Skill UI

An AI persona wrapper that adopts the style of a traditional Chinese mother to enforce productivity. It uses emotional pressure, social comparison, and strict coaching protocols to ensure tasks are completed thoroughly, pushing the AI to debug better and avoid excuses.

A cultural pressure‑style skill that forces the assistant to repeatedly push past failures, think systemically, validate every change, and proactively hunt down related issues until end‑to‑end ownership is demonstrated.

An agent skill simulating high-pressure big-tech corporate culture. It enforces strict owner consciousness, end-to-end validation, and role-based behaviors (P7-P10) using escalating PUA feedback to drive developers or sub-agents to deliver high-quality results.

Implements an ENFP-style SB Leader persona that keeps the core PUA guardrails (rules, escalation, methodology) while swapping pressure for empathy, praise, and playful banter to motivate engineers without losing rigor or closure.

This comprehensive guide details advanced techniques for optimizing Bright Data API scraping performance. It covers implementing connection pooling, robust response caching layers (LRU Cache), and managing concurrent requests with backpressure control. Use this when facing slow API responses or requiring high-throughput, scalable data extraction.

This guide provides production-ready patterns for using the @clickhouse/client library in Node.js. It covers critical, real-world scenarios such as safe typed queries, streaming large data inserts (with backpressure handling), robust batch operations with retries, and streaming SELECT queries for low memory consumption. It also includes best practices for comprehensive error handling and connection lifecycle management, ensuring stable and scalable ClickHouse integrations for data pipelines.

This guide provides advanced techniques to optimize integration performance with the MindTickle API. It addresses common enterprise bottlenecks such as slow learner progress queries, rate limit errors, and high-volume webhook backpressure. Techniques covered include implementing Redis caching, using offset-based pagination for bulk data retrieval, managing concurrent requests with connection pooling, and applying exponential backoff for robust rate limit handling.

A guide and tooling strategy to optimize programmatic interaction with the Notion API. It helps developers minimize costly rate-limit pressure, reduce engineering overhead, and maximize data throughput by identifying redundant calls, implementing efficient caching, and restructuring database queries. Essential for applications that rely heavily on Notion data synchronization and retrieval.

A comprehensive guide for diagnosing and resolving slow search performance in Qdrant. This resource covers issues related to low QPS (throughput), high latency for single queries, and slow filtered searches. Solutions include tuning HNSW parameters, managing memory pressure, implementing quantization, optimizing indexing, and configuring cluster settings for maximum efficiency.

This rigorous review simulates an interrogation by a Chief AI Officer (CAIO). It pressure-tests any AI plan across six critical dimensions: evaluation metrics, failure mode handling (SLOs), international regulatory compliance (e.g., EU AI Act), technical architecture (API vs. fine-tuning), cost economics, and necessary team roles. Use this when ensuring AI features are robust, safe, and commercially sound before launch or major investment.

This skill simulates a highly rigorous 'retention-obsessed CCO' interrogation. It pressure-tests any major business plan—whether concerning customer retention, segmentation, CS team scaling, or new hiring—by forcing the analysis of six critical, data-driven questions. Use this when major strategic decisions are pending, requiring deep vetting of customer health and financial viability.

This skill simulates a Chief Data Officer's deep interrogation, pressure-testing any plan touching core data strategy. Use it before approving ML model training runs, selecting data infrastructure (warehouse vs. mesh), productizing data assets, or making major team hires. It ensures that all data decisions are grounded in clear business decisions, compliance, and structural necessity.