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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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.
HarnessTrim: a deterministic, benchmarked token-economy layer across Claude Code, Codex & OpenCode
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.
CTOs share playbooks for governing LLM cost and usage in production
Engineering leaders discuss strategies for managing LLM costs and usage as AI features scale from prototype to production. A key challenge is that user-facing workflows often trigger multiple LLM calls, making costs non-obvious during MVP stages.
Understudy: local Ollama drafts answers before Claude, saving API costs
Understudy is a single-file hook that intercepts prompts to Claude Code and routes routine tasks (summarization, classification, JSON conversion, etc.) to a local Ollama model for a draft. The draft is injected as context with instructions for Claude to verify and deliver if correct, or discard and redo if not. It saves API costs by offloading grunt work to a free local model while keeping Claude as the quality gate.
Developer seeks prompt caching optimization for GitHub Copilot Agent Mode with GPT-5.6
A developer is trying to optimize prompts for GitHub Copilot Agent Mode, noting that GPT-5.6 models make prompt caching more valuable due to specific Cache Read and Write costs. They reference OpenAI's API documentation for prompt caching (1024-token prefix, 128-token increments, identical prefix matching, short-lived in-memory caches, optional 24-hour extended caches) but cannot find whether GitHub Copilot exposes the same behavior or has its own orchestration layer.