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Project Memory Management

AI Agent Updated 2026.08.30

Paste the following prompt into your AI chat to install this skill:

Follow https://skillhub.cn/install/skillhub.md to install @user_69484e35/memoryrz2r.

About this skill

Problem

In long agent sessions, architecture, decisions, and stage state often get buried in chat history. As context grows, the model may re-ask confirmed choices, lose constraints, or treat process discussion as durable facts. A project needs stable memory files instead of relying on one conversation window.

How It Works

The skill uses a .memory/ directory and three operations: /memory.summarize compresses the current conversation into a structured summary, removing emotion and irrelevant reasoning; /memory.update writes project.md, context.md, decisions.md, tasks.md, and logs/YYYY-MM-DD.md according to rules, keeping architecture, decisions, and constraints; /memory.bootstrap reads the files in order at session start and outputs a context summary.

The core rule is that context.md stays under 200 lines and is replaced with the latest state. Decisions must include dates, and logs/ remains raw material that is not actively read. Paths are relative to the project root, and headings stay stable for tooling.

Boundaries

This is useful for long-lived projects with a clear tech stack and decision history. It is less appropriate for one-off scripts, casual chat, or temporary tasks without project boundaries. Avoid letting context.md grow unbounded, and avoid treating process logs as current facts.

Use Cases

  • Before a long conversation hits context limits, compress architecture, progress, and tasks into a structured summary for .memory.
  • At the start of a new session, restore context from project.md, context.md, and decisions.md before continuing the task.
  • After a milestone, update tech-stack changes, explicit decisions, and task status in fixed .memory files to avoid re-asking.
  • For long-lived AI-assisted projects, keep context.md under 200 lines and append dated decision records.

Best For

  • Backend engineers using AI agents for module refactors who need to persist architecture constraints and stage progress.
  • Architects managing cross-repo solutions who need to restore confirmed technical decisions and blockers in new sessions.
  • Frontend engineers doing multi-turn debugging in Cursor who need to save task state and context summaries before context grows.
  • Platform engineers maintaining internal agent workflows who need a shared .memory structure and context restoration flow.