Results for “seasonal-decompose”

4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

**Monthly Partitions:**

Understand through recursive decomposition and modular reconstruction of simple operations to reproduce emergent complexity. Use this skill whenever reasoning is required. Framework for [[#parse|parsing]], [[#branch|branching]], [[#reduce|reducing]], [[#ground|grounding]] and [[#emit|emitting]]. Employs metacognitive reasoning epistemology, leveraging first principles through recursive decomposition[^1]. Self-referential and scale-invariant.

Reduction is a trade, not an improvement.

Analyze content sequences within a single section of a page for AEM Edge Delivery Services, providing neutral descriptions without assigning block names.

Use to build Science's data, code, and materials availability — mandatory deposition in approved repositories, accession numbers, a compliant data-availability statement, and materials/reagent sharing.

Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.

Assemble tax-season materials — quarterly estimates or year-end 1099s — plus an organized accountant packet.

Use to plan and run peak-season support — what to freeze, pre-stage and measure daily so volume spikes do not become quality and attrition crises. Trigger for "peak season readiness", "holiday staffing plan", "Black Friday prep", freeze policy or KB changes before peak, pre-hire and ramp for surge, daily war room metrics, peak post-mortem, or when last year's peak broke SLA and attrition.

Find root causes of bugs, test failures, or unexpected behavior before proposing any fixes.

Systematic root-cause analysis for bugs, errors, and unexpected behavior

Systematic debugging with root cause investigation. (gstack)

Guides systematic root-cause investigation before proposing fixes for any technical issue, with a four-phase process and red flags to avoid guessing.

Diagnoses a reported bug, error, test failure, or unexpected behavior through a 4-phase root-cause analysis before any code changes.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

Interpretabilidade e explicabilidade de modelos usando SHAP (SHapley Additive exPlanations). Use essa skill ao explicar predições de modelos de machine learning, computar importância de features, gerar plots SHAP (waterfall, beeswarm, bar, scatter, force, heatmap), depurar modelos, analisar vieses ou justiça de modelos, comparar modelos ou implementar IA explicável. Funciona com modelos baseados em árvores (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), modelos lineares e qualquer modelo black-box.