Read Optimizer for Openclaw

A high-performance file reading utility designed to minimize token consumption through targeted sampling and pattern matching.

autogame-17
v1.0.0
Feb 17, 2026
0
730
10

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install read-optimizer

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install read-optimizer 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 Read Optimizer?

Read Optimizer is a specialized utility built to solve the challenge of context window management within Openclaw Skills. When dealing with massive log files, long documentation, or extensive source code, feeding the entire file to an AI agent is often slow and expensive. This skill provides a smarter way to interact with the file system by extracting only the most relevant parts of a file.

By leveraging Read Optimizer, developers can ensure their agents remain fast and cost-effective. The tool acts as a middleware that samples files based on user-defined strategies, ensuring that the AI receives the high-signal data it needs without the noise of irrelevant lines. It is an essential component for any workflow involving large-scale data analysis or complex debugging in Openclaw Skills.

Read Optimizer Use Cases

  • Scanning multi-gigabyte server logs for specific error signatures or stack traces.
  • Reviewing the beginning and end of long technical specifications to understand overall structure.
  • Analyzing only the modified code in a large repository by targeting git-specific changes.
  • Reducing latency during automated code audits by focusing on high-interest patterns using regex.

How Read Optimizer Works

  1. The user or agent specifies a target file and selects an optimization strategy: smart, grep, or diff.
  2. In Smart Mode, the skill extracts the top and bottom lines of a file to provide a summary view while skipping the middle.
  3. In Grep Mode, the tool searches the file using regex patterns and returns only the matches along with specified context lines.
  4. In Diff Mode, the skill identifies lines changed since the last git commit, focusing the agent's attention on recent updates.
  5. The optimized content is then piped back to the agent interface within the Openclaw Skills ecosystem, preserving the token budget.

Read Optimizer Setup

To use this skill within your environment, ensure you have the necessary scripts in your skills directory. You can invoke the optimizer directly via the command line:

# Smart read with custom line count
node skills/read-optimizer/index.js --file path/to/large-file.log --mode smart --lines 100

# Focused search for specific errors
node skills/read-optimizer/index.js --file path/to/app.js --mode grep --pattern "const|function" --context 3

Read Optimizer Data Schema & Taxonomy

The Read Optimizer skill processes file paths and returns text segments based on the following configuration parameters:

Option Type Default Description
--file String Required The file path to be processed.
--mode Enum smart The sampling strategy (smart, grep, diff).
--lines Number 50 Number of lines to read for head and tail.
--pattern String null Regex pattern used in grep mode.
--context Number 2 Number of context lines to include around grep matches.

Read Optimizer Advanced Features

  • Context-aware Grep: Retrieve lines surrounding a match to give the agent better situational awareness without reading the whole file.
  • Git Integration: Native support for git-diff allows Openclaw Skills to perform incremental analysis on codebases.
  • Dual-ended Sampling: Simultaneously inspect the start and end of files to capture headers and footers or recent log entries.
  • Token Latency Reduction: Specifically tuned to improve the response time of LLMs by stripping redundant data before processing.

SKILL.md


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