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Found 36 Skills
Token optimization best practices for cost-effective Claude Code usage. Automatically applies efficient file reading, command execution, and output handling strategies. Includes model selection guidance (Opus for learning, Sonnet for development/debugging). Prefers bash commands over reading files.
Compressed communication using symbols and abbreviations. Use when context is limited or brevity is needed.
Creates or updates professional-grade agent skills (SKILL.md + optional scripts/references/assets) with strict validation and an iterative generator↔critic workflow. Use when: you want a new skill, want to refactor an existing skill for clarity/token-efficiency, or want to reach an A-grade rubric score.
Use when users say "create a skill", "make a new skill", "build a skill", "skill for X", "package this as a skill", or when refactoring/updating/auditing existing skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Template-based AI prompt engine with YAML templates, brand kit injection, input sanitization for security, and token-efficient context blocks.
Search Tool Hierarchy
Use RepoPrompt CLI for token-efficient codebase exploration
Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.
Choose the right search tool for each query type
Optimize agent skills to reduce context bloat while preserving answer coverage. Use when: (1) A skill's SKILL.md body exceeds ~250 lines or duplicates its references/ files (2) A skill's YAML description is verbose or triggers false positives from sibling skills (3) Planning or executing a body/reference split for a skill (4) Auditing skill token efficiency
Prompt engineering and optimization for AI/LLMs. Capabilities: transform unclear prompts, reduce token usage, improve structure, add constraints, optimize for specific models, backward-compatible rewrites. Actions: improve, enhance, optimize, refactor, compress prompts. Keywords: prompt engineering, prompt optimization, token efficiency, LLM prompt, AI prompt, clarity, structure, system prompt, user prompt, few-shot, chain-of-thought, instruction tuning, prompt compression, token reduction, prompt rewrite, semantic preservation. Use when: improving unclear prompts, reducing token consumption, optimizing LLM outputs, restructuring verbose requests, creating system prompts, enhancing prompt clarity.