Agent model selection

Skill AllanWessels/Bratan/docs/build-skills/agent-model-selection

Bratan is a self-improving Retrieval-Augmented Generation framework built on an adversarial three-agent loop

Install

npx -y skills add AllanWessels/Bratan --skill agent-model-selection

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What its author says it does

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Pass model explicitly when dispatching sub-agents. Parent-default inheritance bypasses cost considerations; reserve stronger models for tasks that genuinely need them.

SKILL.md

2.9 KB, as published. Nobody here has run it

Agent Model Selection

When to use

Any time you dispatch a sub-agent via the Agent tool.
When the task mix includes both high-reasoning (architecture review, complex debugging) and low-reasoning (run a test suite, apply a grep, format a file) work.

When NOT to use

You are the sub-agent (model selection is for the dispatcher).

How to apply

Pass model: explicitly in every Agent tool call. Do not rely on inheritance from the parent session:

model: "sonnet"    # fast, cheap — use for: test runs, file reads, curl checks,
                   # mechanical code changes, grep-and-report tasks

model: "opus"      # slow, expensive — use for: architecture review, deep
                   # reasoning over a large codebase, novel algorithm design,
                   # judge agent (when correctness is load-bearing)

Decision table

Task type	Model
Run pytest / vitest / Playwright and report results	sonnet
Apply a targeted code fix from a spec	sonnet
Execute a pre-handoff checklist (curl + ls)	sonnet
Audit a test suite for structural gaps	sonnet
Design a new subsystem architecture	opus
Deep code review over 10+ files	opus
Judge / evaluator agent (correctness is load-bearing)	opus or sonnet pinned
Multi-file refactor from a spec	sonnet

The judge exception

If the model is acting as a stable evaluator / judge whose output is trusted as ground truth (e.g. a RAG judge agent), do not downgrade it mid-run for cost reasons. Inconsistent judge model = inconsistent scores = the optimization loop optimizes noise. Pin the judge model in config and enforce it.

Why this works

Sub-agents inherit the parent session's default model unless overridden. In a session where the parent is using a strong model for architecture work, every dispatched sub-agent (including "just run ls and report back") also runs on that model. At scale — dozens of parallel verification agents — this cost multiplies significantly. Explicit selection ensures each agent uses the minimum capable model for its task.

Anti-patterns to avoid

Omitting model: and assuming inheritance is intentional — it is accidental. Always be explicit.
Downgrading a judge or evaluator agent to save cost — the judge's reliability is a system invariant, not an optimization target.
Using a strong model for a mechanical task "just to be safe" — this is false safety. A test runner either passes or fails; a stronger model does not change the test results.

Cross-links

[[parallel-fanout-verification]] — where most sub-agent dispatches happen; all test-runner agents should use sonnet