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Found 69 Skills
Optimizes text, prompts, and documentation for LLM token efficiency. Applies 41 research-backed rules across 6 categories: Claude behavior, token efficiency, structure, reference integrity, perception, and LLM comprehension. Use when optimizing prompts, reducing tokens, compressing verbose docs, or improving LLM instruction quality.
Enterprise session state management, token budget optimization, runtime tracking, session handoff protocols, context continuity for Claude Sonnet 4.5 and Haiku 4.5 with context awareness features
Audit and optimize OpenClaw API costs. Applies six proven optimizations — model routing, prompt caching, lean context, local heartbeats, rate limits, and workspace trimming — to cut monthly spend by up to 90%. Use when asked to reduce costs, optimize tokens, audit API spend, or configure cost-saving settings.
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.
Advanced search options in GrepAI. Use this skill for JSON output, compact mode, and AI agent integration.
Document debugging sessions with hypothesis tracking and knowledge base
Manage ECC skill loading — defer unused skills to save init tokens, restore on demand. Use when user wants to check, defer, or restore ECC skills.
Transform and format media content creatively. Applies stylization and formatting transformations to various media types.
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)
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.
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.
Workflow for repository reconnaissance and operations using GitHub CLI (gh). Optimizes token usage by using structured API queries instead of blind file fetching.