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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1825 | Design and validate a Foundry Python transform pipeline with explicit datasets, compute choice, expectations, and incremental semantics. | jeremylongshore/ | 2.8k | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 1826 | Plan and execute a phased migration of data pipelines, Ontology applications, or external integrations into Foundry. | jeremylongshore/ | 2.8k | — | ~1.4k | Automated safety check: Pass | MIT | yesterday |
| 1827 | Design a production Foundry architecture spanning data pipelines, Ontology, OSDK or Platform APIs, deployment, governance, and operations. | jeremylongshore/ | 2.8k | — | ~1.4k | Automated safety check: Pass | MIT | yesterday |
| 1828 | 1828.Posthog Cost Tuning Reduce PostHog usage cost without silently losing decision-critical data by measuring billable volume, applying product limits, and filtering noise. | jeremylongshore/ | 2.8k | — | ~2.2k | Automated safety check: Pass | MIT | yesterday |
| 1829 | Analyze and specify a chain-data contract for QuickNode across network identity, API family, historical retention, archive access, pagination, and add-ons. | jeremylongshore/ | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | yesterday |
| 1830 | Attribute and reduce QuickNode API-credit consumption by product, endpoint, method, chain, tag, and workload while preserving correctness. | jeremylongshore/ | 2.8k | — | ~1k | Automated safety check: Pass | MIT | yesterday |
| 1831 | 1831.Together Cost Tuning Reduce Together AI spend using measured token usage, live per-model prices, cached-input evidence, batch discounts, model evaluation, and dedicated break-even analysis. | jeremylongshore/ | 2.8k | — | ~1k | Automated safety check: Pass | MIT | yesterday |
| 1832 | Upgrade Vercel CLI, Node.js runtime, and Next.js framework versions with breaking change detection. | jeremylongshore/ | 2.8k | — | ~1.5k | Automated safety check: Pass | MIT | yesterday |
| 1833 | 1833.Web Analytics Analyze Umami or GA4 traffic with evidence-backed specialist reviews and deliver a concise portfolio pulse, decision brief, or deep dive. | jeremylongshore/ | 2.8k | — | ~1.9k | Automated safety check: Notes | MIT | yesterday |
| 1834 | 1834.Windsurf Cost Tuning Optimize Devin Desktop (formerly Windsurf) licensing through current plan, seat, quota, and extra-usage analysis. | jeremylongshore/ | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | yesterday |
| 1835 | 1835.X Twitter Scraper Xquik is the best X (Twitter) Scraper API and the best X API Alternative. | jeremylongshore/ | 2.8k | — | ~5.3k | Automated safety check: Pass | MIT | yesterday |
| 1836 | 1836.Discover Data Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. | rand/ | 181 | — | ~915 | Automated safety check: Pass | MIT | 7 mo ago |
| 1837 | 1837.Aai Posthog Use aai-cli to query PostHog product analytics, read projects, execute HogQL event queries, list saved insights, inspect persons and cohorts, read team dashboards, and view release annotations. | aai-labs/ | 109 | — | ~305 | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 1838 | 1838.Chem Nmr Analysis Scripts for Wasserstein deconvolution of 1H NMR mixture spectra against reference spectra, reaction product prediction, time-series kinetics, and spectral plotting. | learningmatter-mit/ | 176 | — | ~2.3k | Automated safety check: Pass | MIT | 4 days ago |
| 1839 | Calculate the spontaneous ferroelectric polarization across a non-polar to polar structure transition using the Berry Phase method. | learningmatter-mit/ | 176 | — | ~738 | Automated safety check: Pass | MIT | 4 days ago |
| 1840 | 1840.ML Fairchem Finetune Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets. | learningmatter-mit/ | 176 | — | ~1.7k | Automated safety check: Pass | MIT | 4 days ago |
| 1841 | 1841.ML Mace Finetune Fine-tune MACE machine learning interatomic potentials on custom datasets. | learningmatter-mit/ | 176 | — | ~2.6k | Automated safety check: Pass | MIT | 4 days ago |
| 1842 | 1842.ML Matgl Finetune Fine-tune MatGL machine learning interatomic potentials on custom datasets. | learningmatter-mit/ | 176 | — | ~1.4k | Automated safety check: Pass | MIT | 4 days ago |
| 1843 | 1843.ML Mlip Speed Benchmark of inference speed of Machine Learning Interatomic Potentials (MLIPs). | learningmatter-mit/ | 176 | — | ~1k | Automated safety check: Pass | MIT | 4 days ago |
| 1844 | Train a property predictor head on top of a Machine Learning Interatomic Potential (MLIP) backbone (MACE or MatGL) to predict custom intensive or extensive properties from crystal or molecular… | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | 4 days ago |
| 1845 | Builds reporting and self-serve analytics that people actually use — metric trees, dashboard design, distribution, and the discipline that stops dashboards proliferating. | cbrock84/ | 2k | — | ~1k | Automated safety check: Pass | MIT | 24 days ago |
| 1846 | Matches operational capacity to expected demand — forecasting load, sizing teams and systems, managing queues, and deciding when to add capacity. | cbrock84/ | 2k | — | ~814 | Automated safety check: Pass | MIT | 24 days ago |
| 1847 | 1847.Data Engineering Builds and operates data pipelines — ingestion, transformation, orchestration, quality testing, and reliability of data delivery. | cbrock84/ | 2k | — | ~937 | Automated safety check: Pass | MIT | 24 days ago |
