Academic

Evaluate and elevate writing effectiveness through multi-dimensional quality assessment. Analyzes craft, coherence, authority, purpose, and voice with genre-calibrated thresholds. Use for refining drafts, diagnosing quality issues, generating quality content, or teaching writing principles.

Systematic blueprint for reading and annotating academic papers with searchable notes, explicit constraints, and quality gates.

Use when formulating clinical research questions (PICOT framework), evaluating health evidence quality (study design hierarchy, bias assessment, GRADE), prioritizing patient-important outcomes, conducting systematic reviews or meta-analyses, creating evidence summaries for guidelines, assessing regulatory evidence, or when user mentions clinical trials, evidence-based medicine, health research methodology, systematic reviews, research protocols, or study quality assessment.

Use when writing or reviewing career documents including research statements, teaching statements, diversity statements, CVs, or biosketches. Invoke when user mentions research statement, teaching philosophy, diversity statement, biosketch, academic CV, faculty application, or needs help with career narrative, positioning, or professional documents for academic advancement.

Use when content must be translated between audiences with different expertise, context, or goals while preserving accuracy but adapting presentation. Invoke when technical content needs business framing (engineering decisions → executive summary), strategic vision needs tactical translation (board presentation → team OKRs), expert knowledge needs simplification (academic paper → blog post, medical diagnosis → patient explanation), formal content needs casual tone (annual report → social media post), long-form needs summarization (50-page doc → 1-page brief), internal content needs external framing (roadmap → public updates, bug tracking → known issues), cross-cultural adaptation (US idioms → international clarity, Gen Z → Boomer messaging), medium shifts (written report → presentation script, detailed spec → action checklist), or when user mentions "explain to", "reframe for", "translate this for [audience]", "make this more [accessible/formal/technical]", "adapt for [executives/engineers/customers]", "simplify without losing accuracy", or "same content, different audience". Apply to technical communication (code → business value), organizational translation (strategy → execution), education (expert → novice), customer communication (internal → external), cross-cultural messaging, and anywhere same core message needs different presentation for different stakeholders while maintaining correctness.

Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.

Metalearning skill that helps master any topic efficiently by identifying critical 20% material, building expert vocabulary, and creating research-backed learning roadmaps. Auto-trigger when user says "learn [topic]", "help me learn [topic]", "I want to learn [topic]", or asks for guidance on understanding a new subject. Supports comprehensive plans, interactive guidance, or minimalist just-in-time delivery.

Structured reflective problem-solving methodology. Process: decompose, analyze, hypothesize, verify, revise. Capabilities: complex problem decomposition, adaptive planning, course correction, hypothesis verification, multi-step analysis. Actions: decompose, analyze, plan, revise, verify solutions step-by-step. Keywords: sequential thinking, problem decomposition, multi-step analysis, hypothesis verification, adaptive planning, course correction, reflective thinking, step-by-step, thought sequence, dynamic adjustment, unclear scope, complex problem, structured analysis. Use when: decomposing complex problems, planning with revision capability, analyzing unclear scope, verifying hypotheses, needing course correction, solving multi-step problems.

Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.

Guidelines for multi-agent AI and learning projects with lesson-based structures. Activate when working with AI learning projects, experimental directories like .spec/, lessons/ directories, STATUS.md progress tracking, or structured learning curricula with multiple modules or lessons.

Diátaxis documentation framework for organizing content into four categories - tutorials (learning-oriented), how-to guides (problem-solving), reference (technical specifications), and explanation (conceptual understanding). Essential for creating and organizing documentation in docs/ directory.

Create an interactive educational MicroSim using the p5.js JavaScript library with distinct regions for drawing and interactive controls. Each MicroSim is a directory located in the /docs/sims folder. It has a main.html file that references the javascript code and the main.html can be referenced as an iframe from the index.md. The metadata.json contains Dublin core metadata about the MicroSim.

Use when generating, updating, or organizing documentation (component/API docs, project indexes, diagrams, tutorials, learning paths) - provides structured workflows and references for docs generation, indexing, diagrams, and teaching.

Create an educational MicroSim using the vis-network JavaScript library. Each MicroSim is a directory located in the /docs/sims folder. It has a main.html file that can be referenced with an iframe. The main.html file imports the main JavaScript code to run the educational MicroSim.

This skill generates curated, verified reference lists for textbooks with level-appropriate resources (10 for junior-high, 20 for senior-high, 30 for college, 40 for graduate). References are formatted with links, publication details, and relevance descriptions. Use this skill when working with intelligent textbooks that need academic references, either book-level or chapter-level.

Create an interactive educational MicroSim using the p5.js JavaScript library with distinct regions for drawing and interactive controls. Each MicroSim is a directory located in the /docs/sims folder. It has a main.html file that references the javascript code and the main.html can be referenced as an iframe from the index.md. The metadata.json contains Dublin core metadata about the MicroSim.

Create an educational MicroSim using the vis-network JavaScript library. Each MicroSim is a directory located in the /docs/sims folder. It has a main.html file that can be referenced with an iframe. The main.html file imports the main JavaScript code to run the educational MicroSim.

This skill should be used when validating whether a specific word or phrase is appropriate, commonly used, and correct in academic technical contexts. Use for checking terminology in research papers targeting top-tier computer science conferences.

Create educational documents that build understanding progressively with citations

Systematic analysis patterns for categorical AI papers (Gavranović, de Wynter, Bradley, Zhang). Use when analyzing academic papers on categorical deep learning, extracting categorical structures from AI research, mapping meta-prompting concepts to category theory, or building comprehensive literature reviews on categorical approaches to AI.