editing-an-existing-application

Execution skill. Use when configuring an application calendar — choosing calendar type, fiscal year, date range, and time dimensions.

Planning skill. Supply chain use cases: demand planning, inventory management, procurement planning, S&OP alignment. Each use case has inline guidance.

Planning skill. Sales performance use cases: quota planning, commission calculations, territory assignment, attainment tracking, pipeline coverage. Each use case has inline guidance.

Planning skill. FP&A use cases: budget/expense planning, revenue forecasting, P&L reporting, multi-currency, financial consolidation. Each use case has inline guidance; load sub-files only for advanced patterns.

Execution skill. Use when sharing metrics or lists between Pigment Applications via Libraries and cross-Application formula references.

Execution skill. Use when investigating slow inputs, slow calculations, or timeouts with the Performance Insights tools (performance_profile_change, performance_profile_change_scope_details, performance_slowest_changes, performance_changes_statistics, get_top_blocks_by_performance). Covers which tool to call, the includeAllExecutions default, reading change profiles (X/Y scope, contention, impacted viewed data), drilling into the scoped dimensions of some executions, locating the bottleneck, and comparing timings before/after a fix. Always profile before formula changes; never optimize from assumptions.

Execution skill. Use when writing PREVIOUS, PREVIOUSOF, or PREVIOUSBASE formulas, configuring iterative calculation cycles, or debugging circular dependency errors.

Execution skill. Use when naming or organizing Pigment Applications, folders, and blocks so models stay navigable, formula-safe, and consistent across teams.

Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.

Routes the weakest VCN samples (output of `tao-analyze-gaps-visual-changenet`) into per-augmentation-module subsets based on each module's label eligibility. Use when the user asks to "route VCN gap samples", "split AOI gaps for k-NN mining and AnomalyGen", or prepare the immediate next step after DEFT gap analysis in a VCN AOI SDA iteration.

Kumo-Anomaly — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune Kumo-Anomaly", "run Kumo-Anomaly inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune Kumo-Anomaly", "use perform_anomaly_analysis_with_diffusion", "automl Kumo-Anomaly", "hyperparameter search Kumo-Anomaly", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "kumo-anomaly", "sdk/anomaly_analysis", or "TSDiffuser_Generic".

Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions. Use when the user asks to "analyze VLM BCQ gaps", "extract VLM false positives and false negatives", or identify failure cases from a predictions JSON for DEFT root-cause analysis on a binary-classification VLM workflow.

Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.

Run TAO Data Services object-detection gap analysis from ground-truth and inference annotations. Use when an object detection workflow needs to identify weak images by comparing model predictions against ground truth using per-class recall, precision, and AP50 thresholds. Use when the user asks to "analyze OD gaps", "find weak OD images", or "run mAP gap analysis".

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".

Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.