RDXmin tool adds terse persona and context-saving compressor for Claude
A new tool called RDXmin extends Claude's output terseness with a terse persona system (three levels: lite, full, ultra) and a tool-output compressor that shrinks tool results before they enter context, reducing token usage. The compressor strips ANSI codes, keeps head/tail and error lines, and deduplicates, running behind a safety allowlist to avoid affecting Read/Edit/Write operations.
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HarnessTrim is a new tool that provides a deterministic, benchmarked token-economy layer across coding agent harnesses like Claude Code, Codex, and OpenCode. It uses idempotent reducers to cut token waste from noisy tool output, model verbosity, thinking tokens, and instruction files, without involving an LLM in the reduction path. The tool coordinates existing single-channel solutions (e.g., Caveman, RTK) behind a unified cross-harness policy, and is cache-aware to avoid touching cacheable prompts.
ContextOps: open-source tool to audit and optimize LLM prompt context
ContextOps is an open-source tool that analyzes LLM prompts to detect token waste such as duplicated retrieval chunks, bloated system prompts, oversized conversation history, and repeated tool outputs. It helps developers reduce costs and improve model consistency by auditing what goes into the prompt before inference.
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Ditto extracts only the user's typed messages from local Claude Code and Codex session logs, stripping tool output and assistant replies, and compiles them into a file that an AI agent can read first. It solves the problem of losing personal work patterns and context across many coding sessions, giving the agent a honest record of how the user actually works.
Upload project folder to get optimized markdown for LLM context
A web tool that lets users upload an entire project folder and receive a single, clean, optimized markdown file ready to paste into Claude or Codex. It solves the problem of manually preparing context for LLMs by automatically consolidating and formatting code files. All processing is done client-side for privacy.
ddiff: LLM-generated diff format for isolating feature sets in code
ddiff is a format for isolating feature sets in code using LLM-generated diffs. It works by prompting an LLM to produce a diff of analysis of intents and code changes related to specific features, which can then be used by another LLM to implement the feature natively. The creator provides a live chat to Telegram group and a markdown WYSIWYG editor with rich uploads.