Dados e Análise

Educational pack for holomorphic dynamics, complex iteration, fractal geometry, and data-driven dynamics (DMD/Koopman). Use this skill when the user asks about: complex dynamics, iteration on the complex plane, Julia sets, Mandelbrot sets, fixed points and stability, period doubling, bifurcation, topology of the complex plane, DMD (Dynamic Mode Decomposition), Koopman operator theory, skill dynami

Grammar of graphics, chart type selection, color theory, interactive visualization, dashboards, and the principles of honest, effective data display. Covers Tufte's data-ink ratio, Bertin's visual variables, perceptual principles, accessibility, small multiples, annotation, and the full workflow from exploratory plots to publication-quality graphics. Use when creating charts, designing dashboards,

Run memory maintenance — decay old memories, compress stale content, consolidate episodic memories into semantic knowledge, and run sleep-like replay. Use when the user says 'clean up memories', 'consolidate', 'run maintenance', 'compress old memories', 'memory cleanup', or periodically to keep the memory system healthy. Also use after importing many memories or at the end of a long session.

Store important decisions, patterns, errors, lessons, and context into Cortex persistent memory. Use when the user says 'remember this', 'save this', 'store this for later', 'note this down', 'don't forget', 'this is important', 'bookmark this', or when a significant decision, bug fix, architecture choice, or lesson learned occurs during a session. Also use after resolving tricky bugs, making tech

Retrospective facilitation and organizational learning for project teams. Covers retrospective formats (Start/Stop/Continue, 4Ls, timeline, sailboat), blameless postmortems, lessons learned databases, knowledge management, organizational learning theory (Senge's Fifth Discipline, Argyris's double-loop learning), after-action reviews (military AAR heritage), GSD's session-to-session retrospective p

Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations,

Drawing conclusions about populations from sample data. Covers sampling distributions, confidence intervals, hypothesis testing (z-tests, t-tests, chi-squared tests, ANOVA), p-values, significance levels, power, Type I and Type II errors, effect sizes, and the logic connecting sample statistics to population parameters. Emphasizes the distinction between statistical significance and practical sign

Explore the memory system's state, find gaps in knowledge, assess coverage, and get diagnostic information. Use when the user asks 'what does my memory look like', 'show me memory stats', 'what am I missing', 'how good is my knowledge', 'memory health', 'show coverage', 'find gaps', 'what topics are weak', or when you need to understand the state of stored knowledge before a task.

Project estimation and scheduling techniques for software development. Covers work breakdown structures (WBS), critical path method (CPM), PERT three-point estimation, Gantt charts, resource leveling, Goldratt's critical chain project management (buffer management), story point estimation, planning poker, reference class forecasting (Kahneman/Flyvbjerg), cone of uncertainty, schedule compression (

Load, validate, and use DACP bundles as structured execution context. Detects .bundle/ companion directories, validates integrity (manifest schema, fidelity, provenance), loads typed data, and builds ExecutionContext for receiving agents. Scripts are NEVER auto-executed. Use when processing incoming DACP bundles, interpreting .bundle/ directories alongside .msg files, validating bundle integrity,

Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.

Provides Kubernetes resource management, Helm chart patterns, service mesh configuration, and autoscaling strategies. Covers HPA, VPA, KEDA, operators, security contexts, and namespace isolation. Use when user mentions 'kubernetes', 'k8s', 'helm', 'istio', 'linkerd', 'service mesh', 'HPA', 'VPA', 'KEDA', 'pod security', 'resource quotas', 'operators'.