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Found 450 Skills
Ultra-lightweight channel for feature workflows: No need to write design docs, checklists, or conduct phased reviews. Let AI write code directly as it normally would, but before it starts, tell it where the CodeStable knowledge base in the project is and how to search it. This way, the code it writes will have fewer pitfalls and be more consistent with project conventions. Trigger scenarios: Users say "fast mode", "fastforward", "skip all those steps", "just start coding", "help me make xxx" and the requirement is too small to go through the design process.
Phase 3 of the feature workflow – Complete the acceptance closed-loop. Four tasks: 1. Check layer by layer against {slug}-design.md to verify if the implementation deviates from the plan; fix any deviations on the spot instead of just "noting them" in the report. 2. Incorporate this feature into the project's overall architecture documentation. 3. If this feature changes the user story or boundaries of the corresponding requirement, update the requirement doc accordingly. 4. If this feature originated from a roadmap item, change the status of the corresponding entry in roadmap items.yaml to done and sync it with the main document. Finally, produce a {slug}-acceptance.md as the closed-loop proof for the entire workflow. Prerequisite: cs-feat-impl is completed. Trigger scenarios: User says "The feature is done, let's accept it", "Do the final check", "Prepare for merge", "Generate the acceptance report".
Generates blog post thumbnail images for Orbitant following the brand's visual identity, using Google's Imagen API (Nano Banana 2). Activates when creating blog images, generating thumbnails, designing featured images for articles, or when someone needs a visual for an Orbitant insight/blog post. Use this skill even if the user just says "I need an image for this article", "create a thumbnail", "generate a hero image", or "make a featured image". Also triggers when the user mentions "Nano Banana 2", "image generation", or asks for a prompt for an AI image tool.
Analyze a Materialize environment for health, performance, and optimization opportunities using the MCP Developer endpoint. Use this skill when someone wants to check environment health, investigate performance issues, troubleshoot stale materialized views, diagnose memory pressure, audit resource utilization, or get optimization recommendations. Trigger this even if the user just says "check my environment", "why is my MV stale", "why is my cluster slow", or "what can I optimize".
The meta-skill that powers all other AI tools. Prompt engineering for creative applications is the art and science of communicating with AI models to produce exactly what you envision—in images, video, audio, and text. This isn't just "write better prompts." It's understanding how different models interpret language, how to structure requests for different modalities, how to iterate systematically, and how to build prompt libraries that encode your creative vision. The best prompt engineers have developed intuition for what words trigger what responses in each model. This skill is foundational—it amplifies the effectiveness of every other AI creative skill. Master this, and you master the interface to all AI creation. Use when "prompt, prompting, prompt engineering, better prompts, prompt optimization, how to prompt, prompt strategy, prompt library, prompt template, make AI understand, prompt-engineering, prompting, meta-skill, ai-creative, foundational, optimization, iteration" mentioned.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
World-class color theory expertise combining the scientific precision of Josef Albers' "Interaction of Color," the systematic thinking of color systems from Pantone and RAL, and the perceptual psychology insights from researchers like Bevil Conway. Color is not just aesthetics - it's communication, emotion, and usability compressed into wavelengths. Great color work is invisible when done right. Users don't notice "nice colors" - they notice when they can't read text, when buttons don't look clickable, when errors don't feel urgent, or when the interface feels "off" without knowing why. Color theory is the science of making the right thing feel obvious. Use when "color theory, color palette, color scheme, color harmony, complementary colors, analogous colors, contrast ratio, dark mode colors, light mode, color tokens, semantic colors, color accessibility, color blindness, color psychology, color system, brand colors, data visualization colors, color, design, accessibility, contrast, dark-mode, theming, tokens, wcag, palette, harmony" mentioned.
Unvarnished technical criticism combining Linus Torvalds' precision, Gordon Ramsay's standards, and James Bach's BS-detection. Use when code/tests need harsh reality checks, certification schemes smell fishy, or technical decisions lack rigor. No sugar-coating, just surgical truth about what's broken and why.
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.
Evaluates student code submissions based on conceptual mastery rather than just correctness. Use to provide high-quality educational feedback on architectural patterns and programming logic.
Track which optimization experiment was best. Use when you've run multiple optimization passes, need to compare experiments, want to reproduce past results, need to pick the best prompt configuration, track experiment costs, manage optimization artifacts, decide which optimized program to deploy, or justify your choice to stakeholders. Covers experiment logging, comparison, and promotion to production.
Help users create custom batch image generation Skills through interactive Q&A. Users don't need to write code; they can generate fully functional image generation Skills just by answering questions. Triggered when users say "Help me create an image generation Skill", "I want to make an image matching Skill", "Create a batch image generation Skill", "How to make an image generation Skill", or "Help me make an AI image generation Skill". Supports any image scenarios such as article illustrations, Logo design, storyboards, social media images, posters, etc.