What happens if a claim fails verification?

You can use the /cf-verify command to trigger a second-pass review by a 'critic' agent or request stronger evidence for specific claims.

Can I use this for large code repositories?

Yes, it is specifically designed for 'reading the whole repository' by splitting the task among FileWorkers that scan shards in parallel.

How does the Context Firewall prevent context window overflow?

It routes large inputs through sub-agents that summarize and extract only the most relevant evidence, passing back compressed results instead of raw data to the master agent.

What is the Evidence Contract in this skill?

The Evidence Contract is a strict requirement that every claim made by the AI must include a locator (like a line range or symbol) so the user can verify the facts without re-reading everything.

Context Firewall

Name: Context Firewall
Author: lollipopkit

bylollipopkit

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Analytics & Monitoring

Optimizes large-scale context processing by routing heavy inputs through sub-agents to prevent window overflow and ensure verifiable results.

The Context Firewall skill implements a sophisticated map-reduce workflow designed to protect Claude's master context from being overwhelmed by massive logs, expansive repositories, or noisy tool outputs. By utilizing specialized sub-agents—such as FileWorkers and Aggregators—it processes large datasets in parallel to produce compressed, auditable results backed by an 'Evidence Contract'. This approach ensures that every claim is verifiable via specific line locators while maintaining a lean, high-performance context window, making it ideal for deep codebase analysis and complex troubleshooting tasks.

Key Features

01Evidence Contract system for verifiable claims with specific line-range locators

02Map-reduce workflow for parallel processing of massive files and logs

03Context overflow prevention through automated sub-agent preprocessing

04Detailed coverage reporting to identify scanned data and explicit gaps

05Risk-based verification sampling to audit AI claims at different confidence levels

065 GitHub stars

Use Cases

01Performing full-repository synthesis and cross-file pattern matching

02Filtering noisy tool outputs from web crawls or API responses into structured evidence

03Analyzing and summarizing multi-gigabyte log files without exhausting the token window

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lollipopkit/cc-plugins context-firewall

For use in Claude.ai and ChatGPT

Download Skill