Nützliche Agent Skills, nach echter Nutzung sortiert.

skill-creator-advanced

Create, harden, and curate production-grade installable skills and skill libraries. Use for API/CLI/reference skills, invocation design, progressive disclosure, evals, placement, promotion, renames, deprecation, or catalog/router consistency. Skip trivial one-file utilities.

Installationen: 1
GitHub Stars: 47
Aktualisiert: 12. Sept.

119 Dateien

jpcaparas

strong-types

Passive skill enforcing strong typing when writing or reviewing PHP, TypeScript, Python, C, Kotlin, Go, Rust, or other typed code. Bans any/mixed, untyped signatures, blind ??/|| fallback chains; prefers DTOs, enums, generics, strict flags. Do NOT force typing onto untyped languages.

Installationen: 1
GitHub Stars: 47
Aktualisiert: 12. Sept.

23 Dateien

jpcaparas

to-diagram

Turn a convoluted engineering, scientific, or general process into one clear Mermaid diagram and export matching Markdown and PNG files. Trigger on /to-diagram, process diagram, workflow visualization, concept map, or explain this flow visually. Do not use for data charts, image-only infographics, or simple diagram syntax questions.

Installationen: 1
GitHub Stars: 47
Aktualisiert: 12. Sept.

12 Dateien

ai-governance-checklist

A Metabase AI-governance coach, covering the controls and rollout side of AI: who may use Metabot, what it can see, what it costs, where the model runs, and the audit trail. Use this skill whenever the user wants to roll out AI analytics safely, control who can use Metabot or what it can see, set spend or token limits on AI, restrict Metabot's system prompt, audit AI usage, evaluate bring-your-own model or self-hosting options for AI, or prep for a security review of AI features. Trigger it even if the user just says "how do we control AI access", "can we limit what Metabot sees", "we need an AI security review", or "run the AI governance checklist" in a Metabase context. Not this skill if the question is about whether the underlying data is modeled, documented, and trustworthy enough for AI — Transforms, metadata, the Glossary, Metrics, the Library — that's ai-readiness-checklist. Rough test: this skill is about who gets to point AI at the data; that one is about whether the data is good enough.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

MIT2 Dateien

ai-readiness-checklist

A Metabase AI-readiness coach, covering the data groundwork that makes AI answers trustworthy: modeling with Transforms, table and field metadata, the Glossary, saved Metrics, and marking canonical content. Use this skill whenever the user wants to check if their data is ready for AI, prep the underlying data for Metabot or the Metabase MCP server, work through an AI readiness checklist, or audit their data modeling/metadata/metrics before turning on AI features. Trigger it even if the user just says "is my data AI ready?", "let's get set up for Metabot", or "run the AI readiness checklist" in a Metabase context. Not this skill if the question is about controlling who may use AI, capping AI spend, restricting what Metabot can see, auditing AI usage, or passing a security review — that's ai-governance-checklist. Rough test: this skill is about whether the data is good enough; that one is about who gets to point AI at it.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

MIT2 Dateien

metabase-cli

Drive a Metabase instance from the terminal via the mb CLI. Authenticate with named profiles; inspect databases (list, get, full metadata rollup, schemas, tables in a schema) and trigger manual schema sync / field-values rescan; inspect tables, fields; list/get/create/update/archive cards (questions, models, metrics) and run them as JSON/CSV/XLSX; list/get/create/update dashboards and patch dashcards; list/get/create collections and traverse the hierarchy by id, entityid, or "root"/"trash" (with items and recursive tree); list/get/create/update/archive native query snippets, segments, and measures; author/update/run transforms and schedule transform-jobs; read/update settings; search content (cards, dashboards, collections, transforms, metrics); manage Enterprise workspaces; git-sync to/from a git remote (status, dirty, import, export, branches, stash, add/remove a collection from sync). Use whenever the user wants to interact with a Metabase from the terminal — "log into metabase", "what profiles do I have", "list cards", "run card 42 as CSV", "create a transform", "list dashboards", "move a dashcard", "list collections", "what's in collection 4", "show the collection tree", "list snippets", "create a segment", "archive a measure", "search metabase for X", "spin up a workspace", "import the latest changes", "add a directory to git sync", "set a setting", "what schemas are in this database", "trigger a sync", "rescan field values", or anything hitting mb .

