bbot

Create and maintain ExecPlans for complex features or significant refactors. Use when planning work that requires design + implementation steps, especially when the repo requires ExecPlans in docs/exec-plans and strict format per PLANS.md.

OmniCore CLI usage via `bbot <command>` for starting kernel/supervisor, checking status, managing sessions, and updating config/secrets. Use when running or documenting OmniCore CLI commands or when CLI flags/behavior change.

Complete project architecture and structure guide. Use when exploring the codebase, understanding project organization, finding files, or needing comprehensive architectural context. Triggers on architecture questions, directory navigation, or project overview needs.

OmniCore architecture and developer guide reference. Use when asked to explain, review, or update OmniCore design philosophy, kernel/adapters/event sourcing, sessions/root paths, compaction and summaries, heartbeat, supervisor or CLI behavior, or to maintain developer-guide.md.

Guide for creating effective skills. This skill should be used when you need to create a new skill (or update an existing skill) that extends capabilities with specialized knowledge, workflows, or tool integrations.

TypeScript code style and optimization guidelines. Use when writing TypeScript code (.ts, .tsx, .mts files), reviewing code quality, or implementing type-safe patterns. Triggers on TypeScript development, type safety questions, or code style discussions.

Unified LLM API library (@mariozechner/pi-ai) for TypeScript/JavaScript that abstracts provider differences across OpenAI, Anthropic, Google, Azure, Mistral, Groq, xAI, Cerebras, Amazon Bedrock, GitHub Copilot, and OpenAI-compatible services. Use when implementing LLM integrations, building agentic workflows with tool calling, switching between AI providers, managing conversation context, handling streaming responses, implementing vision capabilities, or tracking token usage and costs. Triggers on installing/configuring pi-ai, defining tools with TypeBox schemas, implementing stream/complete calls, handling tool calls and results, cross-provider handoffs, context serialization, or any multi-provider LLM integration work.

Guide for building and understanding minimal, self-contained LLM coding agents following the design principles of Pi. Use when designing or customizing AI agents that act as code assistants, especially when opinionated simplicity, robust context management, multi-model support, and deterministic toolchains are desired.