Results for “eval”

Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.

Trains, evaluates, exports, quantizes, and runs inference for SegFormer semantic segmentation models using NVIDIA TAO.

Map growth strategies across market penetration, market development, product development, and diversification to evaluate expansion opportunities.

Evaluates whether suing is worthwhile, explains the litigation process from filing to resolution, and guides case preparation and settlement negotiations.

Guides technical evaluation of code review feedback, emphasizing verification before implementation and reasoned pushback over performative agreement.

Provides guidance on using the Aeon library for time series forecasting, covering model selection, implementation, and evaluation.

Sarbanes-Oxley Act compliance reference — SOX sections, internal controls, audit requirements, and IT governance. Use when evaluating SOX compliance, designing internal controls, or preparing for financial audits.

Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

Help entrepreneurs identify and evaluate communities for a minimalist business. Use when someone is exploring business ideas, seeking their target community, or determining where to start.

Manage NVIDIA Brev GPU instances for TAO training, evaluation, and inference using the Brev CLI and Docker.

Audits UX quality of a codebase or validates implementation against design mockups, then fixes issues and commits changes.

Dispatches a code-reviewer subagent to evaluate work against requirements and plan, then acts on the feedback before merging.

Guides data protection impact assessments under GDPR Article 35, covering mandatory triggers, risk evaluation, and mitigation steps.

学术论文正文写作、结构设计与语言润色总入口。完整起草、续写、扩写、补写或实质性修订各学科的中英文论文正文时，进入paper-write-zh或paper-write-en；只做提纲、结构诊断或重排方案时，进入paper-shape结构模式；只做忠实润色或不新增研究内容的中译英时，进入paper-shape润色模式。研究评价转/doubao-academic-evaluator；独立系统性文献调研转/doubao-literature-research。

Battery Management System reference — cell balancing, SOC estimation, thermal management, and protection circuits. Use when designing BMS for lithium-ion packs, evaluating battery safety, or understanding cell monitoring.

Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.

Use this skill to evaluate which DataQ challenges have winning evidence and which don't. Covers documented vs anecdotal evidence and the 8 high-success patterns.

Use this skill when selecting a TPR-registered Training Provider for behind-the-wheel ELDT training. Covers the registry, evaluation criteria, and common pitfalls.

Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.

Analyzes email sequence performance metrics. Evaluates open rates, click rates, reply rates, and conversion by step. Identifies drop-off points, benchmarks against industry averages, and recommends optimizations.

Implement OpenFeature feature flags in software projects. Use when adding feature flags with OpenFeature SDKs, configuring providers, setting up evaluation context, or integrating the OpenFeature MCP Server.

Perform a detailed SWOT analysis — strengths, weaknesses, opportunities, and threats with actionable recommendations. Use when doing strategic assessment, competitive analysis, or evaluating a product or business position.

Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

Provides technical leadership frameworks for architecture decisions, engineering team scaling, technology strategy, and technical debt assessment.

Evaluate, design, and launch free tools for lead generation, SEO value, and brand awareness.

Helps identify and evaluate communities to build a minimalist business around, based on the philosophy of The Minimalist Entrepreneur.

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Instrument LLM applications with Langfuse to trace, score, and monitor cost, quality, and latency across OpenAI and LangChain integrations.

Guides the creation of high-quality MCP servers, covering design, implementation, testing, and evaluation for Python and TypeScript.

Design and evaluate programmatic SEO strategies for creating SEO-driven pages at scale using templates and structured data.

Reproduces an existing design as a faithful, self-contained HTML file, then generates and evaluates design variations to recommend the strongest option.

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

Scores generated text for morphosyntactic well-formedness by measuring how closely it adheres to language-specific dependency rules extracted from treebanks.

Meta-cognitive architecture optimization skill. Use when the user asks to "refactor the architecture", "optimize claude code", "evaluate components", or "run architecture audit". Also triggers automatically every 24 hours.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.