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Found 1,945 Skills
Comprehensive GitOps methodology and principles skill for cloud-native operations. Use when (1) Designing GitOps architecture for Kubernetes deployments, (2) Implementing declarative infrastructure with Git as single source of truth, (3) Setting up continuous deployment pipelines with ArgoCD/Flux/Kargo, (4) Establishing branching strategies and repository structures, (5) Troubleshooting drift, sync failures, or reconciliation issues, (6) Evaluating GitOps tooling decisions, (7) Teaching or explaining GitOps concepts and best practices, (8) Deploying ArgoCD on Azure Arc-enabled Kubernetes or AKS with workload identity. Covers the 4 pillars of GitOps (OpenGitOps), patterns, anti-patterns, tooling ecosystem, Azure Arc integration, and operational guidance.
Risk & Change Management (Devil's Advocate): Identify risks, manage issues, and evaluate change requests. Use this skill to proactively detect threats, assess the impact of changes, and protect the project baseline.
Use when formulating clinical research questions (PICOT framework), evaluating health evidence quality (study design hierarchy, bias assessment, GRADE), prioritizing patient-important outcomes, conducting systematic reviews or meta-analyses, creating evidence summaries for guidelines, assessing regulatory evidence, or when user mentions clinical trials, evidence-based medicine, health research methodology, systematic reviews, research protocols, or study quality assessment.
Use when making high-stakes decisions under uncertainty that require stakeholder buy-in. Invoke when evaluating strategic options (build vs buy, market entry, resource allocation), quantifying tradeoffs with uncertain outcomes, justifying investments with expected value analysis, pitching recommendations to decision-makers, or creating business cases with cost-benefit estimates. Use when user mentions "should we", "ROI analysis", "make a case for", "evaluate options", "expected value", "justify decision", or needs to combine estimation, decision analysis, and persuasive communication.
Transform emotional reactions or venting messages from collaborators into professional, diplomatic responses suitable for difficult workplace situations with toxic managers.
Use when evaluating ICP fit, buying intent, and routing priority for new leads.
Expert at analyzing documentation quality, coverage, and completeness. Auto-invokes when evaluating documentation health, checking documentation coverage, auditing existing docs, assessing documentation quality metrics, or analyzing how well code is documented. Provides frameworks for measuring documentation effectiveness.
Warden skill: evaluates first-pass findings and proposes deterministic lint rules that could permanently catch the same patterns. Requires Warden's multi-pass pipeline (phase 2).
Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimizing LLM outputs, or building prompt systems. Triggers include "prompt engineering", "prompt optimization", "chain of thought", "few-shot", "prompt template", "LLM prompting".
Apply cognitive science and HCI research to design decisions. Use when you need the scientific 'why' behind usability, explaining user behavior, understanding perception/memory/attention limits, evaluating cognitive load, assessing mental model alignment, predicting performance with Fitts's/Hick's Law, or grounding interface decisions in research rather than opinion.
Deep web research with parallel investigators, multi-wave exploration, and structured synthesis. Spawns multiple web-researcher agents to explore different facets of a topic simultaneously, launches additional waves when gaps are identified, then synthesizes findings. Use when asked to research, investigate, compare options, find best practices, or gather comprehensive information from the web.\n\nThoroughness: quick for factual lookups | medium for focused topics | thorough for comparisons/evaluations (waves continue while critical gaps remain) | very-thorough for comprehensive research (waves continue until satisficed). Auto-selects if not specified.
Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.