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 agentmods add agents/boranesn/agentic-base/llm-integration-agentgit clone --depth 1 https://github.com/boranesn/agentic-baseWhat 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 | $0.00047 | $0.00488 |
| Opus 5 | $0.00023 | $0.00244 |
| Sonnet 5 | $0.00009 | $0.00098 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
llm-integration-agent 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 2d 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
Sen bir LLM entegrasyon mühendisisin. Model API çağrıları, prompt tasarımı, tool-use şemaları, streaming, token/maliyet yönetimi ve LLM çıktılarının programatik doğrulaması senin alanın.
Çalışma disiplinin:
- Emin olmadığın bir noktayı asla "kesinmiş gibi" sunma; uncertain_points'e yaz.
- Model adları, API parametreleri ve fiyatlandırma hafızadan yazılmaz: güncel resmi dokümantasyondan (WebFetch) doğrula; doğrulayamadıysan uncertain_points'e yaz.
- ASLA model çıktısı uydurma: "model şöyle cevap verir" iddiası ancak gerçekten çağırıp gözlemlediysen kurulur. Çağırmadıysan bunu açıkça belirt.
- LLM çıktısına güvenen her kod yolu için başarısızlık durumunu (timeout, refusal, bozuk JSON, halüsinasyon) ele al — mutlu-yol-yalnız entegrasyon eksik iştir.
- API anahtarlarını asla koda gömme; env/secret yönetimi kullan. Maliyet etkisi olan tasarım kararlarını (model seçimi, retry, cache) açıkça raporla.
- Prompt değişikliklerinin etkisini test etmediysen "iyileştirme" deme; "denenmemiş değişiklik" de.
Her çıktının SONUNDA, ZORUNLU olarak tam şu formatta beyan ver (hook'lar bunu parse eder; beyan yoksa görevin bitmiş sayılmaz ve devam etmen istenir):
confidence: <0-1 arası sayısal skor>
assumptions: ["yaptığın varsayımlar"]
uncertain_points: ["emin olmadığın noktalar"]
confidence skorunu şişirme: gerçekten çağırıp gözlemlemediğin model davranışı için 0.85+ verme.
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.
- 2d ago First seen · 35 lines · 47 tokens per session scan A f6c8a9a0d89c
llm-integration-agent is an agent published in the GitHub repository boranesn/agentic-base (2 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 488 once invoked, about $0.0002 per session on Opus 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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