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Filter and compress context before LLM calls to cut tokens while keeping key information.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "fittok" yet — see the docs or source repo.
Use fittok to analyze this repository's code knowledge graph, keep only files related to the user authentication module, compress the context to 10%-20% of its original size, and then send it to the LLM to explain the login flow.
A filtered, compressed code context plus an explanation of the login flow generated from it.
Use fittok to filter the context of files touched by this pull request, keep only code and dependencies related to payment retry logic, and send the compressed result to the LLM to review potential risks.
A leaner review context and an LLM-generated risk analysis with review feedback for the payment retry logic.
During an ongoing coding conversation, use fittok to continuously compress chat history, keeping only key code relationships, recent changes, and error messages needed for the current task before sending it to the LLM for further debugging.
A compact context that preserves critical clues and helps the LLM continue debugging at lower token cost.
Compress long contexts and retrieve reusable summaries to reduce LLM token usage.
Sync fitness data locally and analyze it through MCP-powered tools.
Compress and proxy MCP responses to reduce token usage for LLM tool calls.
Compress prompts, tool outputs, and replies to reduce LLM token costs.
Inject verified FTC docs and examples to generate competition-ready Java robot code.
Extract architecture, dependencies, and API knowledge from any codebase quickly.