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
- bangunai-blog-managerLeoYeAI/agent-master-skillsUse when managing BangunAI Blog content, automating blog workflows, and writing MDX articles with BangunAI conventions. Supports Obsidian-like features (callouts, mermaid, LaTeX, wikilinks, backlinks, graph view).RedactareFără avertismente★ 2,2 K↓ —
- dsh-code-reviewNouqufei1993/skills-hub审查代码改动或 PR 时使用，依据目标项目的实际需求检查正确性、生命周期、接口行为、验证证据和文档，报告有依据且可采取行动的问题。InginerieFără avertismente★ 1,8 K↓ —
- disputes-and-legal-holdscbrock84/headcountHandles a dispute from the first sign of it — recognizing when preservation obligations attach, issuing and scoping a legal hold, suspending automatic deletion across every system that holds relevant material, preserving privilege, managing outside counsel and cost, and evaluating settlement against the real cost of continuing. Use this to respond to a claim or demand letter, issue a hold, work out which systems the hold has to reach, or decide whether to fight.JuridicFără avertismente★ 2,6 K↓ —
- postgres-hybrid-text-searchtimescale/pg-aiguideUse this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.DateFără avertismente★ 1,9 K↓ —
- verifycodeaholicguy/ai-devkitAI DevKit · Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.TestareFără avertismente★ 1,7 K↓ —
- design-postgres-tablestimescale/pg-aiguideUse this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.DateFără avertismente★ 1,9 K↓ —
- timescaledb-hyperfunctionstimescale/pg-aiguideUse this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percenDateFără avertismente★ 1,9 K↓ —
- quantitative-analysiscbrock84/headcountAnswers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.InginerieFără avertismente★ 2,6 K↓ —
- partnership-marketingcbrock84/headcountBuilds reach through other people's audiences — co-marketing partnerships, creator and influencer programs, and community building. Use this to find and evaluate partners, structure a joint campaign or creator deal, plan a community strategy, or decide whether a partnership is worth the coordination cost.MarketingFără avertismente★ 2,6 K↓ —
- spss-amos-semNoubrycewang-stanford/Auto-Empirical-Research-SkillsQuestionnaire structural equation modeling in the SPSS + AMOS workflow that Chinese management, psychology, education, nursing and tourism theses use — data cleaning of an SPSS .sav file, reverse coding, straight-lining, demographics, Harman common-method-bias test, Cronbach's alpha / CITC reliability, EFA (KMO, Bartlett), CFA convergent validity (standardized loadings, CR, AVE), discriminant validity (Fornell-Larcker, HTMT), model fit (chi2/df, CFI, TLI, RMSEA, SRMR), structural paths and hypothesis tests, bootstrap mediation with specific indirect effects, multi-group measurement invariance, and Hayes PROCESS models 1/4/6/7/8/14. One spec card drives an R/lavaan engine set to AMOS defaults and writes ready-to-run SPSS syntax (.sps), AMOS Program Editor VB.NET code, AMOS user-defined estimands, an AMOS drawing checklist with the numbers AMOS should show, Chinese three-line tables in Word, a results-chapter draft and a transparent model-modification log. Use when the user mentions SPSS, AMOS, SEM, structural TestareFără avertismente★ 4,9 K↓ —
- typeuibergside/typeuiUse TypeUI with Hermes Agent to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.DesignFără avertismente★ 2,2 K↓ —
- implementation-planningcbrock84/headcountTurns a spec or requirement into a written plan a separate session or agent can execute, then drives that plan through review checkpoints. Use this before touching code on any multi-step task, when work needs handing to someone else, when a task keeps sprawling mid-implementation, or when a plan exists and needs executing in order with verification between steps.PlanificareFără avertismente★ 2,6 K↓ —
- excelKlotzkette/claude-fuer-deutsches-rechtFür Excel-Anlagen und Zahlenbausteine: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.DocumenteFără 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
- spark-memory-thermal-opswshobson/agentsManage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.ProductivitateFără avertismente★ 42,1 K↓ —
- pydeseq2K-Dense-AI/scientific-agent-skillsPerforms bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.TestareFără 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
- pua-enLeoYeAI/agent-master-skillsPut your AI on a Performance Improvement Plan. Forces exhaustive problem-solving with Western big-tech performance culture rhetoric and structured debugging. Trigger when: (1) task failed 2+ times or stuck tweaking same approach; (2) about to say 'I cannot', suggest manual work, or blame environment without verifying; (3) being passive—not searching, not reading source, just waiting; (4) user frustration: 'try harder', 'stop giving up', 'figure it out', 'again???', or similar. Also for complex debugging, env issues, config/deployment failures. All task types: code, config, research, writing, deployment, infra, API. Do NOT trigger on first-attempt failures or when a known fix is executing.DevOpsFără avertismente★ 2,2 K↓ —
- zeitleisteKlotzkette/claude-fuer-deutsches-rechtFür Zeitleiste des Datenschutzvorfalls — minutiöse Rekonstruktion: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Prüfprodukt mit Risiko und nächstem Schritt.AlteleFără avertismente★ 1,8 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
- 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
- chief-marketing-officercbrock84/headcountOwns brand, demand generation, content, communications, and how the market understands what the business does. Use this to set marketing strategy, allocate budget across channels, decide positioning and messaging, judge whether a campaign is worth running, diagnose why demand has stalled, or arbitrate between brand-building and short-term acquisition. Also use to decide which marketing work to stop.MarketingFără avertismente★ 2,6 K↓ —
- 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
- 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
astropy
K-Dense-AI/scientific-agent-skills
DateActualizat acum 7 zile
De la autor
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
Instalare
npx skills add K-Dense-AI/scientific-agent-skills --skill astropy -a claude-code
Se instalează în .claude/skills/astropy/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Fără descărcare: licența acestui skill (Personalizată sau neclară) nu ne permite să-i redistribuim fișierele. Comenzile de instalare le iau direct de pe GitHub.
La ce are acces
Doar instrucțiuniFolosește rețeauaLicență: Personalizată sau neclară
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De ce 89
Securitate100
Proveniență95
Întreținere100
Licență45
Adopție73
Fișiere (9)
- SKILL.md
- references/coordinates.md
- references/cosmology.md
- references/fits.md
- references/review.md
- references/tables.md
- references/time.md
- references/units.md
- references/wcs_and_other_modules.md