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.
npx skills add Azure-Samples/eShopLite --skill model-selectiongit clone --depth 1 https://github.com/Azure-Samples/eShopLiteWrote 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/azure-samples/eshoplite/model-selection)<a href="https://agentmods.dev/skills/azure-samples/eshoplite/model-selection"><img src="https://agentmods.dev/badge/skills/azure-samples/eshoplite/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/azure-samples/eshoplite/model-selection"><img src="https://agentmods.dev/badge/skills/azure-samples/eshoplite/model-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.01300 |
| Opus 5.5 | $0.00000 | $0.00520 |
| Sonnet 5.5 | $0.00000 | $0.00260 |
| Haiku 4.5 | $0.00000 | $0.00130 |
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.
This is a copy
95% identical to model-selection — 16 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.
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
modelparameter for everytasktool 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.json → agentModelOverrides.{name} |
Persistent (survives sessions) |
| 0b | Global Config | .squad/config.json → defaultModel |
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
- READ
.squad/config.json - CHECK for
defaultModelfield — if present, this is the Layer 0 override for all spawns - CHECK for
agentModelOverridesfield — if present, these are per-agent Layer 0a overrides - STORE both values in session context for the duration
On Every Agent Spawn
- CHECK Layer 0a: Is there an
agentModelOverrides.{agentName}in config.json? → Use it. - CHECK Layer 0b: Is there a
defaultModelin config.json? → Use it. - CHECK Layer 1: Did the user give a session directive? → Use it.
- CHECK Layer 2: Does the agent's charter have a
## Modelsection? → Use it. - 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
- Code (implementation, tests, refactoring, bug fixes) →
- FALLBACK Layer 4:
claude-haiku-4.5 - INCLUDE model in spawn acknowledgment:
🔧 {Name} ({resolved_model}) — {task}
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 First seen · 118 lines · 0 tokens per session scan A c2fa36d3343f
model-selection is a skill published in the GitHub repository Azure-Samples/eShopLite (168 stars, last pushed 5d 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 16 lines, and is treated as a copy.
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