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Top Performing in Infrastructure
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| Name | Contains | Score |
|---|---|---|
Standards and workflows for building secure, well-structured Terraform modules, including planning gates, validation steps, and implementation guidance. Contains: task-log-update Use when the user asks to log work, record what was done, or save task progress. Creates a structured markdown task log in `agent-logs/` with validation, waivers, and a final gates summary. task-workflow Use when the user asks you to implement a feature, fix a bug, or complete a repository task end-to-end (from scoping through code changes, validation, and final gate summary). terraform-plan Use when `.tf` or `.tfvars` files have been edited, added, or removed and you need to verify that the code changes produce the intended Terraform plan before the task is considered complete. Runs `terraform plan` in `examples/test_app` and cross-checks the result against the change set you expected your edits to cause. This is a gate: do not declare a Terraform task done without passing it. validation-runner Use when repository changes are complete and you need to run and report the required validation gates for the applicable change class (`docs-only`, `terraform-module`, `example-terraform`, `ci-workflow`, or `mixed`). | SkillsDocsRules | |
Comprehensive documentation and best practices for building Terraform providers with terraform-plugin-framework (v1.17.0). Covers providers, resources, schemas, types, validators, testing, and common pitfalls. | DocsRules | |
Configures Prometheus scraping, log drains, and observability for Supabase infrastructure monitoring. Contains: supabase-observability-metrics Sets up Prometheus scrape jobs targeting the Supabase metrics endpoint and configures log drain pipelines. Establishes monitoring dashboards and alerting baselines. Use when configuring Supabase monitoring, setting up Prometheus metrics, implementing log drains, or establishing observability for production Supabase deployments. | SkillsDocsRules | |
v1.2.2 Reference tile for Themis, a Node.js and TypeScript unit test framework designed for AI coding agents. Covers unit-test authoring, Jest/Vitest migration, agent-readable failure output with repair hints, and first-class integrations for Claude Code, Cursor, and generic agents. Contains: themis Use when the user asks to write unit tests, generate a test suite, or migrate/convert Jest or Vitest tests to Themis in Node.js/TypeScript repos. Produces Themis-native tests, runs validation commands, and applies Themis migration workflows. | SkillsDocsRules | |
Database architecture skills, docs, and rules for high-demand multi-tenant commerce platforms (PostgreSQL source of truth, Neo4j as derived GraphRAG projection, transactional outbox, RLS-based tenant isolation). Includes live schema introspection workflow via explicit Supabase MCP/read-only schema sources. Contains: adr-drafting Use when the user proposes — or the agent detects — a deviation from constitutional defaults that requires an Architecture Decision Record. Triggered by proposals to extract microservices, drop foreign keys, denormalize without measured evidence, store transactional truth in Neo4j, skip Row Level Security, skip the transactional outbox, run destructive migrations, use database-per-service, or any explicit override of a constitutional principle. Drafts a structured ADR with context, decision, consequences, alternatives rejected, migration path, validation criteria, and constitutional sections affected — and refuses to proceed with the underlying work until the ADR is at least Proposed status. commerce-database-architecture Use when designing or reviewing database architecture for high-demand multi-tenant commerce platforms — including PostgreSQL schema design, foreign keys, indexes, JSONB usage, multi-tenant isolation with Row Level Security, transactional outbox, Neo4j GraphRAG projections, event sourcing decisions, audit logging, partitioning, expand/contract migrations, and product/inventory/order modeling for restaurants, boutiques, drugstores, retailers, distributors, grocery, hardware, or appliance businesses. Triggered by any request to design tables, design schemas, create migrations, model products/variants/inventory/orders/payments, choose between monolith and microservices, choose between PostgreSQL and Neo4j as source of truth, model multi-tenant data, design event flows, or review an ER diagram. graph-rag-boundary-review Use when reviewing or designing how Neo4j and GraphRAG interact with PostgreSQL transactional truth — including any feature involving recommendations, semantic product search, ingredient relationships, substitution suggestions, complementary products, AI-assisted discovery, vector search combined with graph traversal, or any proposal that puts orders, inventory, payments, prices, or tenant access rules into Neo4j. Evaluates architectural proposals for data boundary violations, identifies sync pattern errors between Neo4j and PostgreSQL, produces structured design review feedback with severity-ranked findings, counter-proposals with Mermaid diagrams, eventing changes, and