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model-selection

Choose the AI model for each agent launch using saved preferences, session instructions, agent settings, and task type. Save global or per-agent choices for future sessions and include the selected model in launch acknowledgments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Determines which LLM model to use for each agent spawn

설치 방법 보기

이럴 때 유용합니다

  • Choose models automatically by task type
  • Save a default model for all agents
  • Assign a persistent model preference to one agent
  • Return from a saved default to automatic selection

포함된 파일(1)

  • SKILL.md5.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Model Selection

Determines which LLM model to use for each agent spawn.

SCOPE

✅ THIS SKILL PRODUCES:

  • A resolved model parameter for every task tool call
  • Persistent model preferences in .squad/config.json
  • Spawn acknowledgments that include the resolved model

❌ THIS SKILL DOES NOT PRODUCE:

  • Code, tests, or documentation
  • Model performance benchmarks
  • Cost reports or billing artifacts

Context

Squad supports 18+ models across three tiers (premium, standard, fast). The coordinator must select the right model for each agent spawn. Users can set persistent preferences that survive across sessions.

5-Layer Model Resolution Hierarchy

Resolution is first-match-wins — the highest layer with a value wins.

LayerNameSourcePersistence
0aPer-Agent Config.squad/config.json → agentModelOverrides.{name}Persistent (survives sessions)
0bGlobal Config.squad/config.json → defaultModelPersistent (survives sessions)
1Session DirectiveUser said "use X" in current sessionSession-only
2Charter PreferenceAgent's charter.md → ## Model sectionPersistent (in charter)
3Task-Aware AutoCode → sonnet, docs → haiku, visual → opusComputed per-spawn
4Defaultclaude-haiku-4.5Hardcoded fallback

Key principle: Layer 0 (persistent config) beats everything. If the user said "always use opus" and it was saved to config.json, every agent gets opus regardless of role or task type. This is intentional — the user explicitly chose quality over cost.

AGENT WORKFLOW

On Session Start

  1. READ .squad/config.json
  2. CHECK for defaultModel field — if present, this is the Layer 0 override for all spawns
  3. CHECK for agentModelOverrides field — if present, these are per-agent Layer 0a overrides
  4. STORE both values in session context for the duration

On Every Agent Spawn

  1. CHECK Layer 0a: Is there an agentModelOverrides.{agentName} in config.json? → Use it.
  2. CHECK Layer 0b: Is there a defaultModel in config.json? → Use it.
  3. CHECK Layer 1: Did the user give a session directive? → Use it.
  4. CHECK Layer 2: Does the agent's charter have a ## Model section? → Use it.
  5. CHECK Layer 3: Determine task type:
    • Code (implementation, tests, refactoring, bug fixes) → claude-sonnet-4.6
    • Prompts, agent designs → claude-sonnet-4.6
    • Visual/design with image analysis → claude-opus-4.8
    • Non-code (docs, planning, triage, changelogs) → claude-haiku-4.5
  6. FALLBACK Layer 4: claude-haiku-4.5
  7. INCLUDE model in spawn acknowledgment: 🔧 {Name} ({resolved_model}) — {task}

When User Sets a Preference

Trigger phrases: "always use X", "use X for everything", "switch to X", "default to X"

  1. VALIDATE the model ID against the catalog (18+ models)
  2. WRITE defaultModel to .squad/config.json (merge, don't overwrite)
  3. ACKNOWLEDGE: ✅ Model preference saved: {model} — all future sessions will use this until changed.

Per-agent trigger: "use X for {agent}"

  1. VALIDATE model ID
  2. WRITE to agentModelOverrides.{agent} in .squad/config.json
  3. ACKNOWLEDGE: ✅ {Agent} will always use {model} — saved to config.

When User Clears a Preference

Trigger phrases: "switch back to automatic", "clear model preference", "use default models"

  1. REMOVE defaultModel from .squad/config.json
  2. ACKNOWLEDGE: ✅ Model preference cleared — returning to automatic selection.

STOP

After resolving the model and including it in the spawn template, this skill is done. Do NOT:

  • Generate model comparison reports
  • Run benchmarks or speed tests
  • Create new config files (only modify existing .squad/config.json)
  • Change the model after spawn (fallback chains handle runtime failures)

Config Schema

.squad/config.json model-related fields:

{
  "version": 1,
  "defaultModel": "claude-opus-4.6",
  "agentModelOverrides": {
    "fenster": "claude-sonnet-4.6",
    "mcmanus": "claude-haiku-4.5"
  }
}
  • defaultModel — applies to ALL agents unless overridden by agentModelOverrides
  • agentModelOverrides — per-agent overrides that take priority over defaultModel
  • Both fields are optional. When absent, Layers 1-4 apply normally.

Fallback Chains

If a model is unavailable (rate limit, plan restriction), retry within the same tier:

Premium:  claude-opus-4.8 → claude-opus-4.7 → claude-opus-4.6 → claude-sonnet-4.6 → (omit model param)
Standard: claude-sonnet-5 → claude-sonnet-4.6 → gpt-5.6-sol → gpt-5.6-terra → gpt-5.6-luna → gpt-5.4 → gpt-5.3-codex → claude-sonnet-4.5 → gemini-2.5-pro → (omit model param)
Fast:     claude-haiku-4.5 → gpt-5.4-mini → gpt-5-mini → (omit model param)

Never fall UP in tier. A fast task won't land on a premium model via fallback.

리뷰

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