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Found 422 Skills
Execute AI image generation with optimal quality. Use when you need to generate images via Replicate API. Triggers on: generate image, create visual, product shot. Outputs generated images for feedback and iteration.
Build, validate, and deploy LLM-as-Judge evaluators for automated quality assessment of LLM pipeline outputs. Use this skill whenever the user wants to: create an automated evaluator for subjective or nuanced failure modes, write a judge prompt for Pass/Fail assessment, split labeled data for judge development, measure judge alignment (TPR/TNR), estimate true success rates with bias correction, or set up CI evaluation pipelines. Also trigger when the user mentions "judge prompt", "automated eval", "LLM evaluator", "grading prompt", "alignment metrics", "true positive rate", or wants to move from manual trace review to automated evaluation. This skill covers the full lifecycle: prompt design → data splitting → iterative refinement → success rate estimation.
Generates Nano Banana Pro prompts for 4-panel engineer humor comics. Use when user mentions "漫画作成", "エンジニア漫画", "4コマ", or "あるある".
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.
Use when improving general prompts for structure, examples, and constraints.
Enhanced reasoning patterns via slash commands (/think, /verify, /adversarial, /edge, /compare, /confidence, /budget, /constrain, /json, /flip, /assumptions, /tensions, /analyze, /trade) or natural language ("argue against", "what could break", "show reasoning", "deep review", "meta-prompts", "thinking modes", "second-best approach", "list assumptions", "opposing perspectives").
Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.
Generate high-divergence, out-of-the-box analysis plans and prompts that counter anchoring, mode collapse, and context bias while staying practical. Use when requests ask for unconventional strategies, non-obvious options, radical reframing, MCP-assisted synthesis across prior messages/sources, or "think differently" outputs that still require executable next steps.
Generate images using Google's Gemini API — hero backgrounds, OG images, placeholder photos, textures, and style-matched variants. Uses free-tier models for drafts, paid for finals. No dependencies beyond Python 3. Trigger with 'generate image', 'gemini image', 'make a hero background', 'create placeholder photo', 'generate OG image', 'AI image', or 'need an image for'.
This skill guides creating autonomous agents for Claude Code plugins using markdown files with YAML frontmatter. Use when building new agents, designing agent system prompts, or configuring agent behavior.
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.
LLM app development with RAG, prompt engineering, vector databases, and AI agents