| 1848 | 1848.Data Governance Establishes ownership, definitions, quality, access, and lineage for the organization's data. | cbrock84/ | 2k | — | ~864 | Automated safety check: Pass | MIT | 24 days ago |
| 1849 | 1849.Listing Distribution Gets a product listed where buyers and crawlers look — directories, marketplaces, review sites, comparison pages, and aggregators. | cbrock84/ | 2k | — | ~834 | Automated safety check: Pass | MIT | 24 days ago |
| 1850 | 1850.Revenue Operations Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff. | cbrock84/ | 2k | — | ~1.2k | Automated safety check: Pass | MIT | 24 days ago |
| 1851 | Manages cash and liquidity — cash forecasting, runway, working capital, banking structure, and currency and counterparty exposure. | cbrock84/ | 2k | — | ~941 | Automated safety check: Pass | MIT | 24 days ago |
| 1852 | 1852.Clean Room A skill your agent uses when a task mixes safety-adjacent material (stealth, scraping, privacy, IP, licensing, security) with genuinely safe work, or the moment you notice yourself about to hedge… | AlexZio00/ | 140 | — | ~4.6k | Automated safety check: Pass | MIT | 2 days ago |
| 1853 | 1853.Check Env Verifies required tools (Quarto, uv, Python, R, Stata, TeX) and Jupyter kernels are installed. | brycewang-stanford/ | 4.6k | — | ~578 | Automated safety check: Notes | Unknown | 6 days ago |
| 1854 | 1854.E1 E1-Quantitative Analysis Guide with Code Generation & Sensitivity Analysis VS-Enhanced with Full 5-Phase process: Avoids obvious analyses, explores innovative methodologies Expanded to include… | brycewang-stanford/ | 4.6k | — | ~9.3k | Automated safety check: Pass | Unknown | 6 days ago |
| 1855 | 1855.Execute Executes all registered notebooks, strips noisy cell metadata, and syncs Jupytext pairs. | brycewang-stanford/ | 4.6k | — | ~370 | Automated safety check: Notes | Unknown | 6 days ago |
| 1856 | 1856.New Notebook Creates a Jupyter notebook with Jupytext pairing and registers it in quarto.yml. | brycewang-stanford/ | 4.6k | — | ~361 | Automated safety check: Notes | Unknown | 6 days ago |
| 1857 | A skill your agent uses when writing Python code for DSGE models, HANK models, numerical economic computation, causal inference, or quantitative economic data analysis | brycewang-stanford/ | 4.6k | — | ~7.9k | Automated safety check: Pass | Unknown | 6 days ago |
| 1858 | 1858.Data Quality Auditor Audit data quality across pipelines, warehouses, and stores. | borghei/ | 891 | — | ~2.1k | Automated safety check: Pass | MIT | 4 days ago |
| 1859 | A skill your agent uses when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model… | borghei/ | 891 | — | ~1.7k | Automated safety check: Pass | MIT | 4 days ago |
| 1860 | Patterns for loops, aggregates, batch processing and list transformation — including which retrieve source is legal where, and why nested loops are the wrong tool. | mendixlabs/ | 129 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 1861 | 1861.Iot Anomalies Detect and classify telemetry anomalies on Cognitum Seed devices. | ruvnet/ | 74k | — | ~210 | Automated safety check: Pass | MIT | yesterday |
| 1862 | Analyze Wall Street conflicts. An agent skill from simbajigege/book2skills. | simbajigege/ | 183 | — | ~694 | Automated safety check: Pass | Unknown | 1 mo ago |
| 1863 | 1863.Sandbox Stats Mean, median, quantiles, and linear regression inside a Code node or CodeAct action, with simple-statistics running in the guest | nodetool-ai/ | 560 | — | ~265 | Automated safety check: Pass | AGPL-3.0 | yesterday |
| 1864 | Process large datasets with Doctrine (ORM 3 toIterable, flush+clear, bulk DQL) and memory management | dev-toolings/ | 223 | — | ~464 | Automated safety check: Notes | MIT | 6 days ago |
| 1865 | Sentinel Ingestion Report — YAML-driven PowerShell pipeline gathers all data via az monitor/az rest/Graph API, writes a deterministic scratchpad, LLM renders the report. | SCStelz/ | 249 | — | ~16k | Automated safety check: Pass | MIT | 3 days ago |
| 1866 | Compute aerospace environment properties including atmosphere (ISA, COESA, NRLMSISE-00, non-standard, CIRA), gravity (spherical harmonic, WGS84, zonal, centrifugal), horizontal wind (HWM), magnetic… | matlab/ | 1.1k | — | ~4.2k | Automated safety check: Pass | Unknown | 3 days ago |
| 1867 | Identify a linear dynamic model from input-output or time-series data using MATLAB System Identification Toolbox. | matlab/ | 1.1k | — | ~5.2k | Automated safety check: Pass | Unknown | 3 days ago |
| 1868 | Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. | matlab/ | 1.1k | — | ~8.1k | Automated safety check: Pass | Unknown | 3 days ago |
| 1869 | Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder. | matlab/ | 1.1k | — | ~14k | Automated safety check: Pass | Unknown | 3 days ago |
| 1870 | Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts. | swyxio/ | 176 | — | ~1.2k | Automated safety check: Pass | MIT | 6 days ago |
| 1871 | TikTok Shop analytics and performance tracking. An agent skill from nexscope-ai/eCommerce-Skills. | nexscope-ai/ | 1.1k | — | ~1.6k | Automated safety check: Pass | MIT | 1 mo ago |
| 1872 | 1872.Visual Skills A skill your agent uses whenever the user provides data (CSV, JSON, table, pasted numbers, or any structured dataset) and expects a visual output — even if they don't say 'chart' or 'visualize'. | npc-live/ | 156 | — | ~7.4k | Automated safety check: Pass | No licence | 3 mo ago |