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Found 1,943 Skills
Save progress and generate a continuation prompt. Updates PRD status markers, captures git state, and writes checkpoint.md for the next session.
Before starting any significant task, force explicit evaluation of available skills. For each potentially relevant skill, state YES/NO with reasoning. Only proceed to implementation after skills have been consciously evaluated and activated. Prevents the ~50% "coin flip" activation rate that occurs when skills are passively available but not deliberately considered.
Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
Evaluate complex requests from 3 independent perspectives (Creative, Pragmatic, Comprehensive), reach consensus, then produce complete outputs. Use for architecture decisions, creative content, analysis, and any task where multiple valid approaches exist.
Benchmark compensation against market data. Trigger with "what should we pay", "comp benchmark", "market rate for", "salary range for", "is this offer competitive", or when the user needs help evaluating or setting compensation levels.
Evaluates and prevents unnecessary abstractions by analyzing interfaces, layers, and patterns against concrete requirements. Use when evaluating new abstractions, reviewing architecture proposals, detecting over-engineering, or simplifying existing code. Triggers on "is this abstraction necessary", "too many layers", "simplify architecture", "reduce complexity", "over-engineered", "do we need this interface", or when reviewing design patterns.
Code quality gatekeeper and auditor. Enforces strict quality gates, resolves the AI verification gap, and evaluates codebases across 12 critical dimensions with evidence-based scoring. Use when auditing code quality, reviewing AI-generated code, scoring codebases against industry standards, or enforcing pre-commit quality gates. Use for quality audit, code review, codebase evaluation, security assessment, technical debt analysis.
Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
Evaluate output, identify lessons, decide accept/rework. Use after implementation.
Evaluate UX/UI using Jakob Nielsen's 10 usability heuristics. Comprehensive audit of visibility, control, consistency, error prevention, recognition, flexibility, aesthetics, error recovery, and documentation.
Systematically explore and evaluate a library, tool, or GitHub repo in an isolated scratch environment. Use this skill whenever the user asks to "try", "evaluate", "explore", or "kick the tires" on a library/repo/tool, especially when they provide a GitHub URL, npm/pip package name, or repo shorthand like "owner/repo". Use it when they want real primitives, failure modes, and composability beyond quickstarts before deciding on integration. Produces runnable scratch/ scripts demonstrating key primitives, a composition script, and a Tutorial.md with honest findings. This is NOT for full integration into an existing codebase.