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Token Saver 75+

by @mariovallereyes

Automatically classifies requests to optimize cost by routing to the cheapest capable model and applies maximum output compression for 75%+ token savings.

Versionv1.0.0
Downloads1,714
TERMINAL
clawhub install token-saver-75plus

πŸ“– About This Skill


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 | β€” | | 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 β€” 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 β†’ spawn Codex
  • Bulk text processing (>3 items) β†’ spawn Groq
  • Complex multi-step tasks β†’ spawn Opus (T4)
  • Simple formatting/rewriting β†’ spawn Groq
  • When NOT to spawn

  • T1 questions (yes/no, time, status) β€” handle directly
  • Single tool calls (calendar, web search) β€” handle directly
  • Short responses that need no processing β€” handle directly
  • Spawn patterns

    Groq (free bulk work):

    sessions_spawn(
      task: "",
      model: "groq/llama-3.1-8b-instant"
    )
    

    Codex (all code):

    sessions_spawn(
      task: "Write  code that . Include comments. Output the complete file.",
      model: "openai/gpt-5.3-codex"
    )
    

    Opus (T4 strategy):

    sessions_spawn(
      task: ". 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 β€” spawned agents have no conversation history 2. Be specific β€” vague tasks waste tokens on clarification 3. One task per spawn β€” don't bundle unrelated work 4. For code: always use Codex β€” never write code yourself

    Output Compression (applies to ALL tiers, ALL models)

    Templates

  • STATUS: OK/WARN/FAIL one-liner
  • CHOICE: A vs B β†’ Recommend: X (1 line why)
  • CAUSEβ†’FIXβ†’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") β†’ 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? β†’ No tool. 2. Batchable? β†’ Parallelize. 3. Can a spawned Groq handle it? β†’ Spawn instead of doing it yourself. 4. Cheapest path? β†’ memory_search > partial read > full read > web. 5. Needed? β†’ Do not fetch "just in case."

    Failure Protocol

  • If Groq spawn fails β†’ retry with GPT-5.2
  • If Codex spawn fails β†’ retry with GPT-5.2
  • If orchestrator can't handle T3 β†’ 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]

    πŸ”’ Constraints

  • 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") β†’ 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 |