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Found 422 Skills
Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
Build local-first executive assistant workflows with OpenClaw for data intake, operational memory, and communications triage
Use when the user wants to iterate on a viral-article scoring system itself, calibrate or improve a scoring prompt against labeled samples, or run batch scoring experiments on a fixed article set. Best for prompt-only scoring research where the evaluator scripts stay fixed and only the scoring rubric/prompt is meant to evolve.
Build high-quality /goal commands for OpenAI Codex CLI 0.128+ that maximize audit-friendliness and minimize false-completion. Use this skill whenever the user wants to write, draft, generate, improve, or refine a /goal prompt — even if they don't say "skill" — including phrases like "help me write a goal", "design a goal for X", "review my goal command", "make a goal for this repo", or any request involving long-running Codex tasks. Also trigger when the user mentions Ralph loop, persistent agent objectives, or asks Codex to "keep working until done". Produces a complete, copy-pasteable /goal command using the 5-section golden template (Objective/Scope/Constraints/Done when/Stop if), supports three interaction modes (step-by-step, full-description, hybrid), auto-detects project type (Node/Python/Swift/Go/Rust/static) by inspecting filesystem or repo URL, reads AGENTS.md/CLAUDE.md if present, and predicts audit-friendliness before output.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Build full-stack web applications powered by Google Gemini's Nano Banana & Nano Banana Pro image generation APIs. Use when creating Next.js image generation apps, text-to-image tools, or iterative image editors.
Provides comprehensive guidance for Midjourney AI image generation including prompt engineering, image generation, parameters, and best practices. Use when the user asks about Midjourney, needs to generate AI images, create prompts, or work with Midjourney features.
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".
Create effective debugging prompts—include error messages, stack traces, expected vs actual behavior, logs, and attempted solutions
Cognitive Scaffolding structures an agent's context window using principles from cognitive science — primacy effects, recency bias, chunking, and attention allocation.
GPT Image 2 prompt gallery, agentic skill, and CLI for OpenAI image generation and editing with curated prompts and reference workflows