Tokenizer

### Initial orchestration layer added for token-optimizer-skills. - Introduced four new files: `memory_agent.py`, `orchestrator_config.json`, `router.py`, and `skill_runner.py`. - Lays the foundation for modular and automated skill orchestration within the token optimization workflow. - No existing files modified; these additions prepare the skill for more advanced, multi-agent or multi-skill execution patterns.

Most OpenClaw agents suffer from context window bloat as conversations grow. Loaded skills, long histories, large tool outputs, and repetitive code quickly eat up tokens — leading to higher costs, slower responses, truncated context, and degraded performance. Token Economy Skill Suite fixes this by giving your agent built-in intelligence to monitor and actively manage its own token usage. Key Changes It Introduces: Proactive Diagnosis — The token-audit skill analyzes the current context and produces a professional Token Budget Report showing exactly where tokens are being wasted (skills metadata, history, tool dumps, etc.). Aggressive Optimization — The token-optimizer skill applies 5 powerful strategies: Memory Distillation — Converts long chat histories into compact structured JSON facts (decisions, preferences, constraints, next actions) — often saving 40–70% tokens. Prompt Compression — Uses LLMLingua-2 to shrink large documents or RAG results while keeping meaning intact. Skill Lazy Loading — Identifies and flags bloated SKILL.md descriptions so you can keep only what you need. Context DNA Compression — Summarizes repetitive boilerplate code (imports, UI layouts, standard patterns) into tiny comments — up to 80% savings on code-heavy tasks. Model Dialect Rewriting — Rewrites prompts in the most token-efficient format for your model (especially XML-style for Gemma). Persistent Memory Hygiene — Encourages moving data from expensive Working Memory to cheap Episodic (JSON facts) and Semantic stores, preventing the context death spiral in long-running projects. Real-World Impact: Dramatically lower token usage and API costs in extended sessions More reliable agent behavior (less truncation, better recall of past decisions) Cleaner, faster context windows Works model-agnostically but includes Gemma-specific optimizations Includes ready-to-run helper scripts (analyze_skills.py, distill_memory.py, compress_prompt.py, count_tokens.py) In short: This skill transforms your agent from a passive token burner into a self-optimizing, token-aware system — essential for anyone doing serious work with OpenClaw over long conversations or large codebases.