Skills.

Give your AI a better way to work.

A skill is a set of written instructions that teaches an AI how to do one job the way it should be done: review a pull request, plan a migration, write the release notes.

Install one here and it travels with your account into Claude, Claude Code, Cursor and every other client you sign in with.

Category: Databases & data

2,461 results · page 44 of 83

  • chem-spectrum-matcherSkillDatabases & data

    Match an experimental spectrum (1H NMR, 13C NMR, IR) against predicted or database reference spectra for candidate ranking and structure confirmation. Supports local catalog lookup, public database fallback, and pluggable similarity metrics.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • general-query-literature-databaseSkillDatabases & data

    Find relevant simulation workflows in the in-house literature database.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • levyra-databaseSkillDatabases & data

    Implement, debug, or review Levyra Room entities, DAOs, migrations, schemas, caches, stores, backups, downloads, favorites, playlists, history, and persistent queue data.

    Ready to connect★ 164

    github.com/luc4n3x/levyra-deepsound44 stars

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  • mat-calphad-phase-diagramSkillDatabases & data

    Calculate and plot multi-component temperature-composition phase diagrams from Thermodynamic Database (.tdb) files using CALPHAD methods.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • mat-db-mpSkillDatabases & data

    Query Materials Project database for crystal structures, computed properties, elastic/magnetic data, and structurally similar materials using the MP API.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • mat-db-optimadeSkillDatabases & data

    Query the Crystallography Open Database (COD) and other OPTIMADE-compliant databases for experimental crystal structures.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • mat-synthesis-recommendationSkillDatabases & data

    Query and rank synthesis recipes from Materials Project's text-mined literature database with precursors, procedures, and journal references.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • ml-fairchem-finetuneSkillDatabases & data

    Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • ml-mace-finetuneSkillDatabases & data

    Fine-tune MACE machine learning interatomic potentials on custom datasets.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • ml-matgl-finetuneSkillDatabases & data

    Fine-tune MatGL machine learning interatomic potentials on custom datasets.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • ml-mlip-benchmarkSkillDatabases & data

    Benchmark MLIP accuracy against a labeled dataset — compute MAE/RMSE for energy/atom and forces, and generate parity plots.

    Ready to connect★ 164

    github.com/learningmatter-mit/atomisticskills158 stars

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  • agricultural-data-scientistSkillDatabases & data

    Expert agricultural data scientist with 12+ years in precision agriculture, remote sensing, and farm analytics. Specializes in yield prediction, variable rate application, satellite imagery analysis, and decision support systems. Use when: precision-agriculture, remote-sensing, yield-prediction, ag-

    Ready to connect★ 163

    github.com/theneoai/awesome-skills158 stars

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  • ai-application-engineerSkillDatabases & data

    Expert-level AI Application Engineer with deep knowledge of RAG systems, LangChain, LlamaIndex, vector databases, prompt engineering, LLM API integration, and agent frameworks

    Ready to connect★ 163

    github.com/theneoai/awesome-skills158 stars

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  • end-to-end-autonomous-researcherSkillDatabases & data

    Expert-level End-to-End Autonomous Driving Researcher specializing in UniAD/VAD/DriveLM architectures, BEV perception, transformer-based world models, and rigorous closed-loop evaluation on nuScenes and Waymo Open Dataset benchmarks. Use when: e2e-autonomous, bev-perception, imitation-learning, worl

    Ready to connect★ 163

    github.com/theneoai/awesome-skills158 stars

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  • kmp-starter-dataSkillDatabases & data

    Data layer on the KMP Starter Template — repositories and data sources, Logics guidance, DataStore persistence, Room database, StarterFileManager, and the Calf file picker.

    Ready to connect★ 162

    github.com/devatrii/kmp-starter-template162 stars

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  • kmp-starter-feature-analyticsSkillDatabases & data

    The KMP Starter Template analytics system — AppEvent/EventsTracker, Analytics routing, Mixpanel + Firebase providers, combining providers, and runtime swaps.

    Ready to connect★ 162

    github.com/devatrii/kmp-starter-template162 stars

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  • kmp-starter-feature-databaseSkillDatabases & data

    The KMP Starter Template Room database — entities, DAOs, migrations, and DB configuration in features/database. How to add tables and bump versions safely.

    Ready to connect★ 162

    github.com/devatrii/kmp-starter-template162 stars

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  • kmp-starter-featuresSkillDatabases & data

    How to reuse the KMP Starter Template's built-in feature modules — Analytics, Remote Config, Purchases, Database, Store Reviews & Updates, Splash/Onboarding, Notifications, and Locale. Each feature has a dedicated child skill.

