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Found 54 Skills
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of managing token budgets and session longevity.
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of extending context beyond the window via filesystem strategies.
Use when context is growing large (50k+ tokens), performance is degrading, instructions are being ignored mid-conversation, or planning multi-agent workflows. Triggers on "lost context", forgotten instructions, or sessions exceeding 30 minutes.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
[Tooling & Meta] Compress conversation context to optimize tokens
Audit installed skills across project, global, and plugin levels. Lists skills with line counts, identifies improvement opportunities (conciseness, clarity, overlap, token waste). Use when reviewing skill quality, finding bloated skills, or optimizing token budgets.
Scoped CLAUDE.md memory system that reduces context token spend. Creates directory-level context files, tracks savings via dashboard, and routes agents to the right sub-context.
Install, initialize, verify, and troubleshoot RTK (Rust Token Killer) for AI coding agents. Use when you need to reduce shell-command token output, confirm that the correct `rtk` binary is installed, choose between Homebrew, install.sh, or Cargo installation, wire `rtk init` for Claude Code, Codex, Gemini CLI, Cursor, Copilot, Windsurf, Cline, or OpenCode, or use compact wrappers such as `rtk git status`, `rtk read`, `rtk grep`, `rtk test`, `rtk lint`, and `rtk gain`. Triggers on: rtk, rust token killer, token saver cli, rtk init, rtk gain, codex rtk, gemini rtk, opencode rtk, claude hook token reduction.
Diagnose the health of a nao context at any stage of its lifecycle. Use when the user wants a structured review of what's been synced, how RULES.md compares to the target structure, whether every table is documented, whether the data model is MECE, whether tests exist and what their failures reveal, and whether context files are bloated. Outputs a structured audit report with ranked recommendations. Do not use for first-time setup (setup-context) or routine rule writing (write-context-rules).
Ultra-compressed communication mode (lite / full / ultra) that cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Use when the user requests "caveman mode", "less tokens", "be brief", or when output budget is tight.
Context compression and summarization methodology. Techniques for reducing token usage while preserving decision-critical information.
Browser automation workflows with Playwright MCP integration