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

MIT2 Dateien

metabase-database-metadata

Understands the Metabase Database Metadata Format — a YAML-based on-disk representation of databases, tables, and fields synced from a Metabase instance. Use when the user needs to read, edit, or understand metadata files produced by @metabase/database-metadata, or when reasoning about a project's schema (columns, types, FK relationships) through the .metadata/databases folder.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-embedding-sso-implementation

Implements JWT SSO authentication for Metabase embedding in a project. Supports all embedding types that use SSO — Modular embedding (embed.js web components), Modular embedding SDK (@metabase/embedding-sdk-react), and Full app embedding (iframe-based). Creates the JWT signing endpoint, configures the frontend auth layer, and sets up group mappings. Use when the user wants to add SSO/JWT auth to their Metabase embedding, implement user identity for embedded analytics, set up JWT authentication for Metabase, or connect their app's authentication to Metabase embedding.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-full-app-to-modular-embedding-upgrade

Migrates a project from Metabase Full App / Interactive (iframe-based) embedding to Modular (web-component-based) embedding. Use when the user wants to replace Metabase iframes with Modular embedding web components.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-learning

A spaced repetition learning coach for Metabase. Use this skill whenever the user wants to: learn Metabase, start a Metabase training session, do a Metabase review, practice Metabase concepts, check what they should study today, get quizzed on Metabase, run through active recall on BI tools, or follow a structured learning program for data analytics software. Trigger this skill even if the user just says "let's do my Metabase session", "quiz me on Metabase", or "what should I study today?" in a Metabase learning context.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

MIT12 Dateien

metabase-modular-embedding-to-modular-embedding-sdk-upgrade

Migrates a React project from Metabase Modular embedding (embed.js web components) to the Modular embedding SDK (@metabase/embedding-sdk-react). Use when the user wants to switch from embed.js web components to React SDK components (MetabaseProvider). This is not a version upgrade — it changes the embedding technology.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-modular-embedding-version-upgrade

Upgrades a project's Metabase Modular embedding SDK (@metabase/embedding-sdk-react) or Modular embedding (embed.js) version. Use when the user wants to upgrade their Metabase modular embedding integration to a newer version.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-react-sdk-setup

First-time setup for the Metabase React SDK — instance detection, API key, dashboard discovery, JWT auth, SDK installation, and initial embedding code.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-representation-format

Understands the Metabase Representation Format — a YAML-based serialization format for Metabase content (collections, cards, dashboards, documents, segments, measures, snippets, transforms). Use when the user needs to create, edit, understand, or validate Metabase representation YAML files, or when working with Metabase serialization/deserialization (serdes). Covers entity schemas, MBQL and native queries, visualization settings, parameters, and folder structure.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-semantic-checker

Runs the Metabase semantic checker against a tree of Representation Format YAML files to verify that all references resolve — cross-entity references (collectionid, dashboardid, parentid, parameter source cards, snippet references, transform tags, etc.) and references to columns inside MBQL and native queries. Slow (≥1 min per run). Only use when the user explicitly asks to verify entity references or column references in MBQL/SQL queries; in most cases this runs as a CI step, not locally. Requires database metadata on disk (by default .metadata/tablemetadata.json).

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

metabase-static-embedding-to-guest-embedding-upgrade

Migrates a project from Metabase static embedding to guest embeds (web components via embed.js). Use when the user wants to migrate/convert/switch/upgrade from static embedding to guest embeds, from signed embed iframes to web components, or replace /embed/ iframes with metabase-dashboard/metabase-question components.

Installationen: 1
GitHub Stars: 42
Aktualisiert: 26. Aug.

tenequm

solana-development

Build, test, deploy, and audit Solana programs with Anchor or native Rust, plus ZK Compression (Light Protocol). Use for Solana contracts, token operations, compute optimization, deployment, program audits, or compressed tokens and PDAs.

Installationen: 1
GitHub Stars: 36
Aktualisiert: 11. Sept.

MIT39 Dateien

shipshitdev

context-optimization

- Improve context efficiency through context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality. Use when token costs or context budgets constrain a task, tool outputs are verbose, cache hit rate is low, or context must be partitioned across agents.

Installationen: 1
GitHub Stars: 35
Aktualisiert: 11. Sept.

5 Dateien