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Platform Adapter: Claude Code

Capabilities

Feature	Support
Parallel sub-agents	Yes — multiple `Agent` tool calls in one response
Nested sub-agents	Yes (not needed for party mode)
Inline prompt injection	Yes — full prompt passed at spawn time
Pre-defined agent files	Not required
Model selection	Yes — `model` parameter per agent
Tool access in sub-agents	Based on `subagent_type`

How to Spawn a Party Mode Agent

Use the Agent tool for each selected agent:

Agent tool call:
  description: "{displayName} responds to discussion"
  subagent_type: "general-purpose"
  prompt: <assembled from ./references/agent-prompt-template.md>
  model: <optional — see Model Selection below>

Key parameters:

description — Short label: e.g., "Winston responds to architecture question"
subagent_type — Always "general-purpose" for party mode agents
prompt — Fully assembled agent prompt with personality, context, depth signal, and user message
model — Optional override (see below)

Model Selection Strategy

Claude Code supports per-agent model selection. Use this to optimize cost and speed:

Round calibration	Model	Rationale
Depth: "brief", simple factual question	`"haiku"`	Fast, cheap — no need for heavy reasoning
Depth: "standard", normal discussion	Omit (inherit current)	Default model handles this well
Depth: "deep", complex analysis	Omit (inherit current)	Full capability needed
Cross-talk reactions (2-3 sentences)	`"haiku"`	Short reactive responses don't need heavy models
Farewell responses	`"haiku"`	1-2 sentences of in-character goodbye

Only use "haiku" when the response is genuinely simple. When in doubt, omit the parameter.

Parallel Execution

To spawn agents in parallel, include multiple Agent tool calls in a single response message. Claude Code executes them concurrently and returns all results together.

Example for a 3-agent round:

Response contains:
  Agent call 1: description="Winston responds", prompt=<winston_prompt>
  Agent call 2: description="Maya responds", prompt=<maya_prompt>
  Agent call 3: description="Rex responds", prompt=<rex_prompt>

All three run simultaneously. Collect all results before presenting to user.

Cross-Talk Pass

For cross-talk, spawn agents sequentially (one Agent call per response) so each can see previous outputs. Include Pass 1 responses in the prompt under "Other Agents' Responses This Round".

Consider using model: "haiku" for cross-talk since responses are short reactions.

Constraints

Sub-agents return text results to the orchestrator — not visible to user until presented
Each sub-agent gets a fresh context (no conversation history — include relevant context in prompt)
Sub-agents should NOT use tools — instruct them to respond with text only
Token cost scales linearly with agents spawned per round

Optimization

Single agent for simple questions — skip parallel overhead
Keep conversation context under 400 words
Use "haiku" for brief/reactive rounds to save tokens and time
The orchestrator's context window is the bottleneck in long sessions — maintain the compaction state block diligently
If a spawn fails, present remaining agents normally — don't retry or block

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