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Found 79 Skills
Token optimization best practices for MCP server and tool interactions. Minimizes token consumption while maintaining effectiveness. USE WHEN: user mentions "token usage", "optimize tokens", "reduce API calls", "MCP efficiency", asks about "how to use less tokens", "MCP best practices", "limit output size", "efficient queries" DO NOT USE FOR: Code optimization - use `performance` instead, Text compression - this is about API usage patterns, Cost optimization (infrastructure) - use cloud/DevOps skills
Use when you need to reduce token usage, lower API cost, fit work within a context window, or speed up an expensive/slow agent loop. Triggers on phrases like "optimize tokens", "reduce token usage", "save on cost", "fit in context", "context too long", "running out of tokens", "prompt caching", "compact the context", "agent is too expensive", "shrink the prompt", "cheaper model", "tokens-per-task". Provides a layered tactic catalog — measurement (heuristic counting and budget allocation), prompt-side levers (caching, system-prompt diet, tool-definition pruning), context management (compression, observation masking, file-system offload), agent-loop patterns (parallel tool calls, batch operations, model routing by complexity), and output-side controls (depth tiers, stop sequences, structured output). Project-agnostic and provider-agnostic; works across Claude Code, Codex CLI, Cursor, Gemini CLI, and Copilot.
Optimize token consumption in Claude Code through cache management, context forking, model selection, and input filtering. Use when user mentions high token costs, context window bloat, slow sessions, cache invalidation, verbose agent output, heavy MCP load, or any context management issues. Covers four critical axes - cache misses, context bloating, wrong model/effort level, and verbose input formats. Triggers on "tokens", "cost", "expensive", "context", "cache", "slow session", "optimize", or symptoms of token waste even without explicit mention.
Optimize token usage when delegating to Gemini CLI. Covers token caching, batch queries, model selection (Flash vs Pro), and cost tracking. Use when planning bulk Gemini operations.
Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman-compress <filepath> or "compress memory file"
Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman.
Modo cavernícola en español. Corta ~75% de tokens hablando como cavernícola técnico. Misma precisión técnica, menos palabrería. Niveles: lite, full (default), ultra. Usar cuando el usuario diga "modo cavernícola", "habla como cavernícola", "menos tokens", "sé breve", o invoque /caveman-es.
Find the ghost tokens. Audit Claude Code setup, see where 25-38% of your context goes, fix it. Use when context feels tight.
Triage and categorize GitHub issues with priority labels. Use when user says "triage issues", "check issues", "review open issues", or during regular maintenance of GitHub issue backlog.
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
Provides MoAI-ADK foundational principles including TRUST 5 quality framework, SPEC-First DDD methodology, delegation patterns, progressive disclosure, and agent catalog reference. Use when referencing TRUST 5 gates, SPEC workflow, EARS format, DDD methodology, agent delegation patterns, or MoAI orchestration rules. Do NOT use for context and token management (use moai-foundation-context instead) or strategic analysis (use moai-foundation-philosopher instead).
Clear conversation context while preserving knowledge via context marker. Use when user says "clear context", "start fresh", "done with this task", or when approaching token limits.