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.
49.399 de skill-uri din 2.375 de depozite · actualizat acum 7 minute
49.399 de skill-uri
- 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★ 56 K↓ —Apare și în 2 alte depozite
- stageshowNousickn33/agentic-awesome-skillsCreate React product illustrations and cursor demos using a reusable UI kit, examples, motion guidance, and visual verification tools.DesignFără avertismente★ 49,1 K↓ —
- software-design-philosophywondelai/skillsManage software complexity through deep modules, information hiding, and strategic programming. Use when the user mentions "module design", "API too complex", "shallow class", "complexity budget", "strategic vs tactical", "deep module", "information leakage", "pass-through method", "this code is over-engineered", or "simplify this design". Also trigger when reviewing an interface for simplicity, evaluating whether an abstraction is pulling its weight, deciding whether a comment is worth writing, or choosing between general-purpose and special-purpose approaches. Covers deep vs shallow modules, red flags for complexity, and comments as design documentation. For code quality, see clean-code. For architecture boundaries, see clean-architecture.DesignFără avertismente★ 2,6 K↓ —Apare și în 2 alte depozite
- estimating-and-contingencycbrock84/headcountProduces a cost or effort estimate someone can defend — decomposing the work, choosing between analogous, parametric and bottom-up methods, documenting the basis and its assumptions, expressing confidence as a range, and sizing contingency and management reserve separately. Use this to build an estimate, challenge one you have been handed, work out why estimates keep coming in low, or decide how much reserve a portfolio actually needs.AlteleFără avertismente★ 2,6 K↓ —
- geopandasK-Dense-AI/scientific-agent-skillsGuidance and local audit tools for Python workflows that directly use GeoPandas GeoSeries, GeoDataFrame, spatial operations, or vector-data I/O.InginerieFără avertismente★ 56 K↓ —Apare și în 1 alt depozit
- managed-airflow-recommendationsgoogle/skillsProvides recommendations and best practices for creating, configuring, tuning and optimizing Managed Service for Apache Airflow (MSAA; formerly Cloud Composer) environments. Use when the user asks for guidance, recommendations, or best practices on configuring Managed Airflow environments, scaling Airflow environments, preventing workload restarts, or analyzing system health. Don't use for troubleshooting individual DAG or task run failures, authoring DAGs, or migrating DAGs to newer Airflow versions.OficialProductivitateFără avertismente★ 21,8 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★ 56 K↓ —Apare și în 2 alte depozite
- 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★ 56 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★ 56 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★ 56 K↓ —Apare și în 2 alte depozite
- 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★ 56 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★ 56 K↓ —
- unterlagen-lueckenKlotzkette/claude-fuer-deutsches-rechtFür Unterlagen und Lücken: ordnet Akte, Belege und Lücken; Ergebnis: Dokumentenmatrix mit Nachforderungsliste. Fachgebiet: Produkthaftung und Produktrecht.AlteleFără avertismente★ 1,8 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★ 56 K↓ —
- 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★ 90,2 K↓ —
- neuheit-pruefenKlotzkette/claude-fuer-deutsches-rechtFür neuheit-prüfen: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Schnittstellenkarte mit Zuständigkeits- und Nachweisfragen.AlteleFără avertismente★ 1,8 K↓ —
- ex-ussr-kinship-researchDenisSergeevitch/ex-ussr-kinship-researchИсследует родственные связи и семейную историю по персональным данным и документам с фокусом на бывший СССР, СССР и Российскую империю. Использовать для генеалогического поиска, сопоставления однофамильцев, архивных, метрических, военных, миграционных и репрессивных записей, построения доказательной цепочки и итогового отчёта. Не использовать для слежки за живыми людьми, поиска их текущих контактов или адресов, доступа к закрытым данным, обхода защиты и утверждения биологического родства без независимого подтверждения. EN — evidence-first genealogy and family-history research for the former USSR, the USSR and the Russian Empire (church/metric books, civil registration, military, migration, deportation and repression records, unindexed archive scans, namesake disambiguation, evidence chains, final report). Reads existing family trees as leads and exports source-cited trees in GEDCOM format when requested. Replies in the user's language. Not for tracking living people.ȘtiințăFără avertismente★ 1,6 K↓ —
- hundred-million-offerswondelai/skillsCreate irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity. Use when the user mentions "grand slam offer", "make my offer more compelling", "what bonuses should I add", "guarantee strategy", "offer naming", or "people say its too expensive". Also trigger when packaging a product for higher perceived value, justifying premium pricing instead of discounting, designing a money-back guarantee, or structuring tiers to maximize conversions. Covers the MAGIC naming formula and starving-crowd targeting. For product positioning, see obviously-awesome. For outbound sales, see predictable-revenue.AlteleFără avertismente★ 2,6 K↓ —Apare și în 2 alte depozite
- bug-receiptgithub/awesome-copilotClose defects and incidents with a BUG RECEIPT and VERIFIED, PARTIAL, or BLOCKED status after diagnosis, repair, or recovery.AlteleFără avertismente★ 40,8 K↓ —
- data-migrationcbrock84/headcountMoves data from one system to another without losing it or corrupting it — profiling the source before mapping, deciding between big-bang and parallel-run cutover, reconciling counts and values rather than assuming, handling the records that will not map cleanly, and planning a rollback that is actually executable. Use this to plan or run a migration, assess a migration plan someone else wrote, work out why a completed migration is producing wrong numbers, or size how long one will really take.PlanificareFără avertismente★ 2,6 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★ 49,1 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★ 56 K↓ —Apare și în 2 alte depozite
- new-term-radarzhuyansen/new-term-radarOne run from signal to decision. Finds the names of new AI models, products and repositories seen in the last 24 hours (X, Hugging Face, GitHub, sitemaps), then builds a keyword priority table for them, each new name and the generic search terms behind it, sized against Google Ads volume and DataForSEO Trends (an estimate of Google search interest), with prioritize, watch or skip. Use when the user wants today's new AI keywords, an AI trend radar, or asks what new AI terms are worth building a page or tool for. Also takes names the user already has. Runs the new-term-radar command line; needs an AISA_API_KEY.MarketingFără avertismente★ 2,1 K↓ —
- payroll-operationscbrock84/headcountRuns the pay cycle so it is right, on time, and provable — the calendar and cutoffs, what feeds pay from the HRIS and time systems, gross-to-net and the deductions in it, multi-jurisdiction registration and tax filing, off-cycle payments and corrections, and the reconciliation to the general ledger. Use this to design or audit a payroll process, work out what happens when someone is paid wrong, or understand what changes when you hire in a new state or country.DesignFără avertismente★ 2,6 K↓ —
neuropixels-analysis
K-Dense-AI/scientific-agent-skills
InginerieActualizat ieri
De la autor
Analyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.
Instalare
npx skills add K-Dense-AI/scientific-agent-skills --skill neuropixels-analysis -a claude-code
Se instalează în .claude/skills/neuropixels-analysis/ · 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
Rulează scripturile incluseFolosește rețeauaLicență: MIT
Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 89
Securitate85
Proveniență95
Întreținere100
Licență100
Adopție73
Fișiere (19)
- SKILL.md
- assets/analysis_template.py
- references/AI_CURATION.md
- references/ANALYSIS.md
- references/AUTOMATED_CURATION.md
- references/MOTION_CORRECTION.md
- references/PREPROCESSING.md
- references/QUALITY_METRICS.md
- references/SPIKE_SORTING.md
- references/api_reference.md
- references/plotting_guide.md
- references/standard_workflow.md
- încă 7