re-projection plans. Triggered by mentions of GraphRAG, Neo4j, knowledge graph, recommendations engine, semantic search, vector + graph hybrid search, AI product discovery, or any design that crosses the PostgreSQL ↔ Neo4j boundary. mermaid-diagram-review Use when the user shares a Mermaid ER diagram, schema sketch, or relationship diagram and asks for review, feedback, validation, or critique — including phrases like "what do you think of this", "look at this diagram", "I have this in mind", "can we model it like this", or pastes any block starting with "erDiagram" or "classDiagram". Validates the diagram against the constitution, returns a five-section structured response (constitutional violations, counter-proposal with improved Mermaid, migration plan if existing schema applies, test surface, open questions), and never silently accepts a design that violates tenant isolation, eventing, or graph-RAG boundaries. outbox-and-eventing-design Use when designing or reviewing the eventing layer of the commerce platform — including transactional outbox tables, outbox relays, domain event catalogs, idempotency keys, audit logs, memento snapshots, event sourcing decisions, and Neo4j projection workers. Triggered by requests to design events, design integration with external systems, design notification flows, design data sync to Neo4j or analytics warehouses, decide between event sourcing and CRUD, or review existing outbox / audit / event-sourcing schemas. postgres-schema-introspection Use when the agent needs to inspect the actual current state of a PostgreSQL database before answering a schema question — including before adding columns, before reviewing diagrams, before proposing migrations, or whenever the snapshot in .specify/memory/current-schema-state.md is stale. Connects via the configured Supabase MCP/read-only schema source, queries catalog metadata through list_tables or SELECT-only catalog SQL, and refreshes the snapshot file. Triggered by any "let me check what's already there", "what columns does X have", "what indexes exist on Y", "what does the schema look like", or by the snapshot being older than 24 hours. schema-evolution-workflow Use when the user wants to add an attribute, modify an entity, or model a new concept on top of an existing PostgreSQL schema — including phrases like "add a column to", "track this on the product", "we need to store", "model this concept", "extend the order with", "where should this live", or "should this be a new table". Inspects current state via the postgres-schema-introspection skill, runs a five-placement analysis (new column / JSONB key / EAV row / new related table / wrong entity), maps blast radius across foreign keys, outbox events, Neo4j projections, RLS, and indexes, and produces an expand/contract migration plan with rollback and tests. | SkillsDocsRules | |
A Claude AI skill that reviews and fixes clause numbering and stale cross-references in legal contracts and agreements. | Docs | |
Enforces absolute data boundaries between tenants in shared schema via RLS policies on tenant_id. Contains: tenant-isolation-rls Creates RLS policies enforcing tenant_id isolation on shared-schema tables. Verifies ALTER TABLE ENABLE ROW LEVEL SECURITY before policy creation. Requires tenant_id column and custom-access-token-hook JWT claims. Use when implementing multi-tenant data isolation, tenant-safe queries, shared schema RLS, or tenant_id policy creation. | SkillsDocsRules | |
Expert OpenTelemetry guidance for collector configuration, pipeline design, and production telemetry instrumentation across Kubernetes, ECS, serverless, and standalone deployments. Use when configuring collectors, designing pipelines, instrumenting applications, implementing sampling, managing cardinality, securing telemetry, writing OTTL transformations, or setting up AI coding agent observability (Claude Code, Codex, Gemini CLI, GitHub Copilot). Contains: opentelemetry-skill Build OpenTelemetry collector configs, instrument services, transform telemetry with OTTL, and debug missing traces, metrics, or logs. Use for OTel/otelcol collector config, OTLP export, SDK instrumentation, sampling, cardinality, TLS/PII controls, Kubernetes/Helm values.yaml deployments, collector health and alerts, and AI coding-agent telemetry (Claude Code, Codex, Gemini CLI, GitHub Copilot). | SkillsDocs | |
Gemini Enterprise A2A configuration and rules. Contains: scaffold-gemini-agent Scaffolds a complete A2A agent specifically configured for Gemini Enterprise compatibility, including the JSON-RPC root path and a health check. | SkillsDocsRules | |
v0.8.4 Agent-native E2E runtime with verifiable safety. 16 MCP tools including alethia_propose_tests (agent generates tests from a URL), alethia_assert_safety (proves destructive actions are blocked), and the expect block: NLP primitive unique to Alethia. Zero-IPC; 2-5x faster than Playwright MCP per flow; signed evidence packs. Works with Claude Code, Cursor, Cline. Contains: alethia Use when the user asks to run E2E tests, verify a web page, generate tests for an app, prove destructive actions are blocked, check if a UI element is visible, fill out a form, or drive a browser with natural language. Returns per-step results with safety classifications, policy decisions, DOM diffs, structured page context, and a signed audit trail. | SkillsDocsRules | |
