Skill UI

This tool enforces four rigorous coding principles, inspired by Andrej Karpathy's insights on LLM pitfalls. It guides developers to surface assumptions, prioritize simplicity, make surgical changes, and define verifiable goals. Use it when reviewing code, checking diffs, or before committing to ensure high code quality and prevent over-engineering.

This comprehensive guide is designed for developers and system administrators to diagnose and resolve common integration errors encountered when interacting with the MindTickle API. It covers critical areas such as authentication failures (401, 403), rate limiting, SCIM provisioning conflicts, and content access issues. The document provides explicit error codes, underlying causes, and actionable fixes, ensuring stable and seamless platform integration.

A comprehensive guide detailing best practices for securing MindTickle integrations. It covers critical technical requirements including using `Company-Id` for multi-tenant isolation, implementing HMAC-SHA256 verification for webhooks, strict SCIM payload validation, and PII redaction. Essential for developers ensuring compliance with GDPR and SOC2 standards when handling sensitive employee and HR data.

Configure comprehensive, enterprise-grade access control for Miro via REST API v2. This module manages organization structure, team membership, and granular board permissions (RBAC). It supports advanced features like automated user provisioning using SCIM 2.0 and SSO integration, making it essential for implementing secure, scalable collaboration platforms in large corporate environments.

A comprehensive guide to securing Navan integrations and API access. This skill covers OAuth 2.0 best practices, secure credential storage, automated credential rotation, configuring Single Sign-On (SSO) via SAML/OpenID Connect, and implementing automated user lifecycle management using SCIM provisioning. Essential for hardening enterprise-level integrations.

This guide provides essential knowledge for handling common errors encountered when integrating with Veeva Vault. It covers REST API usage, VQL querying patterns, and VAPIL Java SDK concepts, helping developers manage document lifecycles and resolve issues like invalid sessions, insufficient permissions, or data validation conflicts in a life sciences CRM environment.

This guide provides deep-dive patterns and best practices for migrating enterprise data into Veeva Vault. It is essential for developers implementing advanced, compliant, and regulated workflows within the life sciences sector. Key considerations include validating API changes against strict compliance requirements, utilizing VQL for efficient data retrieval, and handling complex error scenarios during migration.

This skill provides enterprise-grade observability patterns specifically for Veeva Vault deployments. It guides users through implementing advanced patterns, utilizing VQL for efficient data retrieval, and ensuring compliance in regulated life sciences environments. Use this when building robust, observable applications within the Veeva ecosystem.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

A throughput-first interrogation tool designed to pressure-test major engineering plans. It rigorously reviews six critical areas—including DORA metrics, cycle time, hiring funnel health, team structure, and production discipline—before committing to delivery milestones, hiring waves, or organizational changes.

Simulates a comprehensive, multi-stage academic journal peer review process. This agent dynamically configures five expert reviewers (EIC, 3 peers, and a Devil's Advocate) to evaluate a paper across methodology, domain expertise, cross-disciplinary impact, and core arguments. It provides structured reports, editorial decisions, and revision roadmaps, supporting full reviews, post-revision verification, and calibration modes.