2-Prompt System to Carry Context Between Claude Chats Without Token Waste

A Reddit user posted a practical system to solve the problem of hitting Claude's chat length limit mid-project. The solution uses two prompts: one for context extraction from the old chat, another for initialization in the new chat.
The Problem
Long Claude conversations slow down, hit length limits, or get expensive on API. Common workarounds—manual summarization, copy-pasting entire chats, or starting fresh—lose critical decisions, waste tokens, or require re-explaining.
The System
Prompt 1: Context Extraction (paste in old chat)
Instructs Claude to compress the entire conversation into a structured 9-section summary: Objective, Key Context, Decisions Made, Work Completed, Current State, Next Steps, Open Questions/Blockers, Critical Data/Assets, and Style & Preferences. The output must be placed inside one clean code block. Target length: 300–600 words, preserving specifics over generalities.
Prompt 2: Chat Initialization (paste in new chat)
You paste the extracted context block and instruct the new chat to treat it as source of truth. It asks Claude to verify understanding, flag any gaps, and resume from Next Steps rather than restarting.
The exact prompts are provided in the source. For example, the extraction prompt's output structure uses these headers:
- Objective
- Key Context
- Decisions Made (with reasons)
- Work Completed
- Current State
- Next Steps (mark immediate next with →)
- Open Questions / Blockers
- Critical Data / Assets
- Style & Preferences
The initialization prompt includes: "Treat the context as established. Do not re-frame or restart. Maintain all decisions and preferences listed." and asks for a confirmation reply with the objective, immediate next action, and any gaps.
Who It's For
Developers using Claude (API or web) who need to continue long-running projects without losing context or burning tokens on redundant explanations.
📖 Read the full source: r/ClaudeAI
👀 See Also
Strata: A Semantic Layer That Refuses Invalid Queries Before Your LLM Runs Them
Strata is a full-stack semantic layer built by an ex-Netflix engineer that validates and rejects semantically invalid queries before they execute, with partition-aware routing across ClickHouse, Druid, Snowflake, and Athena.

Werld: Open-Ended Artificial Life Simulation with Evolving Neural Networks
Werld is a real-time artificial life simulation where agents with NEAT neural networks evolve their own neural architecture, sensory processing, and behaviors without hardcoded rules or reward functions. The simulation starts with 30 agents on a Watts-Strogatz small-world graph with 64 sensory channels, 7 continuous motor functions, and 29 heritable genome traits.

cxt: A CLI/TUI Tool to Aggregate Code Files into One Clipboard Block for Claude
cxt aggregates entire directories and specific files into a single clipboard-ready block with XML tags and file paths, making it easier for Claude to understand your codebase context.

Local-First Movie Recap Pipeline Using Whisper + CLIP + Ollama
A fully local pipeline that auto-generates narrated movie recap videos using Whisper, CLIP, Ollama, Edge TTS, and FFmpeg. Drop in a movie file, get a narrated recap in ~15 minutes.