model-selection

A set of rules for choosing which language model to use when Squad starts an agent task.

In plain words
What is it for?
Use it to set model choices per agent, for the whole project, for one session, or in an agent’s charter.
Why use it?
It resolves conflicting model preferences in a fixed order, so each agent task receives a predictable model choice.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/webmaxru/is-ai-native/model-selection
Any agent
npx skills add webmaxru/is-ai-native --skill model-selection
Clone the repo
git clone --depth 1 https://github.com/webmaxru/is-ai-native

Made for: Claude Code, Codex.

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,300 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01300
Opus 5 $0.00000 $0.00650
Sonnet 5 $0.00000 $0.00260
Haiku 4.5 $0.00000 $0.00130

Measured 2d ago against content hash c2fa36d3343f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

model-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

95% identical to model-selection — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.copilot/skills/model-selection/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Layer Name Source Persistence
0a Per-Agent Config .squad/config.jsonagentModelOverrides.{name} Persistent (survives sessions)
0b Global Config .squad/config.jsondefaultModel Persistent (survives sessions)
1 Session Directive User said "use X" in current session Session-only
2 Charter Preference Agent's charter.md## Model section Persistent (in charter)
3 Task-Aware Auto Code → sonnet, docs → haiku, visual → opus Computed per-spawn
4 Default claude-haiku-4.5 Hardcoded 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-4.6
    • 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}

Read the full file on GitHub · 118 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 118 lines · 0 tokens per session scan A c2fa36d3343f

Subscribe to this mod's changes

model-selection is a skill published in the GitHub repository webmaxru/is-ai-native (5 stars, last pushed 10d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,300 tokens. A static security scan graded it A with 0 findings. It is 95% identical to model-selection, differing in 8 lines, and is treated as a copy.