model-strategy

Solid

Multi-Modell Orchestrierung und Model-Switching Strategie. Score-basierte Modellauswahl, Cross-Agent-Delegation (Gemini, Codex, Ollama), Advisor-Pairing, Eskalations-Trigger, Berechtigungsmatrix und Kosten-Effizienz-Optimierung.

AI & Automation 2 stars 1 forks Updated today MIT

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Quality Score: 81/100

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Issue Health 10%

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Description 5%

100

Skill Content

<img src="banner.png" width="100%" alt="model-strategy banner"> # Model-Switching Strategie > Multi-Modell Orchestrierung: Score-basierte Modellauswahl, Cross-Agent-Delegation, Advisor-Pairing, Eskalations-Trigger und Kosten-Effizienz-Optimierung --- ## 1. Modell-Katalog ### Claude (Subagent-fähig via Agent-Tool) ``` Level 4 (Reviewer): Opus 4.8 — Advisor, Math-Review [nur User: /model, /advisor] Level 3 (Stratege): Opus 4.6 — Architektur, Konzepte [Subagent: model:"opus"] Level 3 (Kreativ): Fable 5 — Kreative Texte, Stories [Subagent: model:"fable"] Level 2 (Arbeitstier):Sonnet 4.6— Implementation, Debug [Subagent: model:"sonnet"] Level 1 (Schnell): Haiku 4.5 — Boilerplate, Formatting [Subagent: model:"haiku"] ``` ### Externe Agenten (Companion-Scripts / SSH) ``` Level 2-3: Gemini 3.5 pro — Recherche, wiss. Datenbanken [agy-companion CLI] Level 2: Gemini 3.5 flash— Schnelle Recherche [agy-companion CLI] Level 2-3: Codex 5.5 (GPT) — Code-Review, Code-Gen [codex-companion CLI] Level 2: Codex 4.5 (GPT) — Einfachere Code-Aufgaben [codex-companion CLI] ``` ### Lokale Modelle (Token-frei, 24/7) ``` Level 1-2: Ollama (Qwen 3.5:35b-a3b) — Haiku-bis-Sonnet-Niveau [<ollama-host>:11434] Aufruf: SSH + curl http://<ollama-host>:11434/v1/chat/completions Oder: Buddha-Delegation via BACH Control API :8081 ``` ### Erreichbarkeits-Matrix | Modell | LLM-startbar | Aufrufweg | Einschränkungen | |--------|-...

Details

Author: ellmos-ai
Repository: ellmos-ai/skills
Created: 4 months ago
Last Updated: today
Language: Python
License: MIT

Integrates with

Anthropic · AI Ollama · AI SQLite · Database

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Build multi-component features by decomposing the work and dispatching subagents, assigning a model tier per subtask across Claude 5, Gemini 3.6, Codex, and Kimi. Proposes Subagents versus Agent Teams topology by task complexity and token cost. Use when the user says "implement", "build this feature", "execute the plan", "in parallel", "dispatch agents", "subagents", "which model should", "実装して", "作って", "並列で", "サブエージェント", "プランを実行", "どのモデルで", or runs /superforge-dev.

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Testing & QA Listed

co-pilot-loop

Cooperative Multi-Agent Coordination Loop. Spawns a lightweight companion sub-agent (Gemini 3.5 Flash Low) to perform spec discovery, planning, and implementation. The primary agent (Claude) acts as the QA Director, answering Gemini's questions, approving the spec/plan, and running verification tests. Assumes the superpowers plugin is installed in the target repository.

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stuntdouble

Your frontier model is the star — stop making it do the stunts. Two modes. ADVISOR: analyzes a task and recommends the best AI model (Anthropic/OpenAI/Google) with the exact switch command. ORCHESTRATOR: stays ON for the whole session (like a mode toggle) — every task gets scored, decomposed, and dispatched to "stunt double" workers running the cheapest capable model OF THE HOST'S OWN PROVIDER (Claude models in Claude Code, OpenAI models in Codex, Gemini models in Gemini CLI), with the star (strongest model) doing only the final close-up: verification. TRIGGER on /stuntdouble, "stuntdouble on/off", "which model should I use", "select the best model", "optimize model usage", "is this task worth Opus". DO NOT TRIGGER when the user asks about model pricing/APIs in their own application code.

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Davienzomq