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 avizmarlon/agent-skills --skill llm-model-selectiongit clone --depth 1 https://github.com/avizmarlon/agent-skillsWrote 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/avizmarlon/agent-skills/llm-model-selection)<a href="https://agentmods.dev/skills/avizmarlon/agent-skills/llm-model-selection"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/llm-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/avizmarlon/agent-skills/llm-model-selection"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/llm-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 | $0.00051 | $0.01206 |
| Opus 5.5 | $0.00020 | $0.00482 |
| Sonnet 5.5 | $0.00010 | $0.00241 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
llm-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 2mo 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Model Selection — Consult Benchmarks Before Choosing
When a task requires you to recommend or select an LLM for a specific use case, follow a systematic process before defaulting to a well-known model name:
1. Check Existing Benchmarks First
If you or your team have already run benchmarks (bake-offs) for similar use cases, that prior data is more valuable than generic leaderboards. Reasons:
- Context-specific results trump averages. A model that ranks #2 on MMLU may rank #1 on your specific task (code generation, legal analysis, math reasoning).
- Real-world performance differs from benchmark suites. Generic leaderboards test broad knowledge; your task has specific constraints (latency budget, cost per call, long-context handling, instruction-following precision).
- You avoid redundant work. If you've already benchmarked "agent reasoning on financial data," reuse that result instead of re-running it on a similar financial-domain task.
If an existing benchmark covers your use case: use the winner as your default choice. Note the selection criteria so you can explain the decision.
2. When to Re-Benchmark
If your use case diverges from existing benchmarks, narrow the test scope and run a fresh benchmark:
- Scope mismatch: existing benchmark was for "summarization" but your task is "multi-step reasoning with chain-of-thought"
- Different constraints: existing test was on 4K-token documents; you need 100K-token handling
- New model release: new frontier models are available since the last benchmark
- Cost/latency window changed: you now have stricter latency budget or cost ceiling
When re-benchmarking, use existing benchmarks as prior signals — especially for known weaknesses (e.g., "GPT models historically fail at precise JSON output; Claude handles it better"). This lets you design a test that's smaller and faster than the original.
3. Always Validate Availability & Pricing Live
Never trust model names or pricing from memory or old docs. Models are deprecated, renamed, and repriced frequently. Always query the live API or vendor's current listing:
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.
- 2mo ago First seen · 73 lines · 51 tokens per session scan A 656ac45d78c4
llm-model-selection is a skill published in the GitHub repository avizmarlon/agent-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 1,206 once invoked, about $0.0002 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 2026-08-31.
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