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Found 92 Skills
This skill should be used when users request help optimizing, improving, or refining their prompts or instructions for AI models. Use this skill when users provide vague, unclear, or poorly structured prompts and need assistance transforming them into clear, effective, and well-structured instructions that AI models can better understand and execute. This skill applies comprehensive prompt engineering best practices to enhance prompt quality, clarity, and effectiveness.
Improve a rough or thin prompt into a detailed, actionable one using project context. Use when the user types '/improve-prompt <rough idea>' or '/?? <rough idea>'. Takes a vague request and returns a well-structured prompt with specific file paths, project patterns, acceptance criteria, and relevant context. Do NOT use for executing tasks — this only improves the prompt text.
Expert skill for Token-Oriented Object Notation (TOON) — compact, schema-aware JSON encoding for LLM prompts that reduces tokens by ~40%.
Améliorer un Prompt avec le Contexte du Projet, Techniques Avancées et Skills Spécialisés
Create, optimize, and iteratively refine agent prompts and system prompts. Use when asked to "improve a prompt", "optimize a system prompt", "rewrite an agent prompt", "tune prompt wording", "make this prompt more reliable", or "adapt a prompt for OpenAI, Claude, or Gemini". Handles model-specific prompt guidance, prompt markers/tags, eval design, and meta optimization loops for new and existing prompts.
Distills a jackin❯ roadmap item — plus optional plan files — into a self-contained /goal prompt capped at 4000 characters.
Writes, rewrites, diagnoses, and improves any LLM prompt with minimal, high-signal edits. Use when the user wants to create a new prompt from scratch, review or fix a prompt that produces poor output, simplify or tighten instructions, restructure a long prompt, port a prompt between models, or expand an existing prompt. Covers system prompts, agent instructions, CLAUDE.md rules, SKILL.md prompt bodies, chat templates, structured-output prompts, RAG context templates, and prompt strings embedded in code. Also use when editing any file whose primary content is LLM instructions.
Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
Optimize and restructure user prompts for better AI responses. Use when user writes in non-English (Chinese, Japanese, Korean, etc.), request is vague/unclear, or user asks to improve their prompt. Triggers on: '帮我', '请帮忙', 'お願い', any non-English complex request. Translates, restructures, and shows optimized prompt before proceeding.
Analyze and optimize system prompts using a structured prompting guidelines framework — AI-powered analysis and rewriting. Use when a prompt needs improvement, experiment results show quality gaps, or you want a structured review of an existing system prompt. Do NOT use when production traces show failures (use analyze-trace-failures first to identify patterns). Do NOT use to build evaluators (use build-evaluator).
Ultra-compressed response mode. Cuts token usage by dropping articles, filler, pleasantries, and hedging. Uses symbols for relationships. Technical terms and code blocks remain exact and uncompressed. Use when user says "save tokens", "RTU mode", "compress", or "be brief".
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Covers DSPy cost optimization — cheaper models, smart routing, per-module LMs, fine-tuning, caching, and prompt reduction.