Academic

Multi-conversation methodology for iterative stateful work with context accumulation. Use when users request work that spans multiple sessions (research, debugging, refactoring, feature development), need to build on past progress, explicitly mention iterative work, work logs, project knowledge, or cross-conversation learning.

Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.

Apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification. Use for complex problem decomposition, adaptive planning, analysis needing course correction, problems with unclear scope, multi-step solutions, and hypothesis-driven work.

Writes and edits content in Ben Swift's distinctive voice for any type of writing including blog posts, emails, technical documentation, and academic content. Use when the user wants writing in Ben's voice or style.

Persistent memory management for Claude Code via AutoMem. Use this skill when: - Starting a session (recall project context, decisions, patterns) - Making architectural decisions or library choices - Fixing bugs (store root cause and solution) - Learning user preferences or code style - Completing significant work (store summary) - Debugging issues (search for similar past problems)

Curate and validate BibTeX bibliographies against academic databases.

Automated machine learning with hyperparameter optimization using Optuna, Hyperopt, or AutoML libraries. Activates for "automl", "hyperparameter tuning", "optimize hyperparameters", "auto tune model", "neural architecture search", "automated ml". Systematically explores model and hyperparameter spaces, tracks all experiments, and finds optimal configurations with minimal manual intervention.

Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

Expert React video scene component creator for educational content. Builds production-grade, visually distinctive components using framer-motion animations, pixel-precise positioning, and optimized performance patterns. Follows strict component format with React.memo, threshold-based state updates, and module-level definitions. Outputs self-contained TSX components with proper timing sync, 60fps performance, and comprehensive reference-based implementation.

Master 26 documented prompt engineering principles for crafting effective LLM prompts with 400%+ quality improvement. Includes templates, anti-patterns, and quality checklists for technical, learning, creative, and research tasks. Use when writing prompts for LLMs, improving AI response quality, training on prompting, designing agent instructions, or when user mentions 'prompt engineering', 'better prompts', 'LLM quality', 'prompt templates', 'AI prompts', 'prompt principles', or 'prompt optimization'.

Research external open-source repositories to learn patterns and implementations. This skill should be used when agents need to study external codebases, check for existing research before cloning, manage temporary workspaces, and persist findings to the global thoughts system. Triggers include: studying external repos, learning from open source, cloning for pattern research, or checking what has been researched.

Guidance for writing SPARQL queries against RDF/Turtle datasets, particularly for university or academic data. This skill should be used when tasks involve querying RDF data with SPARQL, filtering entities based on multiple criteria, aggregating results, or working with Turtle (.ttl) files.

Develop research-backed AI competitive strategies combining academic research, market trends, and social sentiment analysis

Guidance for writing and verifying SPARQL queries against RDF datasets, particularly university/academic ontologies. This skill should be used when tasks involve querying RDF data with SPARQL, working with academic datasets (students, professors, departments, courses), or performing complex graph pattern matching with filters and aggregations.

Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis.

Use when analyzing YouTube videos, extracting insights from tutorials, researching video content, or learning from talks and presentations

Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.

Master the know CLI tool for managing specification graphs. Use when working with spec-graph.json, understanding graph structure, querying entities/references/meta, managing dependencies, or learning graph architecture. Teaches dependency rules, entity types, and graph operations.

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.

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.