Use when the user gives a short, rough, or one-line idea for an app, tool, script, or feature and wants it built end-to-end without being asked clarifying questions. Triggers - "build me...", "create an app that...", "I want a tool for...", "quick app for...", minimal specs, seed ideas.

Use when the user has an idea for an app, tool, or feature and wants to be interviewed in depth first — answering structured clarifying questions until the requirements are fully pinned down — before it gets built end-to-end. Triggers - "ask me questions then build", "interview me about my idea", "help me spec this out and build it", vague ideas the user wants to shape interactively.

Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.

Go testing patterns including table-driven tests, subtests, benchmarks, fuzzing, and test coverage. Follows TDD methodology with idiomatic Go practices.

Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.

Idiomatic Go patterns, best practices, and conventions for building robust, efficient, and maintainable Go applications.

PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.

Spring Security best practices for authn/authz, validation, CSRF, secrets, headers, rate limiting, and dependency security in Java Spring Boot services.

Test-driven development for Spring Boot using JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo. Use when adding features, fixing bugs, or refactoring.

JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot.

Keep gbrain current with this repo's code and refresh agent search guidance in CLAUDE.md. Wraps the gstack-gbrain-sync orchestrator with state (gstack)

Find out what audience signals exist for an idea about people. Describe who you're trying to reach in plain English and see what's out there — which signals Watt has, how big and how fresh each one is, and what related angles are worth a look. A guided, one-question-at-a-time walk, so you get the lay of the land before building anything from it. Start here to size up an idea or get your bearings — "what's out there for runners", "is Watt a fit for X", "what else relates to Y" — or type /watt:explore.

Audit, score, or improve existing skills with Tessl review output, metadata checks, repo conventions, and skill-authoring best practices. Use when creating or revising a skill, asking why a skill did not activate, checking skill quality, comparing a skill against `AGENTS.md`, `CLAUDE.md`, or repo rules, or preparing a skill for publish. Do not use to verify general application code or to rewrite unrelated docs.

Collect a testimonial (name, rating, text, email) and publish it to testimonials.html

Build distinctive, high-quality frontend interfaces. Use when users request web components, pages, landing pages, portfolios, or creative applications. Generates production-ready code with creative typography, layered color systems, fluid motion choreography, and inventive layouts. Eliminates generic AI aesthetics. Say "Prime" for maximum quality.

Design a loss function and harness for a long-running /goal optimization run (loss-function development, LFD). Use when the user wants to set up an autonomous optimization loop, distill a product from public artifacts, turn a spec into an optimization target, or asks to design a /goal. Observes the existing environment, interrogates the task, ingests or generates the spec, builds a blinded eval, generates and verifies the harness, red-teams the target for cheats, and emits goal.md ready to launch. Re-invoke in patch mode when a running loop cheated and the loss function needs patching.

Run a prepare → review → revise pass over an agent-written user-facing output (report, status update, strategy doc, review finding, handoff, recommendation) before it is finalized. Builds a short communications brief, reviews the draft against it and against named agent-writing failure modes (assumed context, numbered references without meaning, retained rejected ideas, leaked process history, buried recommendation), then revises or flags residual risk.