bradygaster/squad is a tool that creates a human-directed team of AI development agents inside a project repository through GitHub Copilot. Developers use it to delegate work among persistent specialists such as frontend, backend, testing, and lead agents while retaining responsibility for decisions and review. The catalogue entries are the skills, agents, and instructions that define and coordinate those team members.
Borrowing it
Nothing to install: this file belongs to bradygaster/squad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bradygaster/squad/dev/.copilot/skills/model-selection/SKILL.mdgit clone --depth 1 https://github.com/bradygaster/squadWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bradygaster/squad/model-selection)<a href="https://agentmods.dev/skills/bradygaster/squad/model-selection"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/model-selection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bradygaster/squad/model-selection"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/model-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 130 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.02188 |
| Opus 5.5 | $0.00006 | $0.00875 |
| Sonnet 5.5 | $0.00003 | $0.00438 |
| Haiku 4.5 | $0.00002 | $0.00219 |
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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Before spawning an agent, the coordinator determines which model to use. This skill codifies the 4-layer hierarchy, role-to-model mappings, task complexity adjustments, and fallback chains. Applies to all agent spawns in Team Mode.
Patterns
4-Layer Hierarchy
Check these layers in order — first match wins:
Layer 1 — User Override: Did the user specify a model? ("use opus", "save costs", "use gpt-5.3-codex for this"). If yes, use that model. Session-wide directives ("always use haiku") persist until contradicted.
Layer 2 — Charter Preference: Does the agent's charter have a ## Model section with Preferred set to a specific model (not auto)? If yes, use that model.
Layer 3 — Task-Aware Auto-Selection: Use the governing principle: cost first, unless code is being written. Match the agent's task to determine output type, then select accordingly:
| Task Output | Model | Tier | Rule |
|---|---|---|---|
| Writing code (implementation, refactoring, test code, bug fixes) | gpt-5.6-terra |
Standard | Quality and accuracy matter for code. Use standard tier. |
| Writing prompts or agent designs (structured text that functions like code) | gpt-5.6-terra |
Standard | Prompts are executable — treat like code. |
| NOT writing code (docs, planning, triage, logs, changelogs, mechanical ops) | gpt-5.6-luna |
Fast | Cost first. Luna handles non-code tasks by default. |
| Visual/design work requiring image analysis | gpt-5.6-sol |
Premium | Vision capability required. Overrides cost rule. |
Role-to-model mapping (applying cost-first principle):
| Role | Default Model | Why | Override When |
|---|---|---|---|
| Core Dev / Backend / Frontend | gpt-5.6-terra |
Writes code — quality first | Heavy code gen → gpt-5.3-codex |
| Tester / QA | gpt-5.6-terra |
Writes test code — quality first | Simple test scaffolding → claude-haiku-4.5 |
| Lead / Architect | auto (per-task) | Mixed: code review needs quality, planning needs cost | Architecture proposals → premium; triage/planning → luna |
| Prompt Engineer | auto (per-task) | Mixed: prompt design is like code, research is not | Prompt architecture → gpt-5.6-terra; research/analysis → haiku |
| Copilot SDK Expert | gpt-5.6-terra |
Technical analysis that often touches code | Pure research → claude-haiku-4.5 |
| Designer / Visual | gpt-5.6-sol |
Vision-capable model required | — (never downgrade — vision is non-negotiable) |
| DevRel / Writer | gpt-5.6-luna |
Docs and writing — not code | — |
| Scribe / Logger | gpt-5.6-luna |
Mechanical file ops — cheapest possible | — (never bump Scribe) |
| Git / Release | gpt-5.6-luna |
Mechanical ops — changelogs, tags, version bumps | — (never bump mechanical ops) |
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.
- 1mo ago Changed 7edbafbf74ae
- 1mo ago First seen · 144 lines · 15 tokens per session scan A a52cc96f0553
model-selection is a skill published in the GitHub repository bradygaster/squad (3,300 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 2,188 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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