Token-Optimization-Mastery

🎯 The Core Problem

Context Inflation & Bandwidth Exhaustion: As Large Language Models (LLMs) handle massive codebases, they suffer from "Instruction Dilution" (forgetting primary constraints due to an overloaded context window). This results in extreme API costs, massive latency, and severe bandwidth exhaustion. V3.1 introduces mathematical output limits to fully eliminate AI verbosity.

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✨ Features of the New Version (V3.1)

V3.1 integrates Graph Navigation Protocols (inspired by advanced code-review graphs) into the existing TOON/Kortex architectures:

1. Strict 800-Token / 5-Call Cap: AI is strictly constrained to complete any task in ≤5 tool calls and ≤800 total output tokens.
2. Minimal Detail Level: AI must operate at detail_level="minimal" and only escalate when strictly necessary.
3. Graph Impact Radius: AI must trace callers_of and callees_of to measure "Impact Radius" before editing code, preventing the need to read entire files.
4. Auto-Memory Logging: Task completions and architectural decisions (ADRs) are silently logged to the Claude-Mem corpus.

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🏗 The 5-Layer Architecture

Layer 1: Strict Communication Rules

• Extreme conciseness (Bandwidth Conservation Mode).
• Fresh AI sessions per major task to prevent context drift.
• V3.1 Rule: Strict output cap of ≤800 tokens per task.

Layer 2: TOON & Graph Trace Strategy

• Run smart_outline or smart_search to map dependencies.
• Trace Call Graphs (Callers/Callees) to measure Impact Radius safely.
• Use smart_unfold to expand only the targeted symbol.

Layer 3: Kortex Semantic-Map / Claude-Mem Paging

• Search: Retrieve semantic index IDs only (~100 tokens).
• Timeline: Get surrounding conversational context (~300 tokens).
• Fetch (JIT): Retrieve detailed records for a maximum of 3-5 filtered IDs (~1,500 tokens).

Layer 4: Corpus Workflow & Auto-Logging

• Use build_corpus and prime_corpus to query vectors instead of polluting active chat memory.
• Silently log all task completions and Architecture Decision Records (ADRs) back into the corpus for future zero-cost recall.

Layer 5: Scoped Grep (Find Before Read)

• Never read a file to find a pattern. Use grep_search with specific extension filters (Includes=["*.py"]) and line-number targeting.

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🚀 Official V3.1 Empirical Benchmarks

Test Date: April 30, 2026 | Simulated Task: Deep Module Extraction & Refactor

Conclusion: The V3.1 architecture successfully reduces overall task token consumption by >96%, achieving near-instantaneous latency and zero context drift.

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📚 Documentation

• 📄 قراءة الدليل باللغة العربية (AR)
• 📄 Read the Skill Guide in English (EN)

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.