agent-bridge-kit

Analyzes user's requests, determines tech stack, plans structure, and coordinates agents.

Apply consistent changes across many files at once. One pattern, many targets.

**MANDATORY:** Use for complex/vague requests, new features, updates.

Reduce AI token usage by **6.8x average** (up to **49x** on monorepos) by giving the AI a structural map of your codebase instead of letting it read everything.

Keep sessions productive by compressing completed work while preserving key decisions.

Advanced multi-agent coordination with parallel dispatch and synthesis. Use for complex tasks requiring multiple specialist perspectives.

Distilled from production-proven coordinator patterns. Transforms sequential agent chains into intelligent parallel orchestration.

**Philosophy:** Every pixel has purpose. Restraint is luxury. User psychology drives decisions.

Enables agents to remember across sessions. Never re-discover what was already learned.

Coordinate multiple agents for complex tasks. Use for multi-perspective analysis, comprehensive reviews, or tasks requiring different domain expertise.

Orchestration through AG Kit's built-in Agent Tool

Create project plan using project-planner agent. No code writing - only plan file generation.

Save information to persistent memory for cross-session recall. Stores preferences, conventions, decisions, and context.

The best code is the code you don't have to write. The second best is the code anyone can read.

Turn repetitive patterns into reusable skills. If you've done it three times, it should be a skill.

"Code that exists" ≠ "Code that works." This skill ensures changes are verified through execution.

Verify code changes work by running them. Proves through execution, not just inspection.

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

Apply operations across multiple files simultaneously. Pattern-based bulk modifications, search-and-replace across codebases, consistent changes to many files at once.

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

Socratic questioning protocol + user communication. MANDATORY for complex requests, new features, or unclear requirements. Includes progress reporting and error handling.

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Code review guidelines covering code quality, security, and best practices.

Token-efficient code review using Tree-sitter AST graphs and MCP. Reduces AI assistant token usage by 6.8–49x by computing blast radius of changes instead of reading entire codebases. Uses SQLite graph database for structural analysis.

Manage and compress conversation context in long sessions. Detect when context is growing large, summarize completed work phases, archive old findings while preserving key decisions. Prevents context degradation.

Advanced multi-agent orchestration with parallel workers, synthesis protocols, and coordinator lifecycle. Use when complex tasks require multiple agents working in parallel with intelligent result synthesis.

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.

Documentation templates and structure guidelines. README, API docs, code comments, and AI-friendly documentation.

Design thinking and decision-making for web UI. Use when designing components, layouts, color schemes, typography, or creating aesthetic interfaces. Teaches principles, not fixed values.

Game development orchestrator. Routes to platform-specific skills based on project needs.

Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

Internationalization and localization patterns. Detecting hardcoded strings, managing translations, locale files, RTL support.

Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.

Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

Persistent cross-session memory management. Enables agents to remember user preferences, project conventions, and past decisions across different sessions using a structured MEMORY.md index and topic files.