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 19 ore
48.622 de skill-uri
- memoKlotzkette/claude-fuer-deutsches-rechtFür Internes Rechtsgutachten: Gutachten-Gerüst: erstellt Entwurf mit Antrag, Beweis und Anlagen; Ergebnis: Schriftsatz mit Begründungs- und Anlagenlogik.AlteleFără avertismente★ 1,8 K↓ —
- genimlK-Dense-AI/scientific-agent-skillsSupports audited local Geniml genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- pathmlK-Dense-AI/scientific-agent-skillsSupports local computational pathology research with PathML: slide loading and tiling, preprocessing and QC, h5path storage, multiplex quantification, spatial graphs, and bounded model inference. Use for whole-slide H&E, CODEX, Vectra, Mesmer, HoVer-Net, and HACTNet workflows.DocumenteFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- rowanK-Dense-AI/scientific-agent-skillsRowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.DevOpsFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- autoskillK-Dense-AI/scientific-agent-skillsAnalyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 1 alt depozit
- networkxK-Dense-AI/scientific-agent-skillsCreates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 1 alt depozit
- events-and-field-marketingcbrock84/headcountPlans and runs events that produce pipeline — conferences, trade shows, webinars, field programs, and measuring whether any of it worked. Use this to decide whether to sponsor an event, plan a conference presence or webinar, design a field program, or work out why event spend is not producing pipeline.MarketingFără avertismente★ 2,6 K↓ —
- daskK-Dense-AI/scientific-agent-skillsScales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.DateFără 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
- pysamK-Dense-AI/scientific-agent-skillsProvides Python/HTSlib workflows for genomic files. Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- liteparseK-Dense-AI/scientific-agent-skillsLocal document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities are spatial text boxes, Markdown, page raster output, and local parsing with optional custom HTTP OCR.DocumenteFără avertismente★ 55,6 K↓ —Apare și în 1 alt depozit
- deepspot-mK-Dense-AI/scientific-agent-skillsGenerates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.DocumenteFără avertismente★ 55,6 K↓ —
- cobrapyK-Dense-AI/scientific-agent-skillsPerforms constraint-based metabolic modeling with COBRApy, including FBA, pFBA, FVA, gene knockouts, flux sampling, growth media, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- diffdockK-Dense-AI/scientific-agent-skillsPredicts protein-small-molecule binding poses with DiffDock and DiffDock-L from PDB or sequence plus SMILES/SDF/MOL2. Covers batch docking, pose triage, confidence interpretation, and validation. Use for molecular docking and virtual-screening pose generation, not binding-affinity prediction.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 1 alt depozit
- understand-knowledgeEgonex-AI/Understand-AnythingAnalyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.AlteleFără avertismente★ 89,5 K↓ —
- systems-administrationcbrock84/headcountRuns servers and corporate systems — patching, configuration baselines, change control, capacity, and the routine that prevents incidents. Use this to establish a patching cadence, standardize server configuration, plan a maintenance window, decide change control for infrastructure, or clean up systems that have drifted from any known state.DevOpsFără avertismente★ 2,6 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↓ —
- 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↓ —
- 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↓ —
- 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
- 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↓ —
- 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↓ —
huashu-speech-coach
alchaincyf/huashu-skills
AlteleActualizat 26 oct. 2026
De la autor
演讲与分享教练。基于Patrick Winston（MIT AI教授）的How to Speak方法论，帮助准备线下培训、技术分享、B站教程视频等演讲场景。当用户提到"演讲"、"分享"、"培训"、"讲课"、"PPT演讲"、"开场"、"结尾"、"如何讲"、"演讲结构"时使用此技能。
Instalare
npx skills add alchaincyf/huashu-skills --skill huashu-speech-coach -a claude-code
Se instalează în .claude/skills/huashu-speech-coach/ · 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: MIT
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 89
Securitate100
Proveniență80
Întreținere100
Licență100
Adopție53
Fișiere (2)
- SKILL.md
- references/patrick-winston-how-to-speak.md