Loading...
Loading...
Found 51 Skills
Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies"
Converts MCP servers to Claude Skills to save tokens. Runs the introspection tool to generate skill wrappers.
Create production-ready skills from expert knowledge. Extracts domain expertise and system ontologies, uses scripts for deterministic work, loads knowledge progressively. Use when building skills that must work reliably in production.
Build a persistent knowledge graph of your codebase so Claude reads only what matters — up to 49x fewer tokens on coding tasks.
Audit your Claude Code setup for token waste and context bloat. Use when the user says "audit my context", "check my settings", "why is Claude so slow", "token optimization", "context audit", or runs /context-audit. Starts by running /context to see real overhead, then audits MCP servers, CLAUDE.md rules, skills, settings, and file permissions. Returns a health score with specific fixes.
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.
Expert integration patterns for Claude API and TypeScript SDK covering Messages API, streaming responses, tool use, error handling, token optimization, and production-ready implementations for building AI-powered applications
Context compression and summarization methodology. Techniques for reducing token usage while preserving decision-critical information.
[Tooling & Meta] Compress conversation context to optimize tokens
Claude Code skill that makes AI agents respond in caveman-speak, cutting ~65-75% of output tokens while preserving full technical accuracy
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.
Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)