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 ai-cost-optimizationgit 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/ai-cost-optimization)<a href="https://agentmods.dev/skills/komunite/kalfa/ai-cost-optimization"><img src="https://agentmods.dev/badge/skills/komunite/kalfa/ai-cost-optimization.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.01396 |
| Opus 5 | $0.00008 | $0.00698 |
| Sonnet 5 | $0.00003 | $0.00279 |
| Haiku 4.5 | $0.00002 | $0.00140 |
Grade A, and why
ai-cost-optimization 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 8d 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
80% 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.
Ai Cost Optimization
Amaç
Eyleme dönüştürülebilir, ölçülebilir sonuçlar sunan kapsamlı bir ai cost optimization oluşturun. 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 ai cost optimization 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
- ai cost optimization 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
- ai cost optimization 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.
- 8d ago First seen · 133 lines · 15 tokens per session scan A 39e190dc76db
ai-cost-optimization is a skill published in the GitHub repository komunite/kalfa (244 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 1,396 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to agent-evaluation, differing in 54 lines, and is treated as a copy.
Other skills, from other repositories
atlas-obscura
Search Atlas Obscura for weird, wonderful, and hidden gem places near any destination. Find the interesting stuff, not boring plaques. Search by coordinates, get full details with descriptions and images.
cf-validate
A validator for 1C configuration source files. It checks the XML structure and the files and objects that make up a 1C business-application configuration.
agent-code-generator
Generates Agent definitions (.md files) based on user intent and standard templates.
content-creation
Comprehensive guide to strategic content creation covering content strategy, writing frameworks, optimization techniques, distribution channels, performance measurement, and AI-assisted content workflows. Designed for marketers and content professionals building audience-focused content programs.
checking-freshness
Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.
changelog-composer
Generates structured changelogs and release notes from git history and PRs, classifying breaking changes, features, fixes, performance, docs. Triggers on: "generate changelog", "write release notes", "what changed since", "prepare release", "release notes for", "diff since tag".