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 komunite/kalfa --skill llm-comparisongit clone --depth 1 https://github.com/komunite/kalfaWrote 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/komunite/kalfa/llm-comparison)<a href="https://agentmods.dev/skills/komunite/kalfa/llm-comparison"><img src="https://agentmods.dev/badge/skills/komunite/kalfa/llm-comparison/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/komunite/kalfa/llm-comparison"><img src="https://agentmods.dev/badge/skills/komunite/kalfa/llm-comparison.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 | $0.00017 | $0.01401 |
| Opus 5 | $0.00009 | $0.00700 |
| Sonnet 5 | $0.00003 | $0.00280 |
| Haiku 4.5 | $0.00002 | $0.00140 |
Grade A, and why
llm-comparison 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 7d 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
77% identical to agent-evaluation — 54 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Llm Comparison
Amaç
Eyleme dönüştürülebilir, ölçülebilir sonuçlar sunan kapsamlı bir llm comparison analiz edin ve üretin. Bu beceri, her seferinde profesyonel düzeyde çıktı sağlayan, kalite doğrulamalı yapılandırılmış bir süreç sunar.
Kategori: Yapay Zeka ve Otomasyon
Girdiler
Zorunlu
- Hedef: Bu çıktı ile neyi başarmak istiyorsunuz
- Bağlam: İlgili arka plan bilgileri
İsteğe Bağlı
- Kısıtlamalar: Dikkate alınması gereken sınırlamalar veya gereksinimler
- Mevcut Çalışma: Üzerine inşa edilecek önceki belgeler veya veriler
Sistem Bağlamı
Başlamadan önce:
- Mevcut proje bağlamı ve öncelikleri için
memory.md'yi oku - İlgili öğrenilmiş kurallar veya kısıtlamalar için
knowledge-base.md'yi kontrol et - Projedeki mevcut ilgili belgeleri gözden geçir
- Bu çıktı ile ilgili
.claude/workspace/TaskBoard.md'deki aktif görevleri not et
Süreç
Adım 1: Bağlam ve Araştırma
- Projedeki mevcut llm comparison belgelerini gözden geçir
- İlgili öğrenilmiş kurallar veya kısıtlamalar için
knowledge-base.md'yi kontrol et - Mevcut proje bağlamı ve öncelikleri için
memory.md'yi kontrol et - Kilit paydaşları ve gereksinimlerini belirle
- En uygun çerçeveyi seç: AI Readiness Assessment, Automation ROI Calculator, Human-in-the-Loop Design
Adım 2: Analiz ve Çerçeve Uygulaması
- llm comparison yapılandırmak için seçilen çerçeveyi uygula
- Boşlukları, fırsatları ve riskleri belirle
- Başarı metriklerini tanımla: Time Saved Per Task, Automation Rate, Error Reduction %, Cost Per AI Operation
- Varsayımları ve bağımlılıkları belgele
- Yaklaşımı sektör en iyi uygulamalarına göre doğrula
Adım 3: Çıktıyı Oluştur
- llm comparison aşağıdaki çıktı formatını kullanarak yapılandır
- Genel tavsiyeler değil, spesifik ve eyleme dönüştürülebilir öneriler ekle
- Uygulanabilir yerlerde somut rakamlar, zaman çizelgeleri ve kıyaslamalar ekle
- Tutarlılık için mevcut proje belgeleriyle çapraz referans yap
- Her bölümün değer kattığından emin ol — dolgu içeriği çıkar
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.
- 7d ago First seen · 133 lines · 17 tokens per session scan A 4dfbd76db6ee
llm-comparison is a skill published in the GitHub repository komunite/kalfa (244 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 1,401 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to agent-evaluation, differing in 54 lines, and is treated as a copy.
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