Memory Manager

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Memory Manager Skill

Overview

This skill provides a standardized protocol for managing the project's persistent memory file (memory.instructions.md). It ensures that AI agents can reliably save and restore context across chat sessions, regardless of which instruction directory the project uses. This skill is agent-agnostic — any agent in the ecosystem can invoke it.

When to Use

• Write Mode: At the end of a significant milestone or phase completion, when the user agrees to save progress.
• Read Mode: At the start of a new chat session to bootstrap context from prior sessions.

Workflow 1: File Discovery Protocol (Mandatory First Step)

⚠️ DIRECTIVE: This workflow MUST be executed before ANY read or write operation. Never hardcode or assume the memory file path.

1. Search recursively for memory.instructions.md across ALL subdirectories within the project, prioritizing the following instruction roots and their subfolders:
   • .omp/instructions/ (and subfolders)
   • .pi/instructions/ (and subfolders)
   • .commandcode/instructions/ (and subfolders)
   • .opencode/instructions/ (and subfolders)
   • .github/instructions/ (and subfolders)
   • .agents/instructions/ (and subfolders)
   • instructions/ (and subfolders at the project root)
2. Use recursive search tools (such as grep_search searching for filename memory.instructions.md or glob patterns like **/memory.instructions.md) to scan across subfolders, ignoring build/vendor folders (node_modules, .git, dist).
3. Resolution:
   • If exactly ONE file is FOUND: Lock the discovered path as the target for all subsequent read/write operations in this session.
   • If MULTIPLE files are FOUND: You MUST pause and present the list of found paths to the user. Ask the user which path should be locked as the active memory for this session. Do NOT proceed until the user explicitly chooses one.
   • If NOT FOUND in any location: Create the instructions/ folder at the project root and initialize a new memory.instructions.md file inside it using the Initial Memory Template below.

Initial Memory Template (For New Files Only)

# Project Memory Log

> Active Location: [path_where_this_file_is_created]
> This file is managed by the `memory-manager` skill.
> It persists context across AI chat sessions to prevent knowledge loss.
> Do NOT manually edit this file unless necessary.

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Workflow 2: Read Mode (Context Bootstrap)

Use this workflow to load context at the beginning of a new session.

1. Execute Workflow 1 to locate the memory file.
2. Read the entire file using the appropriate read tool.
3. Extract and internalize the following critical fields from the most recent checkpoint entry:
   • Current SDLC Phase — Which phase of the development lifecycle is active.
   • Active Artifacts — Status of key SDLC documents (PRD, Spec, Plan).
   • Latest Milestones — What was accomplished in the last session(s).
   • Dead-Ends — Approaches that failed previously. Do NOT repeat these.
   • Pending Actions / Blockers — What remains to be done or what issues are unresolved.
   • Checkpoint Tail — The 1-sentence HTML comment at the bottom for rapid context recovery.
4. Acknowledge to the user (in Bahasa Indonesia) that context has been loaded AND explicitly state the locked path. Example:

   "Saya telah membaca memori proyek dari [discovered_path]. Fase SDLC terakhir adalah [Phase]. Progres terakhir mencakup [Milestones]. Saya siap melanjutkan."

5. Proceed with the user's request, now fully informed by historical context.