Results for “seasonal-decompose”

Foundational theory for process-oriented concurrency through synchronous message-passing, applicable to multi-agent coordination and parallel decomposition

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

Trains and analyzes Sparse Autoencoders (SAEs) with SAELens to decompose neural network activations into interpretable features, covering loading pre-trained SAEs, training custom ones, and feature steering.

Systematically tear down and remove a service, package, or infrastructure component

Composite skill — full debugging workflow from "this is broken" to root cause and fix. Chains systematic-debugging (root-cause hypotheses) → tracer agent (evidence walk) → sentry (production correlation if applicable) → ci-watch (regression check) → incident-response (if production-impacting). Use when a bug needs deep investigation, not just a quick fix.

A stakeholder asks to make search faster.

Step-by-step debugging workflow: reproduce the bug, isolate the failing component, trace to root cause, apply a targeted fix, and verify the fix resolves the issue without regressions. Use when you encounter a bug, error, exception, crash, or unexpected behavior that needs troubleshooting.

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

**Monthly Partitions:**

`analysis-agent`: use when a request needs normal, failure, edge, abuse, recovery, or operational scenarios; skip when no scenario-decomposition decision exists.

Task Decomposition

Use to write CX commentary that makes a number decision-ready — what moved, why (decomposed), so what, do what — separating noise from signal and stating confidence honestly. Trigger for "write the commentary", "explain this trend", "what should we say about the drop", weekly or monthly narrative, board pack prose, "why did QA fall", metric movement write-up, or when a report has charts but no story.

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.

Find Earth2Studio models, data sources, and examples for weather/climate use cases by consulting live documentation and verifying compatibility via the lexicon system.

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.

Breaks natural-language problem descriptions into sub-tasks suitable for DAG nodes. The entry point of the meta-DAG. Identifies phases, dependencies, parallelization opportunities, and vague/pluripotent nodes that can't yet be specified. Uses domain meta-skills when available. Activate on "decompose task", "break down problem", "plan workflow", "what are the steps", "sub-tasks", "task breakdown". NOT for executing the decomposed tasks (use dag-runtime), building the DAG structure (use dag-planner), or matching skills to nodes (use dag-skills-matcher).

Decompose a task into parallel workstreams, assign agent ownership, run integration at dependency boundaries, and synthesize results.

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.

Run a reproduce → isolate → verify debugging workflow for concrete bugs, regressions, flaky failures, and environment-specific behavior. Use when the user already has a failing command, test, request, UI flow, or narrowed symptom and needs root-cause diagnosis or fix verification rather than raw log-line selection, broad test-policy design, PR review, or generic performance tuning.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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.

Decompose work into small, verifiable tasks with explicit acceptance criteria, ordered by dependencies and sliced vertically for reliable implementation.

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

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

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

Composite skill — promote the long-lived `release` branch to `main`, cut a single version that batches every PR accumulated since the last tag, then clean up. Chains pr-merge-readiness (on release→main PR) → version-bump (semver from commits) → changelog-update (promote [Unreleased] → versioned) → ship (tag + GitHub release) → stale-branch cleanup → optional ship-it for deploy. Use this INSTEAD of releasing every small fix individually. Many small commits become one well-scoped release.

Systematic debugging with root cause investigation. (gstack)

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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

Write SOQL queries that group or filter records by date periods using the 13 SOQL date functions, with rules for WHERE vs GROUP BY placement, date-literal comparisons, and fiscal-year restrictions.