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Found 28 Skills
First-principles thinking; identify what everything else builds on. Use when a learner feels like they're memorizing rather than understanding, wants to distinguish foundational from derived concepts, or needs to reconstruct knowledge from basics. This skill guides through dependency testing (elimination, reconstruction, abstraction descent) while enforcing human-only reasoning about what's core vs. derived. Triggers on phrases like "I'm memorizing not understanding", "what's really foundational", "I can use it but don't understand why", "distinguish core from derived", or when a learner wants deeper understanding of first principles.
You must use this when seeking cross-domain analogies, applying first-principles reasoning, or overcoming creative bottlenecks.
Generate world-class Instagram carousel content on any topic. Produces 7-10 publication-ready slides (1080x1350) with AI-generated visuals, precise typography, Instagram music recommendations, optimized captions, and hashtags. Uses Aristotelian first-principles framework with 7 content archetypes, 6 hook patterns, a mandatory Bullshit Test quality gate, and a comprehensive design system. Fully generalized -- works for ANY topic. Triggers: instagram carousel, create carousel, carousel post, make carousel slides, instagram slides, carousel content, slide deck for instagram, swipeable post
Generates rich technical documentation pages with dark-mode Mermaid diagrams, source code citations, and first-principles depth. Use when writing documentation, generating wiki pages, creating tech...
Explain and apply foundational math for front-end development, including arithmetic, algebra, ratios, exponents, roots, linear equations, inequalities, geometry, trigonometry, coordinates, and choosing CSS versus JavaScript math. Use when a web design calculation needs first-principles explanation before applying CSS, JavaScript, Grid, Flexbox, responsive, or color-specific skills.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Rebuilds the current implementation from scratch as the simplest design that satisfies what was learned, after the user approves the design. Use when the user explicitly asks to redo the work, start over, scrap the approach, or rewrite it more simply. Never infer from a failing or complex implementation. Not for fitting a new requirement into an existing design; use redesigning-from-first-principles.
Strategic thinking framework integrating First Principles Analysis, Stanford Design Thinking, and MIT Systems Engineering for deeper problem-solving. Use when performing architecture decisions, technology selection trade-offs, root cause analysis, cognitive bias detection, or first principles decomposition. Do NOT use for code quality validation (use moai-foundation-quality instead) or implementation workflows (use moai-workflow-ddd instead).
Deep codebase analysis for building architectural context before vulnerability or bug finding. Uses line-by-line analysis with First Principles, 5 Whys, and 5 Hows. Use when deep comprehension is needed before security auditing, architecture review, or threat modeling.
Use when solution space exploration is complete and you're ready to create an implementation plan. Enforces "simple over easy" - the fundamentally right solution, not the path of least resistance. Triggers after /design-solution, when a solution has been chosen, or when asked to "make a plan" or "create a plan".
Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.
Use when facing complex decisions, architectural trade-offs, philosophical questions, or any problem requiring deep analysis before action. Use when the user asks to "think deeply", "question assumptions", "analyze from first principles", "challenge this decision", debates between two approaches (e.g. monolith vs microservices, build vs buy, SSR vs CSR), or invokes /socrates. Also triggered when other skills need a thinking engine for rigorous pre-analysis. Even if the problem seems simple, if there are hidden assumptions worth examining, this skill applies.