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Design and build an OpenMAIC lesson series using a spiral curriculum: core ideas return with deeper explanations, new representations, and unfamiliar applications. It maps conceptual growth across lessons and checks that revisits add understanding.
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Design subject lessons that also teach students how to learn. Add recall practice, explanations in their own words, predictions, and reflection to help learners check their understanding and plan later review while studying the main concepts.
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Use SAELens to train and analyze sparse autoencoders, which separate neural network activity into features that may represent recognizable concepts. Explore pretrained features, evaluate reconstruction quality, and investigate how individual features influence language model outputs.
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Trace how one idea developed across stored notes and records. Identify its earliest documented mention, clearest expression, changes of stance, contradictions, abandoned branches, and current form, with dated evidence and explicit gaps where sources are missing.
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Route articles, meeting notes, videos, documents, and conversations to specialized workflows for saving in GBrain. Preserve sources, add citations, and update related people, companies, concepts, and timelines with links back to the saved material.
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Write and refactor C++20/23 applications, addressing memory management, concurrency, and performance bottlenecks. Produce interfaces, implementations, tests, and relevant CMake updates, then check runtime errors and measure performance before applying targeted optimizations.
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Turn implementation plans, pull request changes, and technical concepts into HTML visual explanations with Plannotator styling. Present diagrams, mockups, and review priorities through an annotation interface, with approval controls for plans and proposals.
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Analyze an article to identify where illustrations would help, plan and generate images for concepts, processes, comparisons, or scenes, and insert them into the relevant paragraphs of a Markdown document.
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Add illustrations to long articles by identifying concepts, steps, and comparisons that benefit from visuals. The skill plans and generates suitable images, then inserts them into the relevant sections of a Markdown article.
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Write or modernize C++ with guidance on C++20, C++23, and C++26 features. Review memory ownership, error handling, and template constraints, with recommendations for compiler protections and runtime checks that detect bugs.
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Create websites, dashboards, games, and app interfaces from image-generated design concepts. The skill turns the accepted concept into reusable UI components, implements working controls, and checks browser rendering, interactions, and responsive layouts against the design.
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Explain code, design concepts, and system behavior at a chosen level of detail. Get a brief overview or a deeper walkthrough covering execution flow, design choices, alternatives, usage examples, and related code.
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Understand SenseNova Team Harness, a workspace you host yourself for collaboration between people and local AI agents. Learn its core concepts, startup requirements, and workflow for assigning tasks, sharing input files, and reviewing versioned deliverables.
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Create grounded paid-media creative concepts, copy angles, refresh hypotheses, and measurable test briefs across advertising platforms. Use for ad copy, creative briefs, UGC concepts, headlines, creative fatigue, asset ideas, or a creative refresh.
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Build several genuinely different solutions to the same problem at once, spread across what the user does rather than how it looks. Use when one direction is on the table and the team is about to refine it by default. For choosing between the concepts afterwards, use `concept-selection`.
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Design game concepts from a novel. From SOURCE_BIBLE and PRODUCT_BRIEF, compare meaningful alternatives on the dimensions still unlocked, then select a playable direction using hard vetoes and explicit trade-offs. Use for what game should this novel become, compare game concepts, choose a game direction for this book, decide whether this novel should become an interactive story. 小说游戏概念设计。根据 SOURCE_BIBLE 与 PRODUCT_BRIEF 比较未锁定维度上的有效替代方案，用硬否决和关键取舍选择可玩方向。用于判断小说适合做成什么游戏、该不该做成互动叙事、比较游戏方案等需求。
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Design a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.
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Clarify domain concepts, language, boundaries, and durable architectural decisions. Use when terminology is ambiguous, the user is shaping a domain model, or a design depends on resolving what concepts mean.
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AI creative director with recursive self-assessment. Generates concepts using world-class methodologies (SIT, TRIZ, Lateral Thinking, bisociation), scores against 6 weighted criteria with Cannes/D&AD/HumanKind calibration, and recursively refines until the 9+ threshold is reached. Accepts briefs in any format — text, voice transcript, PDF, or raw notes. Use when the user asks to generate creative concepts, brainstorm campaign ideas, develop a Big Idea or campaign platform, evaluate or critique existing creative work, find consumer insights, or shares a brief for ideation — including activations, PR-stunts, brand utility, experiential, and non-advertising ideas. Calibrates against a library of 569 legendary campaigns (P01-P18 pattern map) to detect saturation and ensure originality. Do not use for media planning, production budgeting, brand identity/logo design, copywriting final drafts, or market research data collection.
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Design a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.
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Use when you have a finalized brainstorm-beagle spec at `.beagle/concepts/<slug>/spec.md` and need a bite-sized, TDD-driven implementation plan before any code is written. Triggers on: "write a plan", "plan this spec", "turn the spec into a plan", "now plan the implementation", "write-plan". Reads the spec, designs the file structure, decomposes work into 2-5 minute TDD steps with exact paths and commands, self-reviews against the spec, gets user approval, then writes to `.beagle/concepts/<slug>/plan.md` and offers to generate an execution handoff prompt via the subagent-prompt skill. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.
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Use when you need a bite-sized, TDD-driven implementation plan but do NOT have a brainstorm-beagle spec to plan against. quick-plan reconstructs intent from the current conversation, fans out domain-expert exploration subagents across the codebase, and synthesizes the same plan format write-plan produces — without requiring `.beagle/concepts/<slug>/spec.md`. Triggers on: "quick plan", "plan this out", "plan what we just discussed", "turn this into an implementation plan", "plan this without a spec", "I don't have a spec, just plan it", "write-plan but no spec". Make sure to use this skill whenever the user wants an implementation or TDD plan and there is no spec to plan against — even if they just say "plan it" after discussing a feature. Writes to `.beagle/plans/<slug>/plan.md`. If a finalized spec already exists at `.beagle/concepts/<slug>/spec.md`, prefer write-plan. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.
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Create polished, code-driven Remotion motion explainers from scripts, narration, subtitles, product specifications, reference videos, UI recordings, diagrams, or abstract concepts. Use for product launches, AI demonstrations, UI interactions, algorithms, scientific principles, electrical systems, data flows, architecture, charts, equations, and concepts best expressed with React, SVG, Canvas, WebGL, or Three.js. Prioritize continuous spatial logic, visible cause and effect, precise interaction states, editable parameters, deterministic rendering, and evidence-backed visual QA.
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Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.
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