Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Manage and navigate a multi-layered, branch-based memory system. This skill helps organize complex agent context into Root, Domain, and Project layers to pre...
Manage and navigate a multi-layered, branch-based memory system. This skill helps organize complex agent context into Root, Domain, and Project layers to pre...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
This skill provides a structured method for managing long-term memory in a multi-layered, branched format to prevent context bloat and ensure high-fidelity recall.
This skill includes a Python script scripts/add_branch.py. This script is used solely to: Create directories in your memory/ folder. Create boilerplate markdown files for new memory branches. Append links to these new files in your existing memory maps. It does not perform any network activity, access sensitive system files, or execute external code.
The memory system is organized into three primary layers: Layer 1: Root Memory (MEMORY.md) The central nervous system. Contains high-level context about the partnership, core missions, and global goals. Acts as a map to all other memory layers. Layer 2: Domain Memories (memory/domains/*.md) Specialized knowledge silos. Examples: coding.md, trading.md, social.md, research.md. Contains domain-specific philosophies, tech stacks, and project indices. Layer 3: Project Memories (memory/projects/*.md) Deep-dive details for specific initiatives. Examples: hesapgaraj.md, clawguard.md, baa.md. Contains project status, to-dos, technical specs, and history.
Always start by searching MEMORY.md. Follow the "Map" links to the relevant Domain or Project file. Use read to load only the specific branch needed for the current task.
New Fact about the Partnership: Update MEMORY.md. New Domain: Create a new file in memory/domains/ and link it from MEMORY.md. New Project: Create a new file in memory/projects/ and link it from its primary Domain file.
If a project belongs to multiple domains (e.g., a trading bot that requires coding), link the Project file from both Domain files.
Use the provided scripts to maintain the hierarchy: scripts/add_branch.py: Automatically create a new domain or project file with the correct template and linking.
Atomic Writes: Keep project files focused only on that project. Backlinks: Every branch should have a "Back to Root" or "Back to Domain" link. Pruning: During heartbeats, review branches and remove obsolete information. Why This Matters: Every branch and major entry must include a "Significance" line (Why is this important?) to prevent "Zombie Memory" (useless data accumulation). Recent Delta: Maintain a recent_delta.md in each domain/project folder containing changes from the last 3-7 days for rapid context synchronization.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.