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Found 1,154 Skills
Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Manage Render services, deploys, databases, and infrastructure from the CLI. Use when deploying, restarting, viewing logs, opening SSH/psql sessions, or validating render.yaml blueprints.
Use this skill when architecting on Google Cloud Platform, selecting GCP services, or implementing data and compute solutions. Triggers on Cloud Run, BigQuery, Pub/Sub, GKE, Cloud Functions, Cloud Storage, Firestore, Spanner, Cloud SQL, IAM, VPC, and any task requiring GCP architecture decisions or service selection.
Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial, SQLite) or remote URL (S3, HTTPS). Use when user references a data file, asks "what's in this file", or wants to preview/profile a dataset. Not for source code.
Complete security remediation workflow. Scans code for vulnerabilities using Snyk, fixes them, validates the fix, and optionally creates a PR. Supports both single-issue and batch mode for multiple vulnerabilities. Use this skill when: - User asks to fix security vulnerabilities - User mentions "snyk fix", "security fix", or "remediate vulnerabilities" - User wants to fix a specific CVE, Snyk ID, or vulnerability type (XSS, SQL injection, path traversal, etc.) - User wants to upgrade a vulnerable dependency - User asks to "fix all" vulnerabilities or "fix all high/critical" issues (batch mode)
Create reproducible, cross-platform development environments with Flox — a declarative environment manager built on Nix. ALWAYS use this skill when the user needs to: set up a project with system-level dependencies (compilers, databases, native libraries like openssl, libvips, BLAS, LAPACK); configure reproducible toolchains for Python, Node.js, Rust, Go, C/C++, Java, Ruby, Elixir, PHP, or any language; manage environments that must work identically across macOS and Linux; pin exact package versions for a team; run local services (PostgreSQL, Redis, Kafka) alongside development tools; onboard new developers with a single command; or solve 'works on my machine' problems. Especially valuable for AI-assisted and vibe coding — Flox lets agents install tools into a project-scoped environment without sudo, system pollution, or sandbox restrictions, and the resulting environment is committed to the repo so anyone can reproduce it instantly. Use this skill even if the user doesn't mention Flox — if they describe needing reproducible, declarative, cross-platform dev environments with system packages, this is the right tool. Also use when the user mentions .flox/, manifest.toml, flox activate, or FloxHub.
Route a vague Prisma Next prompt to the right specific skill. Use for "help me with Prisma Next", "what is Prisma Next", "explain Prisma Next", "I'm new to PN", "where do I start", "what can I do with Prisma Next", "what can I do next with Prisma", "just ran createprisma", "tour of Prisma Next", "Prisma Next overview", and comparison questions like "Prisma Next vs Prisma 7", "PN vs Drizzle", "PN vs Kysely", "PN vs TypeORM". Do NOT use when the prompt clearly matches a workflow skill — adoption / quickstart / first-touch orientation / brownfield introspection, schema / contract editing, migration authoring (db update / migration plan / migrate), migration review on deploy / concurrent migrations, queries / db.orm / db.sql / TypedSQL, runtime / db.ts / middleware wiring, build / Vite plugin / Next.js plugin, debug / structured error envelopes / PN-* error codes, or feedback / bug report / feature request — load that sibling skill directly.
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Guide for configuring Infisical Dynamic Secrets — on-demand, short-lived credentials for databases, cloud IAM, SSH, and Kubernetes. Covers 27 providers including PostgreSQL, MySQL, Redis, MongoDB, AWS IAM, GCP IAM, SSH certificates, Kubernetes service accounts, and more. Use this skill when someone asks about: dynamic secrets, ephemeral database credentials, short-lived tokens, rotating database users, dynamic PostgreSQL/MySQL/Redis credentials, SSH certificates, temporary AWS IAM users, or 'how do I generate temporary credentials with Infisical'.
Preview an existing saved CARTO Builder map inline in the chat via the CARTO MCP server's load_builder_map tool. Use whenever the user references a saved Builder map — by URL, by ID, or by name (resolved via list_maps first). Renders a lightweight read-only preview (layers, basemap, viewport, popups, legend). Widgets, SQL parameters, map description, and other Builder-only features are NOT included; the user can click "Open in Builder" for the full experience. Triggers on "show me the X map", "open the Y map", "preview the Z map", and post-CLI-creation inline previews of a freshly-created map. Distinct from carto-create-builder-maps (CLI authoring), carto-render-inline-map (ad-hoc deck.gl spec), and carto-develop-app (developer app).
Explore and query any dataset annotated with a Frictionless Data Package descriptor (datapackage.json). Use this skill whenever a user wants to discover what tables or resources a dataset contains, look up column names and descriptions, surface usage warnings embedded in metadata, or understand how to load data from Parquet files, DuckDB or SQLite databases, or CSV files described by a datapackage.json. Also use when the user has a datapackage.json and wants to know what's in it, how to query it efficiently, or how to connect its metadata to actual data files. Pairs well with dataset-specific skills (like `pudl`) that layer domain knowledge on top.