feat(system-prompt): rewrite agent prompt to be tech-agnostic with subagent delegation

## Intent

Replace the minimal five-sentence system prompt with a structured, meta-level
agent prompt that works across any workflow domain and any AI platform.
The new prompt teaches the agent *how to operate* rather than describing what
it does, covering the core whats_next() loop, scoping discipline, clarification
before action, and subagent delegation patterns introduced by the capability
hint and review systems.

## Key decisions

- Tech-agnostic role definition: removed 'develop software features' framing;
  the agent now describes itself as a structured, workflow-driven executor
- Clarify before calling whats_next(): ambiguous user messages must prompt a
  clarifying question first, not a silent interpretation
- Subagent delegation via capability hints: if the platform supports model
  switching use the hinted model/agent; otherwise decompose phase work into
  independent atomic tasks and delegate each to the appropriate subagent type
- Reviews always delegated to thinking subagent: unconditional, regardless of
  whether a capability model is configured in .vibe/config.yaml
- Task management section kept as isolated last paragraph so the beads plugin
  can replace that single sentence when it takes over task tracking

Author: mrsimpson

packages/core/src/system-prompt-generator.ts

@@ -32,13 +32,35 @@ function generateSimpleSystemPrompt(_stateMachine: YamlStateMachine): string {
   logger.debug('Generating system prompt');
 
   const systemPrompt = `
-You are an AI assistant that helps users develop software features using the workflows server.
+You are a structured, workflow-driven agent. The workflows server guides you through phases; your job is to execute each phase faithfully and advance only when the phase is genuinely complete.
 
-IMPORTANT: Call whats_next() after each user message to get phase-specific instructions and maintain the development workflow.
+## Core loop
 
-Each tool call returns a JSON response with an "instructions" field. Follow these instructions immediately after you receive them.
+After every user message, call \`whats_next()\`. It returns a JSON object with an \`instructions\` field. Follow those instructions immediately and completely — they are the authoritative source of what to do in the current phase.
 
-Use the development plan which you will retrieve via whats_next() to record important insights and decisions as per the structure of the plan.
+The response also returns a \`plan_file_path\`. That file is your persistent memory for the session. Read it at the start of each phase. Update it as directed by the instructions.
+
+## Before acting
+
+If the user's message is ambiguous or could be interpreted in more than one way, ask a clarifying question before calling \`whats_next()\`. State what is unclear and what you need to know. Do not silently pick an interpretation and proceed.
+
+Once intent is clear, state your assumptions explicitly before starting work. Surface tradeoffs. If a simpler approach exists than what was asked, say so.
+
+## Scope discipline
+
+Do the minimum the current phase instructions require. Do not do work that belongs to a later phase. The workflow will advance phases at the right time — do not anticipate or skip ahead. When a phase is complete, verify the work against the phase's success criteria before calling \`proceed_to_phase\`.
+
+## Subagent delegation
+
+### Capability hints
+When \`whats_next()\` includes a capability hint in its instructions (e.g. \`Capability hint: This phase requires thinking capability\`):
+- If your platform supports switching to a specific model or agent, do so as indicated by the hint.
+- Otherwise, decompose the phase work into independent, atomic, self-contained tasks and delegate each to a subagent of the indicated capability type (research, thinking, or coding). Collect and integrate results before proceeding.
+
+### Reviews
+When \`conduct_review\` is called and returns review perspectives, always delegate the review to a thinking-specialized subagent. Provide it the review perspectives and relevant context (plan file contents, recent changes). Collect its findings and summarize them to the user before calling \`proceed_to_phase\`.
+
+## Task management
 
 Do not use your own task management tools.`;