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AI Agent Interface Kit

Your AI agent works in text. You think in visuals. This kit bridges the gap. Three integration paths (Notion, Linear, open-source kanban, or any REST API), a dispatcher shim that lets you swap backends with one environment variable, and a two-board architecture that separates your tasks from your agent's automations. Hand the kit to your AI agent and it builds the interface layer. From someone who built custom software across 3 platforms, then replaced it all in one afternoon.

✓Architecture patterns from 54 commits, 21 bug fixes, and a one-day migration across 50+ automation scripts.

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What you get

+Dual-mode: knowledge for you, executable scripts for your agent. Hand the ZIP to any AI agent and it sets up the whole system
+Three integration paths: plug into Notion/Linear via API, fork an open-source kanban, or build custom
+Dispatcher shim pattern: swap backends with one env var, zero consumer script rewrites
+Two-board architecture: Ops board (your tasks) + Automations board (agent definitions that never close)
+Tag namespacing: replace 5 database tables with one concept (area/X, p/High, src/cli)
+Session lifecycle: one card per CLI session, subtasks as checklist steps, PID-based ownership
+Automation lifecycle: Intake to Idle to Running to Needs Attention, with comment gating for high-frequency runs
+Natural-language intake: drop a card like "Send me weather at 7am", agent converts to working automation
+Works with ANY AI agent: Claude Code, GPT, Gemini, Cursor, open-source, custom

Package includes

  • •Comprehensive guide (10 chapters, 10,000+ words): architecture, patterns, migration, rollback
  • •AGENT-INSTRUCTIONS.md: self-contained prompt any AI agent can read and execute to build the system
  • •setup.sh: interactive bootstrap that configures everything for your chosen backend
  • •DECISION-GUIDE.md: 5-question framework to pick the right path for your situation
  • •dispatcher.py: ~200-line reference implementation with abstract backend interface
  • •4 backend adapters: Notion API, Linear GraphQL, Fizzy/Rails, Generic REST (with Trello example)
  • •Session hooks: session-start.sh and session-end.sh for CLI integration
  • •Cron runner skeleton: drift-tolerant automation scheduler with lifecycle management
  • •Board setup configs: column definitions and tag scheme for both Ops and Automations boards
  • •Migration checklist: pre/during/post migration steps from real production experience
  • •Cheatsheet: single-page quick reference for commands, lifecycles, and common fixes
  • •Platform-specific setup guides for Notion and Linear

FAQ

Does this only work with Claude Code?

No. Every template uses generic environment variables (AGENT_BOARD_BACKEND, NOTION_API_KEY, etc.). The AGENT-INSTRUCTIONS.md works with any AI agent that can read markdown and run shell scripts.

Do I need to deploy Fizzy?

No. The kit supports three paths. The easiest is plugging into Notion or Linear via their APIs. You only deploy Fizzy (or another open-source kanban) if you want full control. The DECISION-GUIDE helps you choose.

What if I already use Notion/Linear for tasks?

Perfect. The kit includes backend adapters for both. Run setup.sh, pick your backend, enter your API key, and your agent starts creating cards on your existing board within minutes.

How is this different from the AI Agent Blueprint?

The Blueprint builds the agent itself (memory, skills, identity). This kit builds the visual interface layer on top. They complement each other. The Blueprint is about what the agent knows and does. This is about what you see.

Can I feed this to my AI agent?

Yes. That is the primary use case. AGENT-INSTRUCTIONS.md is a self-contained prompt. Your agent reads it, asks which backend you want, runs setup.sh, and builds the whole interface layer. You watch cards appear on your board.

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