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Found 2,036 Skills
Structured 8-factor vendor evaluation framework for AI marketing tools, based on Venkatesan & Lecinski's The AI Marketing Canvas (2nd ed., Stanford Business Books, 2026). Scores each tool against EA market accessibility, data requirements, integration compatibility, team capability, and total cost in UGX, then produces a shortlist with 30-day experiment briefs. Invoke when a client has completed the ai-readiness-diagnostic and is at Canvas Step 2 (Experimentation) and is ready to select specific AI tools for structured trials. Also invoke when a client wants to compare 2–4 named tools before purchasing or committing budget.
Help users make better hiring decisions. Use when someone is evaluating job candidates, making hiring decisions, conducting reference checks, reviewing work samples or take-homes, calibrating their hiring bar, or deciding between finalists.
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.
Use when comparing technology stacks, evaluating frameworks/providers, or assessing TCO, security, and ecosystem health for migration decisions.
Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.
Professionally evaluate story outlines, judge and score from the dimensions of market potential, innovation attributes, and content highlights. Suitable for story outline quality assessment, IP adaptation potential judgment, and project approval decision-making
Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using a confidence-based framework with adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of evaluating LLM output quality.
Create validated LLM-as-a-Judge evaluators following best practices — binary Pass/Fail judges with TPR/TNR validation for measuring specific failure modes. Use when you need to automate quality checks, build guardrails, or measure a specific failure mode identified during trace analysis. Do NOT use when failures are fixable with prompt changes (use optimize-prompt) or when failure modes are unknown (use analyze-trace-failures first).
Evaluates ML models for performance, fairness, and reliability. Use for metric selection, cross-validation strategies, overfitting/underfitting diagnosis, hyperparameter tuning, LLM evaluation, A/B testing, and production monitoring for model drift.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).