v0.1.2 Guidelines for naming MCP tools, describing parameters, and documenting tools in a language- and framework-agnostic manner | Docs | |
Injects tenant ID and RBAC permissions into JWT via Postgres Auth Hooks during token issuance. Contains: custom-access-token-hook Generates Postgres Auth Hook that injects tenant_id and serialized permissions into JWT app_metadata using jsonb_set. Use when implementing RBAC, multi-tenant JWT claims, custom access token hooks, or permission injection into Supabase auth tokens. | SkillsDocsRules | |
Configures database INSERT triggers that offload document chunking and embedding to Edge Functions. Contains: rag-ingestion-trigger-pipeline Creates Postgres INSERT triggers that fire Edge Functions for document chunking and embedding generation. Configures the ingestion pipeline from raw document insert to vector storage. Use when building RAG ingestion, embed-on-insert pipelines, database-driven document ingestion, or automated embedding workflows. | SkillsDocsRules | |
v0.1.0 Atomic Design micro-UI architecture Contains: generate-micro-ui Generates micro Flutter/Dart UI components (atoms/molecules) following Atomic Design patterns. Uses StatelessWidget for atoms and HookConsumerWidget for molecules, including form validation classes and auto-generated widget tests with mocktail mocks. Use when the user asks to create small Flutter UI components such as buttons, text fields, inputs, cards, or reusable widgets following Atomic Design principles, or when they need a Flutter atom, molecule, or micro-UI component with Riverpod integration. | SkillsDocsRules | |
Build and demo Java AI agent systems with langchain4j-agentic: workflow patterns, supervisor, custom Planner strategies (incl. the flagship typed-verdict / CriticResult-style critic pattern), plus MCP tools, A2A remote agents, build setup, and conference-demo storylines. Pinned to 1.15.0 / 1.15.0-beta25. Contains: langchain4j-agentic Build, scaffold, and demo Java AI agent systems with the langchain4j-agentic module — workflow patterns (sequential, loop, parallel, conditional), supervisor, and custom Planner strategies including the flagship typed-verdict (CriticResult-style) critic pattern, plus langchain4j-mcp tool servers and langchain4j-agentic-a2a remote agents. Use whenever the user mentions LangChain4j, langchain4j-agentic, Java AI agents, @Agent / @Tool, AgenticScope, AgenticServices, sequenceBuilder / loopBuilder / supervisorBuilder / plannerBuilder, MCP or A2A in Java, or wants to build a conference demo / workshop / POC around autonomous Java agents. Pinned to 1.15.0 core + 1.15.0-beta25 agentic/mcp; refresh with scripts/check_versions.sh. | SkillsDocsRules | |
v0.2.0 Schema Registry for Apache Kafka - covers schema management (Avro, Protobuf, JSON Schema), compatibility modes, schema evolution, REST API, serializer/deserializer configuration, Kafka Connect converters, Flink SQL integration, and Confluent Cloud. Contains: schema-registry Use when working with Schema Registry for Apache Kafka, Confluent Platform, or Confluent Cloud. Covers schema management (Avro, Protobuf, JSON Schema), compatibility modes, schema evolution, REST API, serializer/deserializer configuration, Kafka Connect converters, and Flink SQL integration with Schema Registry. Trigger this skill whenever the user mentions schema registry, schema evolution, Avro/Protobuf/JSON Schema serialization with Kafka, subject naming strategies, compatibility checking, or Flink SQL with Confluent formats (avro-confluent). Also trigger when users ask about data contracts, schema validation, or serializer/deserializer configuration for Kafka producers and consumers. | SkillsDocs | |
v0.1.1 Context tile for pidge notification library v3 - async API with NotificationClient, Message, and dispatch pattern Contains: pidge-integration Configures NotificationClient handlers, implements async dispatch workflows, and handles DispatchError failures for the pidge v3 notification library in async Python services. Use when working with pidge, pidge v3, pidge notifications, NotificationClient, async notifications, or pidge integration — including setting up API keys, dispatching messages, and handling delivery errors. | SkillsDocsRules | |
A curated collection of Agent Skills for working with dbt, to help AI agents understand and execute dbt workflows more effectively. Contains: creating-mermaid-dbt-dag Generates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks. Use when visualizing dbt model lineage and dependencies as a Mermaid diagram in markdown format. migrating-dbt-core-to-v2 Use when a user needs help triaging dbt-core to dbt v2 migration errors. Runs dbt-autofix first, then classifies remaining errors into actionable categories (auto-fixable, guided fixes, needs input, blocked). migrating-dbt-project-across-platforms Use when migrating a dbt project from one data platform or data warehouse to another (e.g., Snowflake to Databricks, Databricks to Snowflake) using dbt Fusion's real-time compilation to identify and fix SQL dialect differences. upgrading-dbt Use when a user wants to