Model Selection

Every custom agent, subagent, or squad node needs an explicit model choice — never leave it unset on the assumption that an inherited default is adequate for a specialist role. The right model balances five criteria, then gets mapped onto whatever field syntax the target harness actually accepts.

Anatomy

Criterion	Question it answers
Capability tier	How complex is the reasoning this agent's task requires?
Speed	How latency-sensitive is whoever is waiting on this agent?
Cost	What's the budget, and is this agent bounded by a caller's own cost ceiling?
Context & modality	Does the task need a large context window, or image/visual input?
Effort	Can the same model be tuned cheaper or faster instead of switching models entirely?

Workflow

1. Establish task criteria for this specific agent — capability need, latency sensitivity, cost budget, context/multimodal needs → references/selection-criteria.md
2. Pick a starting strategy: efficiency-first (start cheap/fast, upgrade only if evals show a gap) or capability-first (start at the top, optimize down later) → references/selection-criteria.md
3. Prefer tuning an effort/reasoning-effort parameter over switching models when the harness and model support it — it trades intelligence for latency/cost within the same model family, a cheaper lever than a full model swap → references/selection-criteria.md
4. If this agent is one node of a multi-agent squad, or needs independent/adversarial review, apply call-cadence vs. provider prompt-cache TTL, cost-tier ranking/ceilings, and cross-provider diversity → references/cache-and-cost.md
5. Ask which harness targets this agent (if not already established), then apply that harness's exact model field name, value shape, and resolution order → references/harness-mapping.md
6. Verify the chosen model name is still current against the provider's live docs before finalizing — model lineups rotate every few months; never trust a memorized or previously-seen model name as still accurate → references/selection-criteria.md
7. Run markdown-best-practices over any new or edited Markdown produced while documenting the choice (design notes, agent files' surrounding docs)

Reference Files

File	Load When
references/selection-criteria.md	Picking a capability tier for a task, choosing an efficiency-first vs. capability-first starting point, tuning an effort/reasoning-effort parameter, deciding whether to upgrade/downgrade after benchmarking, or verifying a model name is still current
references/cache-and-cost.md	An agent's call cadence vs. its provider's prompt-cache TTL, ranking candidate models by cost tier, applying a cost ceiling inherited from a caller, or picking genuinely independent models/providers for adversarial or second-opinion review
references/harness-mapping.md	Writing the actual model:-equivalent field for GitHub Copilot CLI, Claude Code, or VS Code custom agents, understanding each harness's resolution order and fallback-list support, or extending this skill's guidance to a harness not yet covered