Nm Conserve Context Optimization

Analyze and optimize context window usage with MECW principles, memory tiering, session routing, and subagent coordination

This is an instruction-only skill (documentation and patterns) that appears to do what it says: optimize context usage and manage subagents. Before using it: 1) don't blindly run the example commands — the skill references Python modules (conserve, leyline, scripts/agent_memory.py) that are not included; verify those packages or code come from a trusted source. 2) Review and approve any commands that read or grep your home directories (e.g., ~/.claude) or write checkpoints to /tmp or .coordination/agents/, since these touch local files and logs. 3) If you plan to run the suggested tooling, do so in an isolated/sandboxed environment (or a cloned repo/worktree) so you can inspect created files and logs. 4) If you lack a Claude Code runtime or the referenced Python packages, treat the examples as pseudocode rather than runnable commands. These mismatches are likely benign documentation issues but warrant caution—inspect and control the environment before running any referenced scripts.

Type: OpenClaw Skill Name: nm-conserve-context-optimization Version: 1.0.0 The skill bundle is a comprehensive framework for optimizing LLM context usage based on 'Maximum Effective Context Window' (MECW) principles. It provides detailed documentation and utility logic for token analysis, subagent delegation, memory tiering, and session routing to prevent hallucinations and improve efficiency. No malicious behaviors, data exfiltration, or harmful prompt injections were identified; the content is entirely aligned with its stated purpose of enhancing agent performance within the Claude Code environment.