Agents operate within defined contexts, which are crucial for their performance and adherence to instructions. Efficiently managing this context is key to maintaining clarity and minimizing operational costs. context-audit is an AI agent skill specifically engineered to measure token efficiency in always-loaded prompt files. It helps agents and developers maintain a lean and effective operational context by systematically identifying issues without directly modifying any files. This tool proves highly valuable for individuals responsible for managing complex agent instructions, system prompts, and core identity configurations, ensuring that the foundational layers of an agent remain optimized and free from unnecessary overhead.
What context-audit examines
This tool focuses its analysis on your agent's always-loaded prompt files. Specifically, it thoroughly examines foundational documents such as CLAUDE.md, AGENTS.md, MEMORY.md, and any rendered identity files that define the agent's core persona and capabilities. The primary objective is to measure their token efficiency. Within these critical files, this tool finds several types of issues that can inflate context size, introduce ambiguity, or reduce the agent's overall clarity and precision.
It identifies redundancy, where information is duplicated across different files or even within sections of the same file. Such duplication not only consumes valuable tokens but can also lead to inconsistent interpretation if the redundant pieces diverge. The tool also flags contradictions, which are especially problematic as they can lead to ambiguous instructions or conflicting operational guidelines for the agent. Stale information, no longer relevant but still present in the context, is another key target; keeping outdated data can misdirect an agent or simply waste token allocation. Furthermore, it looks for opportunities for skill-extraction, suggesting where common patterns or repeated instruction blocks might be more efficiently refactored into distinct, callable agent skills. This can modularize context and make it more manageable.
All these findings are ranked based on their potential for token savings. For example, imagine your CLAUDE.md file contains detailed instructions on how to handle specific user requests, and a dynamically rendered identity file includes nearly identical guidance. context-audit would detect this overlap, categorize it as redundancy, and then provide a ranking of this finding based on precisely how many tokens could be saved if this duplicated information were addressed. The critical aspect is that it offers these insights and potential savings without making any direct edits to your actual files, leaving control in your hands.
How context-audit operates
The operation of this skill follows a clear, structured five-step process to deliver its comprehensive insights. First, it systematically enumerates the entire file stack that constitutes the agent's always-loaded context. During this initial discovery phase, it also precisely measures the sizes of these files using a deterministic method: the character count of the file's content is divided by four. This calculation provides a consistent and reproducible estimate of token usage for each component.
Second, following the measurement phase, the tool proceeds to read and rigorously analyze the content across six specific dimensions. These dimensions are crucial for a holistic evaluation: efficiency of the text (conciseness and impact), identification of redundancy (duplicated content), detection of contradictions (conflicting instructions), evaluation of skill-worthiness (potential for new, reusable skills), assessment of staleness (outdated information), and overall clarity of the context. Each of these dimensions contributes to a comprehensive understanding of the context's health.
Third, every finding generated from this detailed analysis is then classified by its associated risk level. The risk categories are clearly defined as zero, low, or medium, offering a straightforward way to prioritize attention based on the potential negative impact of each issue. Fourth, before generating the final output, the draft report undergoes a quality check. This involves gating the report through an eval runner, a step designed to ensure the accuracy, consistency, and overall quality of the generated findings and subsequent recommendations.
Finally, the tool delivers a comprehensive, ranked recommendation table. This table is highly actionable, as it includes specific evidence for each identified finding, detailing exactly what the issue is. Crucially, it also indicates the precise locations within the files where these issues occur, allowing for targeted and efficient follow-up actions by the developer or system administrator. By default, a single cheap-model performs the judging for these analyses, offering a cost-effective solution, though a multi-model panel can be requested for situations requiring a more thorough and diverse evaluative perspective.
Activating and understanding context-audit’s scope
You can trigger context-audit using several natural language phrases, making it straightforward to initiate the analysis process. Common triggers that activate this skill include 'context audit', 'context diet', 'system prompt audit', 'prompt compression', 'reduce context size', and 'token hygiene'. These specific phrases prompt the skill to perform its in-depth analysis on your agent's persistent, always-loaded context.
It is important to clearly understand what this tool is designed to achieve and, equally, what functionalities are outside its scope. This skill is not for editing files directly; its role is purely diagnostic, delivering a report with findings and recommendations. It does not audit on-demand skills, which are components loaded only when specifically invoked by the agent. Similarly, external reference documents, which serve as supplementary knowledge bases, are outside its analytical purview. Furthermore, while it identifies issues within rendered files, it does not recommend direct edits to these rendered outputs. This is a deliberate design choice, as rendered files are often dynamically generated and direct modifications might be overwritten or lead to inconsistencies with their source. The core purpose of context-audit is to provide a comprehensive, prioritized view of your always-loaded context's efficiency. It guides manual optimization efforts by pinpointing exactly where attention is needed to maintain a clean, effective, and resource-efficient agent configuration. It empowers you with the data needed for informed decisions regarding context management.
FAQ
Q: Does the tool change my files? A: No, it delivers a report with findings and recommendations, but does not edit files itself.
Q: What kind of files does it check? A: It targets always-loaded prompt files like CLAUDE.md, AGENTS.md, MEMORY.md, and rendered identity files.
Q: How accurate is its token measurement? A: It uses a deterministic measurement, calculating characters divided by 4 for token estimation.
Closing: Use context-audit to keep your AI agent's always-loaded context lean and precise. Regular audits help ensure optimal performance and resource usage, directly impacting efficiency.
