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.

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).

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.

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).

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.

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.

Interview stakeholders to clarify ambiguous or underspecified requirements before writing code. Use when receiving a new task, feature request, or bug report that lacks clear acceptance criteria. Produces clarified requirements ready for spec authoring. Common triggers: "new feature", "build me", "implement", "add support for", or any task where requirements are vague or incomplete.

Verify that implementation and tests remain synchronized with specs after code changes. Use when code has been generated or modified from specs, after implementation is complete, or when reviewing a PR that touches spec-covered code. Reports mismatched targets, broken test links, and undocumented behavioral changes. Common triggers: "verify the spec", "check spec alignment", "are specs up to date", or after completing implementation work.

Create or update .spec.md files from clarified requirements. Use when requirements have been gathered and confirmed, and specs need to be written or updated before implementation begins. Produces well-structured spec files with frontmatter, requirements, and test links. Common triggers: "write the spec", "update the spec", "create a spec for", or after requirement-gathering completes.

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.

Audits a Claude Code skill for security risks in three modes: before download (from a URL or install command), after download but before install (from a .skill file), or after install (from a local skills directory). Use this skill whenever a user is about to install a skill from any source — including GitHub URLs, git clone commands, npx/npm commands, curl/wget downloads, pip installs, marketplace links, or raw SKILL.md URLs. Also trigger when a user asks "is this skill safe?", "should I trust this skill?", "can you check this before I install it?", "audit this skill", or pastes any link to a skill repository or .skill file. If a user mentions installing ANY skill, proactively offer to audit it first — do not wait for them to ask.

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.

Configures Edge Functions to use EdgeRuntime.waitUntil for background work while returning immediate 200 OK. Routes massive async workloads through pg_cron discovery and pgmq task queuing. Use when building background processing, async third-party calls, long-running edge function tasks, or webhook-driven pipelines.

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.

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).

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.

Route and calibrate research drawn from socially noisy web communities so agents do not mistake crowd mood for truth. Use when research notes, summaries, or quoted material come from Moltbook, Hacker News, Reddit, or Product Hunt and the job is to decide how much weight that material deserves, separate concrete weak signals from vibe/noise, or choose the right source-specific calibration skill before carrying findings forward.

Calibrate research done on Hacker News so agents do not mistake experienced technical cynicism, anti-hype sentiment, or comment-thread confidence for balanced evidence. Use when summarizing Hacker News reactions, extracting concerns from HN threads, citing HN as part of research, or deciding how much weight to give repeated negative or skeptical Hacker News comments.

Interpret and calibrate already-collected research material derived from Moltbook so agents do not mistake noise, spam, novelty, or social heat for reliable evidence. Use when weighing notes, summaries, or quoted material from Moltbook as part of research, deciding whether a Moltbook claim is worth following up, checking source reliability or evidence quality, or separating concrete weak signals from social-performance noise. Prefer when Moltbook is being used as a weak-signal discovery source rather than as authoritative proof. This skill classifies gathered Moltbook material as concrete report, vibe signal, or noise; flags evidence strength and uncertainty; and suggests whether the claim is follow-up-worthy. This is an informational calibration skill, not a browsing or execution workflow.

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.

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.

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.

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.

Creates Postgres triggers that fire database webhooks to Edge Functions or external endpoints on INSERT/UPDATE/DELETE events. Configures pg_net for HTTP callouts and payload serialization. Use when implementing event-driven workflows, database webhooks, trigger-based notifications, or automated pipelines on table changes.

Inspects database health by analyzing slow queries, identifying bottlenecks, and checking Postgres performance indicators. Use when diagnosing database issues, checking slow queries, inspecting DB health, finding performance bottlenecks, or troubleshooting Supabase Postgres problems.

Executes EXPLAIN ANALYZE via MCP to debug slow queries, identify missing indexes, detect sequential scans, and optimize query plans. Use when debugging slow SQL, analyzing query plans, finding unused indexes, optimizing Postgres queries, or investigating index-not-used issues.