Tibsfox

Design heuristics for immersive environments — VR worlds, AR scenes, CAVE installations, responsive rooms. Covers scale and presence, lighting and atmosphere, sight-line composition, acoustic design, comfort-first layout, player/visitor flow, environmental storytelling, and the boundary between stage and audience. Includes Krueger's VIDEOPLACE heritage, Furness's Super Cockpit discipline, and cont

OpenStack Swift object storage service. Provides S3-compatible distributed object storage with containers, ACLs, ring architecture, eventual consistency, large object support (SLO/DLO), tempURL for unauthenticated access, object versioning, and object expiry. Use for deploying, configuring, operating, and troubleshooting OpenStack object storage.

Data collection, analysis, and interpretation for scientific inquiry. Covers measurement and units, data recording and organization, descriptive and inferential statistics, graphical representation, error analysis, drawing valid conclusions from evidence, and recognizing the limits of data. Use when collecting, analyzing, visualizing, or interpreting scientific data at any level.

Spectroscopy, chromatography, titration, elemental analysis, X-ray crystallography, and mass spectrometry for chemical identification and quantitation. Covers UV-Vis and IR spectroscopy, NMR fundamentals, gas and liquid chromatography, gravimetric and volumetric analysis, diffraction methods, separation techniques, and quantitative error analysis. Use when identifying unknown substances, determini

Fundamental movement skills and motor learning for physical education. Covers the three movement families (locomotor, non-locomotor, manipulative), the stage theory of motor learning (cognitive, associative, autonomous), developmental coordination milestones, and the teaching progression from gross to fine motor control. Use when designing introductory PE lessons, assessing motor competence, diagn

ALU execution pattern for ephemeral autonomous work. Detects hooked work, enters GUPP autonomous mode, executes the assigned bead, commits and pushes results, then self-terminates. Never coordinates other agents.

Distributed storage systems design and operation for cloud platforms. Covers the GFS/HDFS block-and-master pattern, object storage (Swift/S3) with consistent hashing and eventual consistency, block storage semantics, replication vs erasure coding, the CAP theorem in practice, read-repair and anti-entropy, snapshot chains, and the GFS/BigTable/Spanner evolution. Use when designing a storage subsyst

Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among met

Pipeline retirement protocol for completed work items. Implements a 7-stage validate-commit-push-submit-notify-cleanup-terminate pipeline with irreversibility after push. Done means gone.

Domain knowledge for 10 applied subjects: computer science, data science, world languages, psychology, environmental science, nutrition, economics, creative writing, logic, digital literacy. Use when generating Applied & Practical tier pack content.

Security hygiene for GSD's self-modifying skill and agent system. Use this skill whenever: creating, editing, or deleting skill files (.claude/skills/, .claude/commands/), modifying agent definitions (.claude/agents/), working with YAML configuration or chipset files, handling JSONL observation data (.planning/patterns/), processing community-contributed skills or chipsets, any file path operation

Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable.