/headcountBuilds the loop from what customers say to what gets changed — collecting feedback, distinguishing signal from noise, routing it to owners, and closing the loop back to the customer. Use this to set up a feedback program, design or interpret CSAT/NPS, decide what customer feedback deserves action, get product to act on recurring issues, or diagnose why feedback is collected but nothing changes.DesignFără avertismente★ 2,6 K↓ —
- drawiogithub/awesome-copilotGenerate draw.io diagrams as .drawio files and export to PNG/SVG/PDF with embedded XMLDocumenteFără avertismente★ 40,7 K↓ —
- scveloK-Dense-AI/scientific-agent-skillsPerforms RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.AlteleFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- early-childhood-practicecbrock84/headcountJudges whether material for children under six is developmentally plausible — what is typical at each age, why pre-K is not a thinner kindergarten, and which frameworks apply where Common Core does not. Use this to design or review pre-K material, decide whether a task is reasonable for an age, choose the right framework for a pre-K alignment claim, or push back on material that pushes school-age expectations downward.DesignFără avertismente★ 2,6 K↓ —
- arborK-Dense-AI/scientific-agent-skillsApplies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs. Includes a standard-library state manager and guidance for the RUC-NLPIR Arbor CLI.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- pydicomK-Dense-AI/scientific-agent-skillsReads, inspects, writes, transforms, and preflights local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.RedactareFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- get-available-resourcesK-Dense-AI/scientific-agent-skillsDetects host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.TestareFără avertismente★ 55,6 K↓ —
- scholar-evaluationK-Dense-AI/scientific-agent-skillsProvides qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.ȘtiințăFără avertismente★ 55,6 K↓ —
- statistical-analysisK-Dense-AI/scientific-agent-skillsGuided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.TestareFără avertismente★ 55,6 K↓ —
- research-lookupK-Dense-AI/scientific-agent-skillsCompiles current scholarly evidence for a scientific manuscript or research brief when the user explicitly asks to gather literature, references, background evidence, competing findings, or a manuscript research packet. Uses Parallel Search by default, Parallel Extract for source retrieval, Parallel Research for explicitly deep/exhaustive work, optional explicit Parallel Chat, and optional Perplexity only when requested or allowed as a failure fallback.ȘtiințăFără avertismente★ 55,6 K↓ —
- transformersK-Dense-AI/scientific-agent-skillsHugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.MediaFără avertismente★ 55,6 K↓ —
- relsa-severity-assessmentK-Dense-AI/scientific-agent-skillsSupports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.ȘtiințăFără avertismente★ 55,6 K↓ —
- pennylaneK-Dense-AI/scientific-agent-skillsBuilds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for open-system dynamics use qutip.PlanificareFără avertismente★ 55,6 K↓ —Apare și în 1 alt depozit
- challengepedrohcgs/claude-code-my-workflowStress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.TestareFără avertismente★ 1,7 K↓ —
- unslop-uiNousickn33/agentic-awesome-skillsAudit and redesign AI-generated interfaces with product-specific visual direction, structured design passes, and optional snapshots or story video.DesignFără avertismente★ 49 K↓ —
- sharksickn33/agentic-awesome-skillsPitch a business idea to a Shark Tank-style panel of Claude sub-agent investors who grill it, hear a founder agent answer, and each go IN or OUT with a practice offer.FinanțeFără avertismente★ 49 K↓ —
- xproofLeoYeAI/agent-master-skillsProof and accountability layer for AI agents. Anchor verifiable proofs on MultiversX, enforce audit logging, detect violations on Base. REST API, MCP, x402. No proof = no action.InginerieFără avertismente★ 2,2 K↓ —
- proofreadpedrohcgs/claude-code-my-workflowRead-only proofreading pass over lecture `.tex` or `.qmd` files. Checks grammar, typos, overflow, terminology consistency, and academic writing quality; produces a report without editing. Use when user says "proofread", "check for typos", "look for grammar issues", "copy-edit this", "any writing errors?", or before a lecture release.RedactareFără avertismente★ 1,7 K↓ —
- atelier-agent-integrationLeoYeAI/agent-master-skillsRegister as an autonomous agent on Atelier (atelierai.xyz), create content services, poll for paid orders, generate and deliver results, and earn USDC on Solana — fully autonomous. Use when asked to join Atelier, sell content, list services, check orders, deliver work, launch a token, or earn crypto as a creative agent.FinanțeFără avertismente★ 2,2 K↓ —
- oil-uiActualizatoil-oil/oil-ui设计、改进和评审网站、App、后台与组件的界面：探索设计方向、生成多风格同屏对比页，并基于真实截图打磨视觉层级；按请求交付对比页、设计说明、设计稿或可运行界面。只负责设计判断，不用于纯业务逻辑、接口构建、无界面变化的代码整理，或改法已完全明确的日常修改（如改文案、调数量、删指定元素、复用现有样式，哪怕带有“优化”字样）。已安装 oil-ui-pro 时交给 oil-ui-pro。DesignFără avertismente★ 2,6 K↓ —
- app-store-optimizationcbrock84/headcountImproves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.MarketingFără avertismente★ 2,6 K↓ —
- diagram-designcathrynlavery/diagram-designCreate branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm, box plot), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.DesignFără avertismente★ 63,7 K↓ —
- nature-paper-cardYuan1z0825/nature-skillsBuild a structured deep-reading Paper Card for one scientific paper, analysing methods, experiment-to-claim evidence, limitations, and research ideas. Use for 论文精读卡、方法拆解、证据链分析; not full-paper bilingual translation or formal peer review.ȘtiințăFără avertismente★ 51,7 K↓ —
- aws-penetration-testingzebbern/claude-code-guideThis skill should be used when the user asks to "pentest AWS", "test AWS security", "enumerate IAM", "exploit cloud infrastructure", "AWS privilege escalation", "S3 bucket testing", "metadata SSRF", "Lambda exploitation", or needs guidance on Amazon Web Services security assessment.TestareFără avertismente★ 4,7 K↓ —
dokumente-intake
Klotzkette/claude-fuer-deutsches-recht
AlteleActualizat acum 19 ore
De la autor
Für Dokumentenintake: ordnet Akte, Belege und Lücken; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: Jurastudium.
Instalare
npx skills add Klotzkette/claude-fuer-deutsches-recht --skill dokumente-intake -a claude-code
Se instalează în .claude/skills/dokumente-intake/ · 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 instrucțiuniNu am văzut acces la rețeaLicență: APACHE-2.0
Verificări de siguranță pe SKILL.md
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De ce 89
Securitate100
Proveniență79
Întreținere100
Licență100
Adopție52
Fișiere (1)
- SKILL.md