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 7 ore
48.622 de skill-uri
- onekgpdK-Dense-AI/scientific-agent-skillsQueries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.ȘtiințăFără avertismente★ 55,6 K↓ —Apare și în 2 alte depozite
- deaktivierenKlotzkette/claude-fuer-deutsches-rechtFür /deaktivieren — Skill deaktivieren (ohne Dateilöschung): ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.AlteleFără avertismente★ 1,8 K↓ —
- setup-local-sdkdotnet/skillsInstall a .NET SDK locally for safe preview testing, specific-version pinning, or reproducible team setups — without modifying the system-wide installation. USE FOR: trying .NET previews safely, testing specific SDK versions, installing MAUI or other workloads on a preview, updating or replacing an existing local SDK, creating reproducible team/CI install scripts, configuring global.json paths. DO NOT USE FOR: system-wide SDK installs, .NET hosts older than 10, runtime-only installs, or projects not using SDK-style commands.TestareFără avertismente★ 5,8 K↓ —
- qiime2-ampliconK-Dense-AI/scientific-agent-skillsProcesses paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.AlteleFără avertismente★ 55,6 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
- 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
- cloud-logging-finopsgoogle/skillsAudits Cloud Logging costs for a Google Cloud project: the charges for log entries written to log buckets, the services, log streams and severities that drive them, and the log sinks that route them. Reconciles Cloud Billing export costs with Cloud Monitoring volume metrics, and generates a FinOps report with charts and copy-paste exclusion filters. Use when auditing Cloud Logging spend, finding logging waste, identifying top logging contributors, generating log cost reduction reports, or creating log sink exclusion filters. Don't use for querying log entries (use cloud-logging-query-generation), configuring Cloud Monitoring alerts, or querying metrics/traces.OficialRedactareFără avertismente★ 21,8 K↓ —
- 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
- 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
- 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↓ —
- 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↓ —
- 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↓ —
- 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
- hunt-lfielementalsouls/Claude-BugHunterHunt Local File Inclusion (LFI), Remote File Inclusion (RFI), and Path Traversal — /etc/passwd read, log poisoning → RCE, PHP filter-chain RCE (no upload needed), php:// / data:// / zip:// / phar:// wrappers, RFI via allow_url_include, directory traversal read/write/delete. Covers OOB/blind LFI confirmation and false-positive discipline. Use when hunting file-include or path-traversal bugs on any target.RedactareFără avertismente★ 5,4 K↓ —
- chief-content-officercbrock84/headcountRuns content as an operation — the production pipeline, editorial calendar, repurposing engine, competitive content intelligence, and audits of what already exists. Use this to build or fix a content production system, plan a calendar, decide how one piece becomes many, rank content ideas by expected impact, or audit an existing library for what to update, consolidate, or retire. For deciding what territory to own and why, use `content-strategy` first.RedactareFără avertismente★ 2,6 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↓ —
- taxcbrock84/headcountStructures the tax questions a growing business faces — corporate income, sales and use, payroll, nexus, and the obligations created by hiring or selling somewhere new. Use this to work out what a new state or country obligates you to, prepare for a tax filing or audit, understand sales tax on your product, or check what a remote hire or new market triggers.FinanțeFără avertismente★ 2,6 K↓ —
- agent-platform-inferencegoogle/skillsConnects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.OficialTestareFără avertismente★ 21,8 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↓ —
- gke-ai-troubleshooting-tpu-dynamic-slices-monitoringgoogle/skillsMonitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).OficialAlteleFără avertismente★ 21,8 K↓ —
- clinical-decision-supportK-Dense-AI/scientific-agent-skillsPrepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Supports aggregate or synthetic research documentation and traceability, excluding patient care and live clinical operation.ȘtiințăFără avertismente★ 55,6 K↓ —
- retentioncbrock84/headcountDiagnoses and reduces churn — cancellation flows, save offers, failed-payment recovery, at-risk detection, and the product and service causes underneath. Use this when churn is rising or unexplained, to design a cancellation or win-back flow, to recover involuntary churn, to identify at-risk accounts before they leave, or to decide whether a retention problem is a product problem.DesignFără avertismente★ 2,6 K↓ —
- experimental-designK-Dense-AI/scientific-agent-skillsDesigns experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.DesignFără avertismente★ 55,6 K↓ —
find-hypertable-candidates
timescale/pg-aiguide
DateActualizat acum 5 zile
De la autor
Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
Instalare
npx skills add timescale/pg-aiguide --skill find-hypertable-candidates -a claude-code
Se instalează în .claude/skills/find-hypertable-candidates/ · 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
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