    Ready to connect★ 162

    github.com/devatrii/kmp-starter-template162 stars

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  • piper-tts-trainingSkillDatabases & data

    Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers da

    Ready to connect★ 160

    github.com/sammcj/agentic-coding159 stars

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  • concept-rediscovery-walkSkillDatabases & data

    Guides a learner to invent a math or ML concept themselves through a Socratic walk — a sequence of small guessable questions that ends with the learner stating the formal definition unprompted. The 3Blue1Brown signature move. Use when the learner is meeting a foundational concept (eigenvectors, grad

    Ready to connect★ 159

    github.com/lyndonkl/claude151 stars

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  • data-schema-knowledge-modelingSkillDatabases & data

    Creates rigorous, validated models of entities, relationships, and constraints for database schemas (SQL, NoSQL, graph), knowledge graphs, ontologies, API data models, and taxonomies. Covers relational, document, graph, event/time-series, and dimensional schema patterns with lifecycle modeling, soft

    Ready to connect★ 159

    github.com/lyndonkl/claude151 stars

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  • financial-data-sourcingSkillDatabases & data

    Maps every input a company analysis needs to where it comes from — filing line items, market data, Damodaran reference datasets, macro series — with units, update frequency, acceptable fallbacks and the consistency rules that bind them. Use when gathering data for a valuation, when an input is missi

    Ready to connect★ 159

    github.com/lyndonkl/claude151 stars

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  • graphrag-system-designSkillDatabases & data

    Designs complete GraphRAG systems integrating graph databases, vector stores, orchestration frameworks, and LLM reasoning. Guides through pattern selection, technology stack decisions, integration pipeline design, and domain-specific customizations. Use when designing GraphRAG systems, choosing tech

    Ready to connect★ 159

    github.com/lyndonkl/claude151 stars

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  • add-gdp-scatterSkillDatabases & data

    Add a scatter view (with GDP per capita on x) to existing OWID charts via the admin API, mirroring the admin UI's "Add scatter type" defaults, then retire the old standalone "X vs. GDP per capita" charts by redirecting their slugs to that scatter view. Trigger when the user pastes a table with colum

    Ready to connect★ 156

    github.com/owid/etl156 stars

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  • add-ivs-indicatorsSkillDatabases & data

    Add new survey question codes (e.g. C001, D059, H002_01, Y022, E268, G055) to OWID's values-survey pipeline WITHOUT bumping the version — either the Integrated Values Surveys table (integrated_values_surveys, WVS+EVS merged) or the World Values Survey table (world_values_survey, WVS-only questions).

    Ready to connect★ 156

    github.com/owid/etl156 stars

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  • add-provider-regionsSkillDatabases & data

    Add an external provider's regional aggregation (e.g. World Bank, WHO, Maddison, WID, ILO) to OWID's regions dataset — definitions in regions.yml, per-provider grapher map indicators, and metadata — then register it in owid-grapher, including proposing each region's chart color (ContinentColors) and

    Ready to connect★ 156

    github.com/owid/etl156 stars

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  • adversarial-data-reviewSkillDatabases & data

    Adversarially review an ETL dataset's data and metadata for factual accuracy — verifies metadata claims against the producer's own documentation (fetched from the links in snapshot .dvc files and metadata texts) and cross-checks anomalous plus anchor values against independent sources online, to cat

    Ready to connect★ 156

    github.com/owid/etl156 stars

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  • chart-editingSkillDatabases & data

    Create or edit an ETL-authored Grapher chart — a single-chart `.config.yml` in `etl/steps/export/multidim/`. Use when the user wants to author a chart from ETL, edit one, change its title/subtitle/colors/map settings, or preview an ETL-authored chart on staging. For charts with dropdowns (multi-dime

    Ready to connect★ 156

    github.com/owid/etl156 stars

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  • check-empty-entitiesSkillDatabases & data

    Audit every surface that renders a dataset's indicators — charts, map tabs, MDim views, explorer views, narrative charts, and article references — for views whose pinned entity selection has no data in the new indicators (they render as empty charts with no error anywhere). Grades findings against p

    Ready to connect★ 156

    github.com/owid/etl156 stars

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  • check-hardcoded-yearsSkillDatabases & data

    Audit every surface that renders a dataset's indicators — charts, map tabs, MDim views, explorer views, narrative charts, and article embeds/links with time= parameters — for hardcoded time bounds (minTime/maxTime/timelineMinTime/timelineMaxTime/map.time pinned to a number instead of "earliest"/"lat

    Ready to connect★ 156

    github.com/owid/etl156 stars

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What is a skill?

A skill is plain text, usually a SKILL.md file and the scripts it refers to, written for an AI rather than for a person. It carries the steps, the house rules and the examples a good answer needs, so you stop pasting the same briefing into every new chat.

54,764 of the 55,196 skills listed here can be served through ahel today, and they come from public repositories. Each one has its own page with the instructions themselves on it, so you can read what a skill will tell your AI to do before you install it.

Install one and every AI you use gets it

Installing a skill adds it to your gateway and turns it on in the same step. Claude Code surfaces it as a slash command; any client can read the full instructions with the skill_read tool.

Nothing is copied into a project folder. The instructions are served from your account, so the same skill is there in every AI you connect, and turning it off removes it from all of them at once.

See how to connect your AI