A behavioral optimization protocol designed to make AI agents cost-aware, token-efficient, and logically concise.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install claw-smart-context
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 claw-smart-context 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 Smart Context?
Smart Context is a technical framework designed for developers who need to manage the operational costs and latency of their AI workflows. By implementing rigorous response sizing and context pruning, this skill transforms agents into economical powerhouses that prioritize value over verbosity. When integrated into Openclaw Skills, it ensures that every interaction is scaled to the complexity of the task, preventing the common pitfalls of AI over-explanation and redundant tool execution.
This skill is particularly valuable for high-frequency automated tasks where token accumulation can lead to significant overhead. It forces the agent to treat every token as a financial asset, encouraging the use of more efficient search methods like grep, batched tool calls, and strategic model delegation. By adopting the principles of Smart Context, users can maintain high intelligence levels while drastically reducing their compute footprint.
Smart Context Use Cases
- Reducing operational overhead during large-scale code refactoring or repository analysis within Openclaw Skills.
- Improving response latency for simple status checks, acknowledgments, and yes/no queries.
- Optimizing multi-step workflows by batching independent tool calls into a single execution turn.
- Managing long-term development sessions where context window bloat can lead to decreased model performance and increased costs.
How Smart Context Works
- Complexity Analysis: The agent evaluates the user input to categorize it into a specific response tier, from simple one-sentence answers to structured complex planning.
- Context Pruning: The system checks existing context to prevent redundant file reads or memory searches, ensuring tools are only called when new information is required.
- Tool Batching: Independent operations are bundled into single execution blocks to reduce round-trip latency and token overhead.
- Targeted Data Retrieval: For large files, the skill utilizes targeted reads with offsets and limits rather than loading entire documents into the context window.
- Modality Optimization: High-cost vision tools are bypassed in favor of text-based extraction or existing context visibility whenever possible.
Smart Context Setup
To apply these efficiency protocols to your environment, integrate the behavioral guidelines into your agent configuration. For users managing Openclaw Skills via CLI, you can initialize the optimization settings using the following command:
# Initialize the Smart Context behavior protocol
openclaw skill install smart-context --efficiency-mode high
Ensure your system prompts are updated to include the Response Sizing matrix to enforce consistent brevity across different query types.
Smart Context Data Schema & Taxonomy
The Smart Context skill organizes its operational logic through a tiered complexity matrix and tool execution rules:
| Level | Interaction Style | Token Strategy |
|---|---|---|
| T1: Routine | Minimalist | Result only, no preamble, zero tool-call narration. |
| T2: Standard | Procedural | Numbered steps, no fluff, batched read/write operations. |
| T3: Advanced | Analytical | Structured headers, tradeoff analysis, offset-based file reading. |
| T4: Creative | Generative | Full-length responses where quality takes priority over economy. |
Smart Context Advanced Features
- Intelligent batching of independent tool calls to minimize token turn-around.
- Strategic model delegation, routing background tasks to lower-cost models while keeping the main conversation context in the primary model.
- Automated vision suppression to prevent expensive image processing when text equivalents are available.
- Recursive context pruning that skips narration for routine operations to keep the history clean and readable.
SKILL.md
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