Skill UI

An advanced tool for network intelligence that calculates weighted graph scores to map the optimal path to a target. It identifies potential introducers, measures 'bridge value' across connections, and helps determine whether a connection requires a warm introduction or direct outreach on platforms like X and LinkedIn. Ideal for users needing the raw ranking methodology.

This skill applies principles of behavioral psychology and choice architecture to redesign user experience (UX) flows. It is used when a product's conversion friction is rooted in interaction design, not just copy. By auditing cognitive load, defining optimal default paths, and inserting strategic commitment points, it guides users toward desired actions ethically, maximizing commitment while preserving autonomy. Ideal for improving onboarding, checkout funnels, and complex decision-making sequences.

A comprehensive guide and toolkit for measuring Go application performance. Covers the entire workflow: writing benchmarks, running tests with statistical rigor, profiling hot paths using pprof, comparing results with `benchstat`, and tracking regressions in CI. Helps engineers ensure reliable and measurable performance improvements.

A comprehensive guide and skill set for optimizing Go (Golang) application performance. It establishes a core philosophy of profiling before optimizing, focusing on reducing allocations, improving CPU efficiency, and addressing structural anti-patterns. Provides systematic modes for deep review (hot path, architecture review) and follows an iterative methodology (Benchmark -> Diagnose -> Improve -> Compare) to ensure measurable and stable performance gains.

A comprehensive skill for performing deep security audits and writing secure code in Go. It guides developers through identifying vulnerabilities like SQL injection, XSS, path traversal, and crypto misuse. The framework incorporates advanced techniques such as STRIDE/DREAD threat modeling, data flow tracing, and multi-stage review modes (Audit, Review, Coding), ensuring defense in depth for robust Go applications.

This guide outlines a structured methodology for decomposing large specifications or vague projects into small, verifiable, and ordered tasks. It teaches best practices like dependency mapping, vertical slicing, and defining explicit acceptance criteria, ensuring reliable project execution and a clear development path.

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 utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

Behavioral guidelines for writing, reviewing, and refactoring code with LLMs, emphasizing minimalism, clear assumptions, surgical edits, and verifiable success criteria to avoid common machine-assisted coding mistakes.

A comprehensive guide detailing best practices for building, reviewing, and deploying Salesforce Flows. This skill covers selecting the correct Flow type (e.g., Screen, After-Save), ensuring bulk safety by preventing DML/SOQL in loops, implementing robust fault handling paths, and performing automation density checks to ensure scalable and resilient automation.

This skill analyzes an entire .NET solution (containing multiple projects) to map out a systematic migration path from Oracle to PostgreSQL. It scans for common Oracle indicators (NuGet packages, connection strings, code usage) and classifies every project into categories like MIGRATE, SKIP, or ALREADY_MIGRATED. The output is a structured, actionable Master Migration Plan, helping development teams manage large-scale database transitions efficiently.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.