VRAM scaling skill. Wraps `serve vram`, `serve downscale`, and the scaling ladder. Documents the 3-tier (Beefy/Modest/Thin) hardware detection, the 7-step Beefy scaling ladder, the Modest (5-step) and Thin (4-step) ladders, when to trigger downscale, and how to recover when VRAM frees up. Includes the optimization priority ladder (262K → 30 tok/s → 1M → max speed). Complements the turbofit core skill — turbofit launches models, sirvir-scale adapts them to live conditions.
Token usage monitoring and budget skill. Documents how to read Hermes state.db for real usage data, track spending against a monthly budget, set alert thresholds (75% yellow, 90% orange, 100% red), and suggest upgrades/downgrades based on utilization. Complements the turbofit core skill — turbofit serves models, sirvir-budget tracks what they cost.
Model fleet manager and intelligence engine for Hermes Agent. Autonomous model lifecycle manager — local serving, benchmarking, auto-scaling, API fallback, HuggingFace scanning, creator tracking, backend optimization, budget monitoring, and external app endpoint serving. Optimization priority: 262K context → 30 tok/s → 1M context → max speed.
Dynamic model routing for task-specific optimization. Selects optimal model based on task type, priority, and cost.
Opinionated unified LLM backend (turbofit v5.1). Picks the best main + aux model for your hardware — local or API — launches them detached, wires Hermes-Agent config, and adapts to live VRAM pressure via a scaling ladder. Three hardware tiers: Beefy (local+local), Modest (API+local), Thin (API+API). `serve auto main` auto-detects GPU and suggests the right setup. API fallback is always available (free: DeepSeek V4 Pro + Kimi K2.6). Replaces llama-launch, omni-va, and ad-hoc llama-server scripts. Catalog schema supports per-model binary pinning (atomic fork vs stock), named flag presets (nextn, draft-mtp, turbo4-kv, vision-mmproj), tier ladder (s/sf/sd/f/c), and the 64K Hermes context floor is enforced everywhere. End-user UX is `serve auto main` and the user is done.
Model benchmarking skill. Wraps `serve bench` and lm-eval-harness to run standardized benchmarks (MMLU, GPQA, SWE-bench, HumanEval, AIME) on local fleet models and monitored API models. Documents the benchmark workflow, score interpretation thresholds, and how benchmark results feed back into tier assignments in model-database.yaml. Complements the turbofit core skill — turbofit launches and serves models, sirvir-bench measures them.
Model research skill. Wraps HuggingFace scanning, OpenRouter pricing sync, and creator quality tracking. Documents how to scan HuggingFace for new GGUF models matching fleet archetypes, assess creator quality, update the model database, and sync to GitHub. Includes the creator quality database schema and the daily scan workflow. Complements the turbofit core skill — turbofit serves models, sirvir-research finds and vets them.
External app endpoint serving skill. Documents how to spin up OpenAI-compatible endpoints for ANY app (not just Hermes), manage port assignments, stop external servers, and recommend the right model for a given use case. Includes the 'serve me a model' workflow. Complements the turbofit core skill — turbofit serves Hermes, sirvir-serve serves the world.