Designing features for the "Action Era" that are AI-accessible by default
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See how to installReview the instructions the agent will receive before you install.
Features in LivestockAI must now be designed for dual consumption: Humans (UI) and Agents (MCP/API).
Every feature must be fully functional via Server Functions before any UI is built.
Test: Can an agent complete the entire user story using only bun run check-feature-x.ts (a script calling server functions)?
If yes -> Agent Ready.
If no (logic lives in React components) -> Refactor immediately.
Server functions are the tools we give to agents. Document them accordingly.
export const createBatchFn = createServerFn({ method: 'POST' })
.inputValidator(batchSchema)
.handler(async ({ data }) => {
/* ... */
})
/**
* @description Creates a new livestock batch.
* @workflow
* 1. Check `getFarmFacilities` for space.
* 2. `createBatchFn`
* 3. Log initial `feed_record` if applicable.
*/
Agents operate on Intent, not just data.
Instead of generic CRUD (updateBatch), expose semantic actions (graduateBatch, quarantineBatch).
// ❌ Generic
updateBatch(id, { status: 'sold' })
// ✅ Semantic (Agent Friendly)
markBatchAsSold(id, { date, price, customer })
Semantic actions allow agents to:
Agents need a standard way to ask for permission for high-stakes actions (e.g., ordering feed, selling stock).
The ApprovalRequest Entity:
agentId: Who is asking?action: JSON payload of the server function to call.summary: Human-readable explanation ("I want to order 50 bags of Starter Feed").status: PENDING | APPROVED | REJECTEDAll major entities (Batches, Sales) should have an ai_metadata JSONB column.
Agents use this to store reasoning ("Predicted harvest date change due to low feed intake").
Do not show this raw JSON to users, but use it to power UI "Insights".
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[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.
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Configures 2-Agent workflow between PM and implementation roles. Use when user mentions 2-Agent, 2エージェント, PM連携設定, Cursor設定, Cursor連携, 2-agent運用. Do NOT load for: 単独運用, ワークフロー実行, ハンドオフ処理.
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Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.
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Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.
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Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.
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Add new Agent templates to the meta-agent-skills framework.
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