upgrade, update, or migrate a dbt project to the latest version — e.g. "upgrade my dbt project," "migrate this off dbt 1.5," "get this project running on the latest dbt," "bump the dbt version." Upgrades a dbt v1 project (on 1.3, 1.4, 1.5, 1.6, or 1.7) all the way to 1.12, applying the required breaking, behavior, and deprecated changes from a data-driven issue corpus. Inputs — starting_version (the project's current dbt minor, one of 1.3/1.4/1.5/1.6/1.7) and adapter_type (snowflake/redshift/bigquery/databricks/spark); both are normally supplied by the caller (e.g. the dbt VS Code extension), with fallbacks described in the skill. adding-dbt-unit-test Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt. answering-natural-language-questions-with-dbt Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development. building-dbt-semantic-layer Use when creating or modifying dbt Semantic Layer components — semantic models, metrics, dimensions, entities, measures, or time spines. Covers MetricFlow configuration, metric types (simple, derived, cumulative, ratio, conversion), and validation for both latest and legacy YAML specs. configuring-dbt-mcp-server Generates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt. Use when setting up, configuring, or troubleshooting the dbt MCP server for AI tools like Claude Desktop, Claude Code, Cursor, or VS Code. fetching-dbt-docs Retrieves and searches dbt documentation pages in LLM-friendly markdown format. Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer. maintaining-dbt-documentation Audits dbt documentation coverage and drafts missing model/column descriptions in the project's own house style, one folder at a time, for human review. Use when documenting undocumented models, backfilling missing YAML descriptions, auditing doc coverage, or keeping schema YAML in sync with model SQL — especially on multi-contributor projects where new models routinely land undocumented. querying-the-dbt-information-schema Use when answering questions about a dbt v2 project's own metadata — which models, sources, tests, columns, configs, tags, packages or lineage exist, what is untested or undocumented, what depends on what, how long models took in the last run — or when using `dbt show --info`, `{{ info_schema() }}`, `--generate-info-schema`, `target/info_schema/`, or writing `dbt check` SQL. Prefer this over grepping YAML or parsing `manifest.json` on dbt v2. running-dbt-commands Formats and executes dbt CLI commands, selects the correct dbt executable, and structures command parameters. Use when running models, tests, builds, compiles, or show queries via dbt CLI. Use when unsure which dbt executable to use or how to format command parameters. troubleshooting-dbt-job-errors Diagnoses dbt Cloud/platform job failures by analyzing run logs, querying the Admin API, reviewing git history, and investigating data issues. Use when a dbt Cloud/platform job fails and you need to diagnose the root cause, especially when error messages are unclear or when intermittent failures occur. Do not use for local dbt development errors. using-dbt-for-analytics-engineering Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes. using-dbt-state Use when a user is enabling, configuring, optimizing, or debugging dbt State (the server-backed reuse mechanism that clones or skips nodes instead of rebuilding them). Use when they conflate dbt State with the `state:modified` selector or `--state` deferral. Use when asked about models rebuilding unexpectedly, views with `select *` rebuilding, volatile SQL (`current_timestamp()`, `random()`) rebuilding or not, cross-developer cloning, lag_tolerance. working-with-dbt-mesh Use when changing a dbt model in a way that could break its consumers — renaming, removing, or retyping a column, or changing a model that downstream models, exposures, dashboards, or BI tools depend on — to judge whether the change is breaking and who it affects. Also use when versioning a model (model versions, latest_version, latest_version_pointer, deprecation_date, migration windows), enforcing contracts, setting access or groups, or doing multi-project dbt Mesh work (cross-project refs via dependencies.yml, disambiguating similarly-named models, splitting a monolith). Covers single- and multi-project, and planning or advising as well as implementing. | SkillsDocs | |
Implements TUS resumable upload protocol for files >6MB with advisory lock conflict handling. Contains: storage-resumable-upload Configures TUS resumable upload client for files over 6MB with Postgres Advisory Lock 409 Conflict handling. Enforces tenant-scoped bucket paths. Use when implementing large file uploads, resumable uploads, TUS protocol, uploads over unstable networks, or files larger than 6MB in Supabase Storage. | SkillsDocsRules | |
Enforces strict isolation of service_role key to server-side contexts only. Contains: service-role-boundary Enforces that service_role key is never exposed to client-side code. Validates admin client isolation, privileged operations routing, and server-only key usage. Use when implementing admin operations, server-side Supabase clients, or auditing service_role key usage. | SkillsDocsRules |
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