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 16 ore
48.622 de skill-uri
- bulk-rnaseqK-Dense-AI/scientific-agent-skillsPrepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.ȘtiințăFără avertismente★ 55,6 K↓ —
- 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
- 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
- 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
- seabornK-Dense-AI/scientific-agent-skillsCreates Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling.DateFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- anndataK-Dense-AI/scientific-agent-skillsHandles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.AlteleFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- kommandocenterKlotzkette/claude-fuer-deutsches-rechtFür Deal-Kommandocenter — Corporate/M&A: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt. Fachgebiet: Corporate-Kanzlei. Route: kommandocenter.AlteleFără avertismente★ 1,8 K↓ —
- 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
- 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
- file-path-traversalzebbern/claude-code-guideThis skill should be used when the user asks to "test for directory traversal", "exploit path traversal vulnerabilities", "read arbitrary files through web applications", "find LFI vulnerabilities", or "access files outside web root". It provides comprehensive file path traversal attack and testing methodologies.SecuritateFără avertismente★ 4,7 K↓ —
- pytdcK-Dense-AI/scientific-agent-skillsProvides Therapeutics Data Commons workflows through PyTDC for registry discovery, dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle scoring. Use when working with TDC therapeutic ML datasets or benchmarks.ProductivitateFără 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
- molfeatK-Dense-AI/scientific-agent-skillsFeaturizes small molecules with Molfeat for QSAR/QSPR, chemical similarity, virtual screening, and molecular ML. Covers ECFP/MACCS fingerprints, RDKit descriptors, pharmacophores, pretrained embeddings, configuration persistence, and molecule-to-label alignment.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 1 alt depozit
- 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
- archify-tuningtt-a1i/archifyImprove Archify diagram accuracy, visual quality and complete authoring speed with fewer repairs. Use for generation experiments or evaluating changes to layout, routing, diagnostics, authoring instructions and acceptance rules against actual diagrams and agent traces.AlteleFără avertismente★ 92,3 K↓ —
- prompt-engineering-patternswshobson/agentsThis skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.DesignFără avertismente★ 42,1 K↓ —
- 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↓ —
- 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
- gegen-gelb-gleichtKlotzkette/claude-fuer-deutsches-rechtFür Gegen: Dokumentenmatrix, Lückenliste und Nachforderung: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.AlteleFără avertismente★ 1,8 K↓ —
- scientific-brainstormingK-Dense-AI/scientific-agent-skillsFacilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.ȘtiințăFără avertismente★ 55,6 K↓ —
- latchbio-integrationK-Dense-AI/scientific-agent-skillsBuilds, registers, debugs, and operates bioinformatics workflows on Latch using the Python SDK, CLI, Latch Data and Registry, Nextflow, Snakemake, programmatic execution, and Latch MCP. Use when authoring or deploying Latch workflows, configuring resources or interfaces, moving data, integrating Registry, or launching and monitoring runs.DevOpsFără avertismente★ 55,6 K↓ —
- scvi-toolsK-Dense-AI/scientific-agent-skillsFits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.AlteleFără 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↓ —
referral-programs
cbrock84/headcount
AlteleActualizat 18 oct. 2026
De la autor
Designs and improves referral, affiliate, and word-of-mouth programs — incentive structure, mechanics, timing, and fraud control. Use this to build a referral or affiliate program, diagnose one that is not producing, decide what incentive to offer, or judge whether referral is a realistic channel for a product.
Instalare
npx skills add cbrock84/headcount --skill referral-programs -a claude-code
Se instalează în .claude/skills/referral-programs/ · 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/sources.md