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 prompt-engineering-guidegit 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/prompt-engineering-guide)<a href="https://agentmods.dev/skills/komunite/kalfa/prompt-engineering-guide"><img src="https://agentmods.dev/badge/skills/komunite/kalfa/prompt-engineering-guide/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/prompt-engineering-guide"><img src="https://agentmods.dev/badge/skills/komunite/kalfa/prompt-engineering-guide.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 | $0.00023 | $0.02755 |
| Opus 5.5 | $0.00009 | $0.01102 |
| Sonnet 5.5 | $0.00005 | $0.00551 |
| Haiku 4.5 | $0.00002 | $0.00276 |
Grade A, and why
prompt-engineering-guide 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.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Mühendisliği — Solopreneur Rehberi
Amaç
AI'dan profesyonel kalitede çıktı almak için pratik teknikler. Akademik teori yok — kopyala-yapıştır yapabileceğin şablonlar ve hemen uygulayabileceğin kurallar. Solopreneur olarak AI, senin ilk çalışanın. Ama ona iyi brief vermezsen kötü iş çıkarır.
Kategori: Yapay Zeka ve Otomasyon Ön koşul: Claude veya ChatGPT hesabı Seviye: Başlangıç → Orta
Temel Çerçeve: RACE
Her prompt'u bu 4 katmanlı yapıyla kur:
| Katman | Açıklama | Örnek |
|---|---|---|
| Role (Rol) | AI'ya kim olduğunu söyle | "Sen 10 yıllık deneyimli bir SaaS copywriter'ısın" |
| Action (Eylem) | Ne yapmasını istediğini söyle | "Bir landing page headline'ı yaz" |
| Context (Bağlam) | İşin arka planını ver | "Ürünüm freelancerlar için zaman takip aracı, aylık ₺99" |
| Example (Örnek) | Beklediğin çıktıyı göster | "Şu tarz olsun: 'Stop trading time for money'" |
Neden çalışır? AI, bağlam ne kadar spesifikse o kadar iyi çıktı verir. "Bir metin yaz" dersen ortalama bir şey alırsın. RACE ile yönlendirirsen profesyonel çıktı alırsın.
5 Temel Prompt Kalıbı
1. İçerik Üretimi (Blog, Sosyal Medya, E-posta)
Rol: Sen [niş alanı] konusunda uzman bir içerik stratejistsin.
Hedef kitle: [kitle tanımı — yaş, meslek, acı noktası].
Görev: [içerik türü] yaz.
Ton: [konuşkan / profesyonel / eğitici / ikna edici].
Uzunluk: [kelime sayısı veya paragraf sayısı].
Format: [madde listesi / hikaye / listicle / how-to].
Şu yapıyı kullan:
- Dikkat çekici açılış (ilk cümle kancası)
- Ana mesaj (3 alt başlıkla)
- Kapanış + call-to-action
Örnek ton: "[buraya beğendiğin bir içerikten 2-3 cümle yapıştır]"
2. Araştırma ve Analiz (Rakip, Pazar)
Rol: Sen [sektör] alanında deneyimli bir iş analisti sin.
Görev: [rakip/pazar/trend] analizi yap.
Kapsam: [coğrafya, zaman dilimi, segment].
Şu çerçeveyi kullan:
1. Mevcut durum özeti (5 madde)
2. Güçlü ve zayıf yönler tablosu
3. Fırsatlar (3 tane, her biri potansiyel gelir tahminiyle)
4. Tehditler ve riskler
5. Benim için 3 aksiyon önerisi (öncelik sırasıyla)
Kaynak olarak bilinen verileri kullan. Emin olmadığın yerleri "[doğrulanmalı]" olarak işaretle.
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 · 221 lines · 23 tokens per session scan A fbe44af0442f
prompt-engineering-guide is a skill published in the GitHub repository komunite/kalfa (247 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 2,755 once invoked, about $0.0001 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 2026-09-03.
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