rb-working-diary

Maintain structured markdown diaries of decisions, findings, and next actions.

Updated Jul 2, 2026
One-click install
npx skills add https://github.com/richardmbailey/rb-skills --skill rb-working-diary
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rb-working-diary
Source: https://github.com/richardmbailey/rb-skills/tree/main/rb-working-diary
Command: npx skills add https://github.com/richardmbailey/rb-skills --skill rb-working-diary

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of losing context, decisions, and progress during long-running AI-assisted projects, especially across multiple sessions or model compactions.

Core Features & Use Cases

  • Durable Memory: Maintains a structured record of decisions, investigations, and open questions that persist beyond the current chat session.
  • Handoff Support: Provides a clear trail for future-you or other agents to resume work exactly where it left off.
  • Use Case: When working on a complex multi-day refactoring task, use this skill to log your findings, rejected alternatives, and the exact next steps to ensure no progress is lost when the conversation context is cleared.

Quick Start

Use the rb-working-diary skill to initialize a new project diary and record the current status of your implementation.

Frequently Asked Questions about rb-working-diary

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I maintain project state and context across multiple AI sessions?

You can maintain project state across sessions by using a structured diary to log decisions, findings, and next actions. This preserves operational memory, ensuring durable continuity and context when resuming long-running tasks.

What is the best way to track decisions and progress during long-running coding tasks?

The best way to track decisions during long-running coding tasks is maintaining a structured project diary. It records investigations and open questions, providing a clear handoff trail to resume work exactly where it left off.

How do I prevent losing context after a model compaction or conversation clear?

To prevent losing context after model compaction, use a working diary to preserve operational memory in local markdown logs. This durable record persists beyond the current chat session and survives context window clearing.

Do I need local filesystem access to manage project continuity logs?

Yes, local filesystem access is required to manage project continuity logs. The skill writes structured diary entries and index files directly to local markdown logs to maintain state across sessions.

Can I use project diaries for investigative research tasks, or are they only for coding?

You can use project diaries for investigative research tasks, coding, or any long-running projects. The structured diary applies to any durable continuity requirement needing cross-session context and state tracking.

Why does my AI assistant forget rejected alternatives and next steps between sessions?

Your AI forgets rejected alternatives because session context is cleared. Logging findings and next steps in a structured diary creates a durable memory trail, preventing lost progress when conversations reset.