Use this agent for Metabase Clojure backend work on database driver system, metadata sync, schema introspection, fingerprinting, field value caching, or driver-specific behavior. This includes adding or modifying database drivers, fixing JDBC metadata issues, debugging sync processes, working with the driver…
Use this agent for Metabase Clojure backend work on query processor (QP), MBQL query language, SQL compilation, driver system, middleware pipeline, Lib, metadata providers, or streaming execution. This includes debugging query compilation issues, adding new MBQL clauses, fixing database-specific SQL generation bugs…
Use this agent for Metabase Clojure backend work on the module system itself — adding new modules, splitting/merging modules, configuring .clj-kondo/config/modules/config.edn, resolving circular dependencies, designing module APIs, deciding where code should live (.core vs .api vs .init vs .models.), interpreting…
An AI and machine-learning engineering agent for adding language models and other AI features to software. It covers prompts, document search with generated text, and multi-step agent workflows.
An agent that designs complex business workflows using state machines, events, diagrams, and failure-recovery steps. A state machine describes the allowed stages of a process and the events that move it between stages.
An agent that measures software performance and investigates CPU, memory, input/output, and network bottlenecks. A performance baseline is a repeatable set of measurements used to detect later changes.
Deep single-subsystem audit that reasons about state over time (caches, upserts, migrations, concurrent scans) rather than pattern-matching lines. Used by /bug-audit on the riskiest subsystems; runs on Opus for reasoning depth.
Fast, wide sweep of one audit dimension across the codebase. Returns candidate findings with file:line evidence for the verifier to confirm. Used by /bug-audit; runs on Sonnet for breadth per token.
Adversarially verifies one candidate finding from /bug-audit — tries to REFUTE it by reading the code and, where cheap, reproducing it with a throwaway script. Kills false positives before they reach the report. Runs on Opus.
Use this agent for architectural decisions, feature design, code review, or refactoring in this TanStack Start back-office / dashboard starter ("中台" template). It knows the resource pattern, the Repository data layer, the page archetypes, and the ALWAYS/NEVER conventions, and it designs changes that stay portable and…
Use this agent when designing or implementing UI components for ccboard's web frontend (Leptos WASM). Specializes in Leptos reactive patterns, Rust-to-WASM UI, dark mode design, data visualization, and the ccboard design system. Examples: creating a new dashboard widget, implementing a chart component, designing a…
Use this agent when you need UX critique, information architecture analysis, or feature design validation for ccboard. Specializes in developer tool UX, data density optimization, keyboard-first interaction design, and ensuring new features match how developers actually think and work. Examples:\n\n \nContext: User…
Use this agent when designing or reviewing TUI (Terminal UI) components in ccboard using Ratatui. Specializes in terminal constraints, keyboard navigation, information density, widget selection, layout algorithms, and visual hierarchy in 256-color terminals. Examples: designing a new tab layout, choosing between…
Builds PyWry widgets, dashboards, chat UIs, and TradingView charts end‑to‑end by orchestrating the PyWry MCP tools. Use when the user asks to build, scaffold, or iterate on a PyWry app and the work involves multiple MCP tool calls (e.g. create widget → populate data → add toolbar → wire events → export).
Use proactively when user asks to plan, design, or architect a feature. Produces architecture designs, folder structure, data flow diagrams, and risk assessment. Read-only — markdown and ASCII diagrams only, no code.
Use proactively when user asks to create an issue, write a ticket, or define requirements. Analyzes change requests, asks clarifying questions, and creates well-structured GitHub issues with acceptance criteria.
Implement a ReviewFlow feature via TDD inside-out. Receives a validated plan (docs/plans/ .plan.md) and spec (docs/specs/ .md), implements all layers with RED-GREEN-REFACTOR, self-reviews, and persists a report. Triggers when the user says "implement feature", "start implementation", "implement spec", or "run…
Plan a ReviewFlow feature implementation. Analyzes a spec in docs/specs/, maps it to Clean Architecture layers (Entity → Use Case → Gateway → Controller), and produces a structured plan in docs/plans/ .plan.md. Triggers when the user says "plan feature", "plan implementation", "analyse spec", or "create plan for".