Skill UI

An internal utility designed for deep investigation of Go panics and stack traces. It helps identify Go-specific issues, such as nil pointer dereferences or unhandled errors, by suggesting minimal reproducible `go test` commands and targeted patch locations for robust debugging.

This internal utility probes JavaScript or TypeScript stack traces to provide deep diagnostic insights. It identifies potential language-specific checks (e.g., async boundaries, type contracts), generates minimal reproduction commands for environments like Node.js, npm, or Jest, and highlights targeted patch areas for efficient debugging and bug fixing.

An internal utility designed for deep analysis of Python tracebacks. It performs Python-specific root-cause checks, provides guidance for minimal reproducible examples using pytest, and identifies precise targets for implementing defensive coding practices (like guard clauses or schema validation). Ideal for advanced debugging scenarios.

An internal utility designed for advanced debugging of Rust applications. It helps developers analyze panics or backtraces by checking proper handling of Option and Result types, generating minimal reproducible `cargo test` commands, and identifying precise patch targets for safe code improvements.

Skills and references for writing, debugging, and optimizing DAX in semantic models. Provides a tiered framework, engine internals, patterns, and model optimization strategies, along with trace capture and performance profiling guidance for local and remote models.

A comprehensive guide to authoring, optimizing, and validating Power Query M expressions used within semantic model partitions. This resource covers crucial topics such as maintaining query folding for performance, applying early filtering and column pruning, and leveraging the Power Query API for robust data validation and debugging.

This skill applies a rigorous, evidence-first discipline to coding tasks, inspired by Fable-like best practices. It guides agents through a structured workflow: inspecting the workspace, tracking goals and findings, grounding all conclusions in verifiable evidence, and executing thorough verification gates before declaring completion. Ideal for complex development, debugging, or adapting cross-model prompt guidance into robust, verifiable project rules.

This tool parses raw, complex error logs, stack traces, and crash reports into a highly structured and actionable diagnosis. It goes beyond simple error reporting to identify the probable root cause, analyze the affected code path, and provide concrete, code-level fix suggestions and ordered next debugging steps. Ideal for developers facing critical production bugs or unexplained application crashes.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.

A specialized diagnostic skill designed to analyze failing GitHub Actions workflow logs. It identifies the root cause of crashes, such as missing secrets, environment mismatches, or deprecated syntax. The output provides precise YAML or code diffs required to fix the pipeline and optimize CI/CD processes.

This advanced protocol forces the AI to conduct a thorough context audit before attempting any complex task. It mandates reading every relevant file, identifying architectural gaps, and providing a detailed orientation statement. It is essential for multi-file modifications, system design, and debugging to prevent acting on assumptions and maintain high code accuracy. The AI cannot proceed until all unknown variables are resolved.