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.
git clone --depth 1 https://github.com/boranesn/agentic-baseWrote 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/agents/boranesn/agentic-base/llm-integration-agent)<a href="https://agentmods.dev/agents/boranesn/agentic-base/llm-integration-agent"><img src="https://agentmods.dev/badge/agents/boranesn/agentic-base/llm-integration-agent/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/agents/boranesn/agentic-base/llm-integration-agent"><img src="https://agentmods.dev/badge/agents/boranesn/agentic-base/llm-integration-agent.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 | $0.00047 | $0.00488 |
| Opus 5.5 | $0.00019 | $0.00195 |
| Sonnet 5.5 | $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 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
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.
- 1mo 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 (3 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 488 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.
Other agents, from other repositories
prompt-architect
Designs new prompts from blank using CoT / ToT / ReAct / Constitutional / Self-Consistency / Atom-of-Thoughts patterns. Composes lab specialist for final pass. Auto-invokes when @prompt-conductor dispatches flow-design / flow-knowledge-base, or when Frank says "design a prompt for X", "build me a system prompt for Y"…
prompt-claude-specialist
Anthropic / Claude prompting doctrine specialist. Owns XML-tag structure, prefill technique, extended-thinking signature integrity, system-as-role placement, constitution-style virtue prompting, and Console Prompt Improver 4-step (example identification → XML draft → CoT refinement → example enhancement). Auto-invokes…
prompt-conductor
Top-level Opus composer for the Prompt Hub. Routes every prompt-engineering ask to the right 2-5 specialists from the 12-agent team. Auto-invokes when Frank says "design a prompt for X", "optimize this prompt", "evaluate my system prompt", "harvest prompts from Fabric", "build a knowledge-base prompt set", "IFS…
prompt-evaluator
Wraps promptfoo (MIT) to evaluate prompts. Generates declarative test files colocated with each pattern. Returns scored verdicts and writes them back to pattern frontmatter. Replaces the existing prompt-tester.md scaffold (which had zero fixtures). Auto-invokes when @prompt-conductor dispatches flow-evaluate or as the…
prompt-gpt-specialist
OpenAI / GPT-5 prompting doctrine specialist. Owns developer-role placement, reasoningeffort × verbosity independent axes, Structured Outputs via Zod/Pydantic (never JSON-in-prose), tool-call preambles, contradiction audit, persistence reminders, previousresponseid chaining. Auto-invokes when @prompt-conductor…
prompt-harvester
Bulk-imports patterns from external sources (Fabric MIT, awesome-chatgpt-prompts CC0, awesome-claude-prompts MIT, DAIR.AI taxonomy MIT, lab official docs). Tags provenance + license. Runs quality gate. Returns patterns ready for red-team → librarian publish. Repurposes prior prompt-harvester.md legacy scaffold.…