Team Rituals

Design products and pricing around validated willingness to pay, from Ramanujam & Tacke's "Monetizing Innovation". Use when the user mentions "pricing", "how much should we charge", "willingness to pay", "pricing page", "packaging", "freemium vs free trial", "are we leaving money on the table", "nobody buys at this price", "price increase", or "good-better-best". Also trigger when designing or auditing pricing and packaging, validating willingness to pay before building, segmenting customers by value, or choosing between subscription, usage-based, and freemium models. Covers price-before-product, willingness-to-pay talks, the four failures (feature shock, minivation, hidden gem, undead), leader/filler/killer packaging, and behavioral pricing. For offers and guarantees, see hundred-million-offers. For what customers value, see jobs-to-be-done.

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".

Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses.

Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet / contrastive losses. Use when training, evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include "train metric learning", "ml-recog", "retrieval embeddings", "triplet loss recognition", "fine-grained matching".

Design background Data Atlas style agents for Itô basket research, market discovery, parameter drafting, and human-in-the-loop editing. Use for architecture and workflow planning, not live order execution.

Expert guidance on validating, optimizing, and ensuring quality of Mapbox styles through validation, accessibility checks, and optimization. Use when preparing styles for production, debugging issues, or ensuring map quality standards.