Results for “book-metrics”

Build the funnel metrics that actually get trusted. Stage-by-stage conversion, velocity, win rate, and the single biggest leak, with every definition pinned so nobody relitigates the numbers in the meeting. Built for B2B RevOps teams, customizable to your CRM and your stage model. Trigger on "build my funnel metrics", "what's my conversion by stage", "where's the leak", "what's our win rate", "how fast do deals move", or any funnel diagnostic.

Computes the Recall metric using torchmetrics, including configuration for binary, multiclass, and multilabel tasks.

This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.

Convert books into supervised fine-tuning datasets and train style-transfer models that replicate an author's voice.

Every metric encodes an opinion about which mistake hurts.

Computes the AUROC metric using torchmetrics, handling binary, multiclass, and multilabel tasks with configurable thresholds and averaging.

Computes the Anderson-Darling test statistic and p-value using scipy.stats.anderson for evaluating predictions against ground truth.

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B.

Computes the Receiver Operating Characteristic (ROC) metric using torchmetrics, supporting binary, multiclass, and multilabel tasks.

Computes the SQuAD metric using torchmetrics, given predictions and ground truth. Use when evaluating question-answering outputs with exact match and F1 scores.

Generates or executes a remediation prompt for a book flagged YELLOW or RED by /book-audit, writing a fix-<book-slug>.md that enumerates defects with verbatim PDF source text and verification commands, then optionally executes the fixes to bring the book to GREEN.

Dataset analysis and data quality specialist. Use when the user asks to talk to Cypher, requests the data detective, or needs dataset assessment, bias analysis, and benchmark evaluation.

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform