context-optimization
Optimizes context window via MECW principles and memory tiering. Use when context exceeds 30% or before long multi-step tasks.
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Optimizes context window via MECW principles and memory tiering. Use when context exceeds 30% or before long multi-step tasks.
Recovers broken agent state via crash recovery, context overflow, and merge conflict protocols. Use when an agent session fails or a worktree is corrupted.
Delegates tasks to Gemini or Qwen with quota tracking and error handling. Use when tasks exceed context window or need cheaper processing.
Guide creating Claude Code hooks with security-first design. Use for validation and enforcement.
Shapes agent behavior via instruction framing and style transfer. Use when composing dispatch prompts or writing skill instructions for parallel review agents.
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
The agent's persistent long-term memory — the only knowledge that survives across sessions, shared across the fleet under access control. Consult it at the start of a task to recall prior decisions, findings, and rules before acting, and write outcomes, decisions, and lessons as work completes. Use whenever a memclaw_* tool is present, whenever the user refers to past work ("what did we decide", "last time", "earlier"), or whenever any durable fact needs to be stored, recalled, superseded, or shared with the fleet. Do not use it for throwaway within-session scratch state.
Analyze and improve the improvement process. Use for detecting regressions and meta-optimization.
Surface expert frameworks. Use when creating or evaluating skills, hooks, or agents.