A sophisticated optimization framework designed to reduce LLM API token consumption by 20-35% through intelligent estimation, memory management, and history compression.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install claude-code-api-optimizer-skill
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install claude-code-api-optimizer-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Token Optimizer?
The Token Optimizer is a model-agnostic framework within the Openclaw Skills ecosystem designed to combat the rising costs of LLM API usage. By implementing a standardized set of rules for context management, it allows developers to maintain high-quality agent performance while significantly reducing the financial overhead of long-running conversations.
This skill focuses on three primary pillars: estimating usage before it happens, extracting high-value memory into persistent storage, and compressing stale conversation history. By integrating these Openclaw Skills into your agent's core logic, you ensure that only the most relevant and cost-effective data is transmitted to the model provider.
Token Optimizer Use Cases
- Managing extensive coding sessions where conversation history often leads to context window bloat.
- Reducing costs for production AI agents that handle large volumes of structured JSON or multi-file codebases.
- Maintaining long-term project memory across multiple sessions without incurring the cost of re-reading previous logs.
- Optimizing token budgets for developers working with expensive high-reasoning models using Openclaw Skills logic.
How Token Optimizer Works
- The agent performs a pre-send token estimation using specific character-count formulas to predict the cost of the upcoming payload.
- If the estimated payload exceeds a defined threshold (e.g., 8,000 tokens), the agent triggers reduction logic like JSON stripping or code snippet isolation.
- A secondary, low-cost model extracts non-obvious context and project decisions into a structured directory-based memory system.
- As the conversation grows, the agent compresses older exchanges into a dense summary block, preserving only the last six messages in full fidelity to save up to 80% on history tokens.
Token Optimizer Setup
Integrating the Token Optimizer is straightforward as it requires no external dependencies. Follow these steps to activate these Openclaw Skills:
- Copy the
SKILL.mdcontent directly into your agent's prompt or dedicated skills directory. - Add the token estimation formulas to your pre-request workflow:
# Example estimation logic
tokens = char_count / 4 (text) or char_count / 2 (JSON)
- Initialize a
./memorydirectory to store persistent information. - Set your token budget threshold within your agent's configuration parameters.
Token Optimizer Data Schema & Taxonomy
The skill organizes its memory and optimization metadata using a clear, hierarchical structure to ensure fast retrieval and low overhead.
| File / Element | Description | Format |
|---|---|---|
MEMORY.md |
The main index file acting as a pointer to all extracted knowledge. | Markdown List |
memory/*.md |
Individual topic files (e.g., project-goals.md) containing detailed context. | Markdown with Frontmatter |
<compressed-context> |
A block injected at the top of prompts containing dense summaries of past turns. | XML-wrapped Markdown |
token-formula.md |
Reference sheet for calculating precise character-to-token ratios. | Markdown |
Token Optimizer Advanced Features
- Parallelized I/O for simultaneous reading and writing of memory files during extraction rounds.
- Multi-model orchestration where lightweight models handle bookkeeping while expensive models handle core reasoning.
- Dynamic context window triggers that initiate compression once the conversation hits 60% of the model's total capacity.
- Automated JSON pruning that removes redundant nested objects and null fields to optimize structured data payloads within Openclaw Skills.
SKILL.md
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