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Evaluate pages and components for usability, visual hierarchy, clarity, and emotional tone. Produce a score out of 40, examine tasks from relevant user perspectives, and prioritize concrete improvements around the user's goals and constraints.
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Create Vally evaluation specifications for skills, custom agents, and workflow packages. Design distinct test requests, prepare fixture files, define automated checks and outcome rubrics, and size comparisons to provide meaningful evidence without favoring the skill’s wording.
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Design metrics that measure whether a workflow achieved its intended outcome using recorded session evidence. Choose built-in checks or custom JavaScript, retain suitable Bash evaluators, and define tests that distinguish success, failure, and missing evidence.
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Assess factual claims in AI-generated text by separating facts from opinions, choosing suitable independent evidence, and explaining what is supported or uncertain. Use it for a completed fact-check or an optional exercise in evaluating AI answers.
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Evaluate policy effects with difference-in-differences, a method comparing changes in treated and untreated groups. Guides assumption checks, regression models, staggered adoption analysis, robustness checks, and reporting with event study plots.
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Evaluate papers, research proposals, and literature reviews using structured criteria. Produces evidence-based feedback on research questions, methods, data, analysis, and writing, with optional scores and prioritized recommendations tailored to the work's stage and purpose.
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Evaluate scientific manuscripts and grant proposals for sound methods, statistics, reproducibility, ethics, and clear reporting. Produce a structured review that separates major concerns from minor issues and gives authors specific, constructive suggestions for revision.
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Run a build-and-evaluation pipeline from a natural-language specification. Generate scenarios, implement the project, test scenarios withheld during implementation, and report scores against a target with failures and suggested next steps.
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Review scientific manuscripts and grant proposals using structured checklists. Assess methods, statistics, reproducibility, ethics, and reporting completeness, then organize findings into a balanced report with major concerns, minor comments, and specific suggestions for revision.
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Assess papers, research proposals, and literature reviews using structured quality criteria. Receive strengths, weaknesses, optional five-point scores, and prioritized suggestions tied to specific sections, with expectations adjusted to the work’s purpose and development stage.
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Evaluate an SNS post draft before publishing with a five-part scorecard. Use past engagement data or general benchmarks to assess the opening, account voice, useful detail, formatting, and readiness, then identify specific improvements.
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Design and improve prompts and structured responses for AI applications. The skill also guides evaluation, retrieval augmented generation, which uses retrieved material to inform answers, and agent architecture, with references and scripts for production workflows.
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Plan experiments, analyze data, and build and evaluate predictive models. The skill also covers explaining findings to stakeholders and designing production workflows for model deployment, monitoring, and retraining to support business decisions.
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Write structured reviews of scientific manuscripts and grant proposals. Assess methods, statistics, reproducibility, ethics, and reporting completeness, then organize strengths, major concerns, minor issues, and specific suggestions into constructive feedback.
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Implement filtering, aggregation, joins, and file processing with Polars for tabular data that fits in memory. Use lazy evaluation, which optimizes operations before running them, and parallel execution to improve pipelines and migrate pandas code.
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Develop and review machine learning approaches through at least three rounds of critique and refinement. Compare models with simple reference predictions, question data assumptions, and investigate overfitting, feature choices, and evaluation methods before accepting a solution.
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Evaluate trained machine learning models using metrics suited to the task. Compare candidates consistently, review decision thresholds and probability estimates, and identify weaknesses in validation before deployment or publication.
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Create synthetic defect images for detector training using clean images and masks that mark defect regions. Guide mask placement, Cosmos fine-tuning, image generation, evaluation, quality refinement, and automatic assignment of provisional labels.
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Integrate NVIDIA NeMo Fabric into an application or evaluation system through its Python SDK. Map application settings into typed configuration, choose an execution lifecycle, validate prerequisites, and handle standardized results, artifacts, and telemetry.
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Tune adaptive behavior in an instrumented NeMo Relay application using runtime data. Configure tool parallelism, prompt caching, and state storage, then compare changes against a baseline to check latency, correctness, and failure rates.
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Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context precision, citation coverage, citation validity, diagnostics, Markdown/JSON reports, or threshold-gated evaluation in CI.
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Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.
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Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.
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Structure and debug a LÖVE (Love2D) game in Lua: the love.load/update/draw loop, delta-time movement, input, and screen states. Use when building a LÖVE 11.x game (main.lua, conf.lua, .love).
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