agency-autonomous-optimization-architect

Shadow-tests LLM providers and routes API traffic with cost and security circuit breakers.

Updated Jul 27, 2026
One-click install
npx skills add https://github.com/imMamdouhaboammar/Mimera --skill agency-autonomous-optimization-architect-immamdouhaboammar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agency-autonomous-optimization-architect
Source: https://github.com/imMamdouhaboammar/Mimera/tree/main/.agents/skills/engineering-autonomous-optimization-architect
Command: npx skills add https://github.com/imMamdouhaboammar/Mimera --skill agency-autonomous-optimization-architect-immamdouhaboammar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams running LLM-powered applications face runaway API costs, unvalidated model upgrades, and outages when a single provider fails. This Skill governs autonomous model optimization by shadow-testing new models against production baselines while enforcing hard financial and security guardrails. ## Core Features & Use Cases - Shadow Traffic A/B Testing: Route a percentage of live traffic asynchronously to experimental models and grade them with explicit mathematical scoring criteria (accuracy, latency, hallucination penalties). - Autonomous Routing with Circuit Breakers: Rank providers by historical performance and automatically trip failovers on cost overruns, retry-limit breaches, or anomalous traffic spikes. - AI FinOps Guardrails: Enforce maximum cost per execution, retry caps, and timeouts on every external API call, with cheaper fallback providers mapped in advance. - Use Case: When a new cheaper model is released, shadow-test it on 5% of production traffic, verify it scores within tolerance of the baseline, then autonomously update router weights to cut costs by 80%. ## Quick Start Ask the agent to design a multi-provider LLM router with shadow testing, cost limits per execution, and circuit-breaker failover for your current API workload.

Frequently Asked Questions about agency-autonomous-optimization-architect

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I shadow test a new LLM model against production traffic?▼

Route a small percentage of live requests asynchronously to the experimental model while the production model still serves users. Grade both outputs with explicit scoring criteria such as accuracy, latency, and hallucination penalties before promoting the new model.

How do I prevent runaway LLM API costs in my application?▼

Set a maximum cost per execution, cap retries, and enforce timeouts on every external call. Map a cheaper fallback provider in advance and trip a circuit breaker when cost limits or failure thresholds are exceeded.

What is a circuit breaker for LLM API routing?▼

A circuit breaker monitors a provider's failure count and cost, then automatically stops sending traffic to it when thresholds are breached. Requests fail over to a designated cheaper provider and an admin alert is triggered.

Can this approach handle provider outages like Anthropic or OpenAI downtime?▼

Yes. The router ranks multiple providers and skips any with a tripped circuit breaker, so traffic automatically fails over to healthy alternatives. This maintains workflow completion even during individual API outages.

Why should LLM evaluation avoid subjective grading?▼

Subjective grading produces inconsistent promotion decisions. Define mathematical criteria upfront, such as points for JSON formatting and latency minus penalties for hallucinations, so model comparisons are reproducible and data-driven.