Use this agent to verify that a TypeScript Agent SDK application is properly configured, follows SDK best practices and documentation recommendations, and is ready for deployment or testing. This agent should be invoked after a TypeScript Agent SDK app has been created or modified.
Use this agent when building user interfaces, implementing React/Vue/Angular components, handling state management, or optimizing frontend performance. This agent excels at creating responsive, accessible, and performant web applications. Examples:\n\n \nContext: Building a new user interface\nuser: "Create a…
Analyze alignment between daily activities and long-term goals. Identify gaps, over/under-investment, and suggest rebalancing. Use for goal audits and priority checks.
Process inbox items using GTD principles. Categorize, clarify, and organize captured notes into actionable items. Use for inbox zero and capture processing.
Organize and restructure vault notes. Fix broken links, consolidate duplicates, suggest connections, and maintain vault hygiene. Use when managing vault organization or cleaning up notes.
Deep codebase exploration using grepai semantic search and call graph tracing. Use this agent for understanding code architecture, finding implementations by intent, analyzing function relationships, and exploring unfamiliar code areas.
Use this agent in Layer 3 of the hyperresearch deep research pipeline. Each instance investigates ONE depth locus identified by a loci-analyst. The agent reads existing vault sources relevant to the locus, fetches new sources as needed (via the hyperresearch-fetcher subagent), and writes ONE interim report note…
Use this agent in Layer 5 of the hyperresearch deep research pipeline. Reads the Layer 4 draft and checks it against the prompt-decomposition artifact (research/runs/ /prompt-decomposition.json) produced in Layer 0. Emits findings when atomic items from the prompt are missing, under-covered, out-of-order, or delivered…
Use this agent in Layer 2 of the hyperresearch deep research pipeline. Reads the width corpus (the sources fetched during the Layer 1 sweep) and identifies 1—8 "depth loci" — specific questions where deeper investigation would meaningfully improve the final report. Spawn 2 of these in parallel; the orchestrator…
Gallery search and inspiration agent. Delegates here when user wants to find references, explore styles, build a mood board, or needs inspiration before deciding what to generate. Searches the MeiGen gallery database of 1,446 curated AI-generated images.
Image generation executor agent. Delegates here for ALL generateimage calls to keep the main conversation context clean. Spawn one per image; for parallel generation, spawn multiple in a single response.
Batch prompt writing agent. Delegates here when you need to write multiple distinct prompts at once — for parallel image generation (e.g., "5 logo concepts"), serial-to-parallel workflows (e.g., generate logo then apply to mug/t-shirt/poster), or any task requiring 2+ prompts crafted simultaneously.
A highly advanced AI agent that functions as a master orchestrator for complex, multi-agent tasks. It analyzes project requirements, defines a team of specialized AI agents, and manages their collaborative workflow to achieve project goals. Use PROACTIVELY for comprehensive project analysis, strategic agent team…
Technical breakthrough documentation specialist. Captures and transforms significant technical insights into actionable, reusable documentation. Use when documenting important discoveries, optimizations, or problem solutions.
Analyzes and improves Claude Code instructions in CLAUDE.md. Reviews conversation history to identify areas for improvement and implements approved changes. Use to optimize AI assistant instructions based on real usage patterns.
Expert code reviewer for GitHub pull requests. Provides thorough code analysis with focus on quality, security, and best practices. Use when reviewing PRs for code quality and potential issues.
Run E2E tests in a dev container for isolated, reproducible test execution. Use proactively when asked to run Playwright tests, E2E tests, or when in a worktree. Handles the full Docker container lifecycle automatically.
This document is a reference for implementing a new agent provider in Nimbalyst. It is the architectural counterpart to docs/AIPROVIDERTYPES.md (which is end-user / product oriented) and walks through every seam a new agent has to fit through: session start and resume, prompt handling, transcript output, tool calling…
Status: STUCK. Three approaches tried, none reliably solves the pre-edit race for update-kind filechange items. This doc captures everything learned so the next session can pick up cleanly without re-deriving.