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Agent skill

Model Selection

by bradygaster in bradygaster/squad

Determines which LLM model to use for each agent spawn. An agent skill from bradygaster/squad.

MITAuto-check passed

Install Model Selection

skills CLI
$ npx skills add bradygaster/squad --skill model-selection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install bradygaster/squad model-selection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/bradygaster/squad.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.squad-templates/skills/model-selection .claude/skills/model-selection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
model-selection
GitHub stars
3.3k
Token cost
~1.3k tokens
SKILL.md length
560 words
Files
1
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Determines which LLM model to use for each agent spawn. An agent skill from bradygaster/squad.

  • Works in 4 steps: READ .squad/config.json → CHECK for defaultModel field — if… → CHECK for agentModelOverrides field — if… → …
  • Each agent spawn
  • SKILL.md covers SCOPE, Context, 5-Layer Model Resolution… and AGENT WORKFLOW, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Model Selection is an agent skill from bradygaster/squad. Determines which LLM model to use for each agent spawn

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Squad: AI agent teams for any project. The licence is MIT.

When your agent uses it

  • Each agent spawn

Example prompts

  • “Use the model-selection skill to determine which LLM model to use for each agent spawn. An agent skill from bradygaster/squad”
  • “/model-selection”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit d2364df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Model Selection loads about 1.3k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 560 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~18
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from bradygaster/squad at commit d2364df, republished under its MIT licence (© bradygaster). 560 words, ~1,263 tokens.

Download SKILL.mdSave it as .claude/skills/model-selection/SKILL.md (or your agent's skills folder).
name
model-selection
description
Determines which LLM model to use for each agent spawn
domain
orchestration
confidence
medium
source
extracted

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 a curated model catalog 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/prompts → Terra, docs → Luna, visual → SolComputed per-spawn
4Defaultgpt-5.6-lunaHardcoded 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) → gpt-5.6-terra
  • Prompts, agent designs → gpt-5.6-terra
  • Visual/design with image analysis → gpt-5.6-sol
  • Non-code (docs, planning, triage, changelogs) → gpt-5.6-luna
  1. FALLBACK Layer 4: gpt-5.6-luna
  2. INCLUDE model in spawn acknowledgment: 🔧 {Name} ({resolved_model}) — {task}
Show full SKILL.md (216 more words)Show less
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
  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:

json
{
  "version": 1,
  "defaultModel": "claude-opus-4.6",
  "agentModelOverrides": {
    "agent-alpha": "claude-sonnet-4.6",
    "agent-beta": "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:  gpt-5.6-sol → claude-opus-5 → claude-opus-4.8 → claude-opus-4.7 → claude-opus-4.6 → claude-sonnet-4.6 → (omit model param)
Standard: gpt-5.6-terra → claude-sonnet-5 → claude-sonnet-4.6 → gpt-5.5 → gpt-5.4 → gpt-5.3-codex → claude-sonnet-4.5 → gemini-3.1-pro → (omit model param)
Fast:     gpt-5.6-luna → 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.

© bradygaster, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .squad-templates/skills/model-selection of bradygaster/squad.

Open the folder on GitHubat commit d2364df

Compare with similar skills

Model Selection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Model Selection compared with similar skills
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Model Selection this skillbradygaster/squad3.3k—~1.3kAutomated safety check: PassMIT
Spawnalirezarezvani/claude-skills28k—~829Automated safety check: PassMIT
Spawnaiskillstore/marketplace4301 repos~667Automated safety check: PassNone
Orchestrator Container Spawnsamugit83/redamon2.9k—~1.7kAutomated safety check: PassMIT
Agentica Spawnparcadei/Continuous-Claude-v33.9k2 repos~512Automated safety check: PassMIT
Spawnkoolamusic/claudefiles130—~1.4kAutomated safety check: PassMIT

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Questions about Model Selection

What does Model Selection do?

Determines which LLM model to use for each agent spawn. An agent skill from bradygaster/squad. Model Selection is an agent skill from bradygaster/squad.

When should I use Model Selection?

Model Selection fits situations like: each agent spawn.

How do I install Model Selection in Claude Code?

Run `npx skills add bradygaster/squad --skill model-selection -a claude-code`. Or copy the skill folder (.squad-templates/skills/model-selection in bradygaster/squad) into .claude/skills/model-selection in your project. Claude Code loads it when a task matches its description.

How do I install Model Selection in Codex?

Run `npx skills add bradygaster/squad --skill model-selection -a codex`. Or copy the skill folder (.squad-templates/skills/model-selection in bradygaster/squad) into .agents/skills/model-selection in your project. Codex loads it when a task matches its description.

Can I use Model Selection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add bradygaster/squad --skill model-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-selection, .gemini/skills/model-selection, .github/skills/model-selection and .opencode/skills/model-selection in your project.

What does Model Selection need to run?

SKILL.md names no scripts, command-line tools or credentials: Model Selection is instructions for the agent only.

Does Model Selection access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Model Selection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Model Selection use?

Model Selection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Selection use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Model Selection?

Skills that share tags, products or a category with Model Selection: Spawn (alirezarezvani/claude-skills, 28k stars), Spawn (aiskillstore/marketplace, 430 stars), Orchestrator Container Spawn (samugit83/redamon, 2.9k stars) and Agentica Spawn (parcadei/Continuous-Claude-v3, 3.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Selection?

bradygaster (a GitHub user) maintains it in bradygaster/squad, which has 3,256 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 6, 2026.

Source: bradygaster/squad on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.