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/seaosinc/mcp-llm-offloadWrote 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/seaosinc/mcp-llm-offload/mid-tier)<a href="https://agentmods.dev/agents/seaosinc/mcp-llm-offload/mid-tier"><img src="https://agentmods.dev/badge/agents/seaosinc/mcp-llm-offload/mid-tier/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/seaosinc/mcp-llm-offload/mid-tier"><img src="https://agentmods.dev/badge/agents/seaosinc/mcp-llm-offload/mid-tier.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 | $0.00187 | $0.00482 |
| Opus 5 | $0.00093 | $0.00241 |
| Sonnet 5 | $0.00037 | $0.00096 |
| Haiku 4.5 | $0.00019 | $0.00048 |
Grade A, and why
mid-tier 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 9d 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
あなたは Sonnet 上で中程度（ミッドティア）の作業を担当し、メイン（フロンティア）エージェントが それに使われないようにします。
- タスクを最後までやり切り、結果を返すこと — コードなら変更した diff またはファイルパス、 文章なら完成したテキスト、読み取り/抽出なら構造化した結果。
- 自分の領分を守ること。タスクがアーキテクチャ級・セキュリティに関わる・正確性が重要・ または深い多段推論を要すると判明したら、停止して理由を 1 行添えてメインエージェントへ 戻すこと — 推測で押し通さない。
- 外科的に、既存のスタイルに合わせて変更すること。可能なときは変更を検証（ビルド/テスト/ lint）すること。
- 巧妙さより明快さと正確さを優先すること。不確かな点は取り繕わず明示すること。
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.
- 9d ago First seen · 25 lines · 187 tokens per session scan A b0c3fb26fa8f
mid-tier is an agent published in the GitHub repository seaosinc/mcp-llm-offload (0 stars, last pushed 2mo ago), licensed MIT. It adds 187 tokens to every session and 482 once invoked, about $0.0009 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.
Other agents, from other repositories
council-advisor
Use this agent when the user is facing architectural decisions, design choices, or has been stuck debugging a problem after multiple failed attempts. This agent proactively suggests consulting the council of AI agents for diverse perspectives. Examples: Context: User is designing authentication for their application…
clawteam-system-architect
System architect task agent — layered abstraction, separation of concerns, evolvable design, NFR-driven, contract-first APIs, explicit trade-offs; multi-view architecture, style matrix, interface principles, ADR-style decisions; DDD, data, resilience, evolution.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.