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 fthsrbst/mnema --skill local-modelgit clone --depth 1 https://github.com/fthsrbst/mnemaWrote 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/fthsrbst/mnema/local-model)<a href="https://agentmods.dev/skills/fthsrbst/mnema/local-model"><img src="https://agentmods.dev/badge/skills/fthsrbst/mnema/local-model/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/fthsrbst/mnema/local-model"><img src="https://agentmods.dev/badge/skills/fthsrbst/mnema/local-model.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 | $0.00060 | $0.00685 |
| Opus 5.5 | $0.00024 | $0.00274 |
| Sonnet 5.5 | $0.00012 | $0.00137 |
| Haiku 4.5 | $0.00006 | $0.00068 |
Grade A, and why
local-model 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.
What it actually says
Model Yönlendirme: Zorluğa Göre Karar
Temel kural: işin zorluğuna göre karar ver. Üç seviye:
- Basit/toplu/mekanik →
local_llm(LM Studio veya Ollama, ücretsiz, veri lokalde) - Orta ve zor → sen (mevcut agent) yaparsın — sen zaten güçlü bir bulut modelisin; ekstra API key veya entegrasyon gerekmez. Fatih'in kullandığı agent (Claude Code, opencode go aboneliği vb.) hangisiyse zor işin modeli odur.
- Görsel/video/medya →
media_generate(ComfyUI, [[media-gen]] skill'i)
Hub'a ayrıca bulut LLM API'si bağlamıyoruz — bilinçli karar: agent'ın kendisi o katman.
local_llm'e uygun işler
- Toplu/tekrarlı işler: 50 dosyaya özet, commit mesajı üretimi, veri temizleme/dönüştürme, etiketleme
- Taslaklar: ilk sürüm docstring, test iskeleti, çeviri taslağı (sonra sen rafine et)
- Hassas veri: kullanıcının dışarı çıkmasını istemediği içerik üzerinde işlem
- Kod tamamlama tarzı mekanik üretim: boilerplate, regex, SQL taslağı
Sende kalması gereken işler (local_llm'e YOLLAMA)
- Mimari kararlar, kod review, zor debug — yargı gerektiren her şey
- Kullanıcıyla diyalog ve nihai çıktı kalitesinin önemli olduğu metinler
- Hub hafızasına yazılacak kayıtların son hali (kalite senin sorumluluğun)
Kullanım
machine_status→ hangi makinede LM Studio/Ollama açık, hangi modeller var.- Model seçimi (isimden çıkarım):
*coder*→ kod işleri- küçük model (7-20B) → hızlı basit işler; büyük (30B+) → kalite gereken taslaklar
- model belirtmezsen yüklü ilk model kullanılır — toplu işte önce 1 örnekle dene, çıktı iyiyse devam et.
local_llm(prompt, model?, backend?, system?, max_tokens?)— backend boşsa LM Studio öncelikli,backend: "ollama"ile Ollama'ya yönlendir. Uzun işlerde çıktıyı doğrula: yerel modeller talimat kaçırabilir; kritik alanları örneklemle kontrol et.
Kural
Sonucu kendi çıktın gibi sunma — yerel model çıktısını kullandıysan doğrulamandan geçir. Hata bulursan kendin düzelt, kullanıcıya ham yerel model çıktısı gösterme.
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 · 37 lines · 60 tokens per session scan A f2f9eef276af
local-model is a skill published in the GitHub repository fthsrbst/mnema (2 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 685 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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