bundled/skills/build-error-resolver/SKILL.md
Compatibility alias for build-specific error resolution. Use this when VCO routes to build-error-resolver but the upstream agent is unavailable in the current runtime.
npx skillsauth add foryourhealth111-pixel/vco-skills-codex build-error-resolverInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Provide a stable local entrypoint for VCO build-error-resolver routes.
This skill preserves routing compatibility across environments where the
everything-claude-code:build-error-resolver agent may not be available.
everything-claude-code:build-error-resolver when available.error-resolver workflow.development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.