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 agentmods add skills/alonf/mcppythondemo/model-selectionnpx skills add alonf/MCPPythonDemo --skill model-selectiongit clone --depth 1 https://github.com/alonf/MCPPythonDemoWhat 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 | $0.00000 | $0.01304 |
| Opus 5 | $0.00000 | $0.00652 |
| Sonnet 5 | $0.00000 | $0.00261 |
| 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 yesterday.
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 — 242 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
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.
- yesterday First seen · 118 lines · 0 tokens per session scan A 17ac95eb0e5d
model-selection is a skill published in the GitHub repository alonf/MCPPythonDemo (0 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,304 tokens. A static security scan graded it A with 0 findings. It is 95% identical to model-selection, differing in 242 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…