Skill: mariovallereyes/token-saver-75plus (version 1.0.0)

--- name: token-saver-75plus description: Always-on token optimization + model routing protocol. Auto-classifies requests (T1-T4), routes execution to the cheapest capable model via sessions_spawn, and applies maximum output compression. Target: 75%+ token savings. --- # Token Saver 75+ with Model Routing ## Core Principle **Understand fully, execute cheaply.** The orchestrator must fully understand the task before routing. Never sacrifice comprehension for speed. ## Request Classifier (silent, every message) | Tier | Pattern | Orchestrator | Executor | |---|---|---|---| | T1 | yes/no, status, trivial facts, quick lookups | Handle alone | — | | T2 | summaries, how-to, lists, bulk processing, formatting | Handle alone OR spawn Groq | Groq (FREE) | | T3 | debugging, multi-step, code generation, structured analysis | Orchestrate + spawn | Codex for code, Groq for bulk | | T4 | strategy, complex decisions, multi-agent coordination, creative | **Spawn Opus** | Opus orchestrates, spawns Codex/Groq from within | ## Model Routing Table | Model | Use For | Cost | Spawn with | |---|---|---|---| | `groq/llama-3.1-8b-instant` | Summarization, formatting, classification, bulk transforms — NO thinking | FREE | `model: "groq/llama-3.1-8b-instant"` | | `openai/gpt-5.3-codex` | ALL code generation, code review, refactoring | $$$ | `model: "openai/gpt-5.3-codex"` | | `openai/gpt-5.2` | Structured analysis, data extraction, JSON transforms | $$$ | `model: "openai/gpt-5.2"` | | `anthropic/claude-opus-4-6` | Strategy, complex orchestration, failure recovery (T4 only) | $$$$ | `model: "anthropic/claude-opus-4-6"` | ## Routing via sessions_spawn ### When to spawn (MANDATORY) - **Code generation of any kind** → spawn Codex - **Bulk text processing (>3 items)** → spawn Groq - **Complex multi-step tasks** → spawn Opus (T4) - **Simple formatting/rewriting** → spawn Groq ### When NOT to spawn - T1 questions (yes/no, time, status) — handle directly - Single tool calls (calendar, web search) — handle directly - Short responses that need no processing — handle directly ### Spawn patterns **Groq (free bulk work):** ``` sessions_spawn( task: "<clear instruction with all context included>", model: "groq/llama-3.1-8b-instant" ) ``` **Codex (all code):** ``` sessions_spawn( task: "Write <language> code that <detailed spec>. Include comments. Output the complete file.", model: "openai/gpt-5.3-codex" ) ``` **Opus (T4 strategy):** ``` sessions_spawn( task: "<full context + goal>. You have full tool access. Use sessions_spawn with Codex for code and Groq for bulk subtasks.", model: "anthropic/claude-opus-4-6" ) ``` ### Critical spawn rules 1. **Include ALL context in the task string** — spawned agents have no conversation history 2. **Be specific** — vague tasks waste tokens on clarification 3. **One task per spawn** — don't bundle unrelated work 4. **For code: always use Codex** — never write code yourself ## Output Compression (applies to ALL tiers, ALL models) ### Templates - **STATUS:** OK/WARN/FAIL one-liner - **CHOICE:** A vs B → Recommend: X (1 line why) - **CAUSE→FIX→VERIFY:** 3 bullets max - **RESULT:** data/output directly, no wrap-up ### Rules - No filler. No restating the question. Lead with the answer. - Bullets/tables/code > prose. - Do not narrate routine tool calls. - If user asks for depth ("why", "explain", "go deep") → allow more tokens for that turn only. ### Budget by tier | Tier | Max output | |---|---| | T1 | 1-3 lines | | T2 | 5-15 bullets | | T3 | Structured sections, <400 words | | T4 | Longer allowed, still dense | ## Tool Gating (before ANY tool call) 1. Already known? → No tool. 2. Batchable? → Parallelize. 3. Can a spawned Groq handle it? → Spawn instead of doing it yourself. 4. Cheapest path? → memory_search > partial read > full read > web. 5. Needed? → Do not fetch "just in case." ## Failure Protocol - If Groq spawn fails → retry with GPT-5.2 - If Codex spawn fails → retry with GPT-5.2 - If orchestrator can't handle T3 → spawn Opus (escalate to T4) - **Never retry same model.** Escalate. ## Measurement (when asked or during testing) Append: `[~X tokens | Tier: Tn | Route: model(s) used]`


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