Universal CLAUDE.md reduces Claude output tokens by 63% in benchmarks
A developer has released a universal CLAUDE.md file that significantly reduces token usage in Claude responses while maintaining technical accuracy. The tool was built entirely with Claude's assistance during a single session where Claude researched Reddit threads, GitHub issues, and prompt engineering research.
What it fixes
- "You're absolutely right!" and "Great question!" on every response
- "I hope this helps! Let me know if you need anything!" closings
- Em dashes, smart quotes, Unicode chars that silently break parsers
- Restating your question before answering it
- Unsolicited suggestions and over-engineered code
- "As an AI..." framing
- Hallucination guard - if you correct Claude on something, it treats that as ground truth for the rest of the session and never re-asserts the wrong answer
Benchmark results
The developer tested the same 5 prompts with and without the file:
- Explain async/await: 180 words → 65 words (64% reduction)
- Code review: 120 words → 30 words (75% reduction)
- What is REST API: 110 words → 55 words (50% reduction)
- Hallucination test: 55 words → 20 words (64% reduction)
- Total: 465 words → 170 words (63% reduction)
The developer notes that answers maintain the same technical accuracy - just without the fluff.
Implementation and profiles
The solution requires just one file dropped in your project root with no code changes. It includes three profiles for different use cases:
- CLAUDE.coding.md - for dev projects and code review
- CLAUDE.agents.md - for automation pipelines and bots
- CLAUDE.analysis.md - for data and research work
The tool is built on real GitHub issues (including #3382 with 350+ upvotes) and community research. The repository is open to PRs and profile contributions, with the developer encouraging users to open issues for any annoying behaviors they want fixed.
📖 Read the full source: r/ClaudeAI
👀 See Also
GGUF Model Merging Script and Workflow for Qwen3.5-35B Variants
A Reddit user shared a Python script for merging GGUF model files with minimal loss, specifically combining HauhauCS's Qwen3.5-35B-A3B-Uncensored model with samuelcardillo's Claude-4.6-Opus-Reasoning-Distilled version. The script runs on Google Colab Free Tier and includes quantization support via llama-quantize.
Context Routing Layer Reduces Claude Code Token Usage by Tracking Accessed Files
A developer saved approximately $80 per month on Claude Code usage by adding a context routing layer that prevents the AI from re-reading the same repository files on follow-up turns. The tool tracks what files have already been accessed to reduce redundant token consumption.
rawq: Local CLI Tool for AI Agent Semantic Code Search
rawq is an open-source CLI tool that helps AI agents find relevant code using semantic search with a 33MB local model via ONNX runtime and BM25 lexical search via tantivy. In testing, AI agents using rawq consumed 4x fewer tokens and completed tasks 2x faster compared to blind read/grep tools.
Claude Code Plugin for Reddit Market Research Without API Keys
A Claude Code plugin automates Reddit market research by searching threads, analyzing content, and generating markdown reports with direct links. It requires no Reddit API key, auth, or config files, using public data through a local MCP server.