Results for “eval”

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Present HTML on connected MarketingClaw node canvases, navigate/eval/snapshot, and debug canvas host URLs.

Present HTML on connected OpenClaw node canvases, navigate/eval/snapshot, and debug canvas host URLs.

Evaluate and improve interface usability using heuristic analysis. Use when the user mentions "usability audit", "UX review", "users are confused", "heuristic evaluation", "form usability", "navigation problems", "Nielsen heuristics", "cognitive walkthrough", or "usability testing". Also trigger when reviewing a design for usability issues, improving form completion rates, or evaluating information architecture and navigation. Covers Nielsens 10 heuristics, severity ratings, and information architecture. For visual design fixes, see refactoring-ui. For conversion-focused audits, see cro-methodology.

Deobfuscates malicious JavaScript code used in web-based attacks, phishing pages, and dropper scripts by reversing encoding layers, eval chains, string manipulation, and control flow obfuscation to reveal the original malicious logic.

Evaluates text-to-image generation models by decomposing assessment into five specialized skills (object presence, count, spatial relations, scale, and text rendering) and open-ended prompts, producing interpretable binary scores with visual and textual explanations.

编写评估FastText文本分类模型的Python函数，计算accuracy、F1、recall和precision指标，并处理特定格式的标签文本分割。

DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers

Screens resumes with skill matching, experience evaluation, and bias-free candidate ranking

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

Automates detection engineering workflows in Google SecOps by extracting threat intelligence, generating detection opportunities, simulating attacker behavior with synthetic events, evaluating rule coverage, and creating new YARA-L 2.0 rules to close gaps.

Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, automated anti-pattern detection, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior

Evaluates automated metrics and vision-language models on identifying granular errors in detailed image descriptions and ranking paired descriptions against human judgments, using macro F1, pairwise accuracy, Spearman rank ρ, and Kendall's τ.

Use when planning an HPCA project timeline from venue fit through the two-step July registration and paper gate, the autumn review wait, the single rebuttal/revision window, notification, IEEE camera-ready, artifact evaluation, and the February/March talk, with backward-planning offsets for a hardware-evaluated paper.

Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.

Build and evaluate LLM prompts. Use when crafting system prompts, comparing variants, estimating tokens, or managing prompt templates.

Explain and apply core ML/AI concepts — model types, training pipelines, evaluation metrics, and neural architectures.

Create a role interview kit with role outcomes, interview plan, scorecard, evidence prompts, and evaluation criteria.

Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

Analyze amm operations. Use when you need to understand amm mechanisms, evaluate protocol security, or reference on-chain concepts.

Analyze cex operations. Use when you need to understand cex mechanisms, evaluate protocol security, or reference on-chain concepts.

Analyze mev operations. Use when you need to understand mev mechanisms, evaluate protocol security, or reference on-chain concepts.

Analyze vrf operations. Use when you need to understand vrf mechanisms, evaluate protocol security, or reference on-chain concepts.

Write RFPs with requirements, evaluation, and timeline. TRIGGERS - Use when user needs help with rfp-writer related tasks.

Write RFPs with requirements, evaluation, and timeline. TRIGGERS - Use when user needs help with rfp-writer related tasks.

Analyze cex operations. Use when you need to understand cex mechanisms, evaluate protocol security, or reference on-chain concepts.

Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

Derive owner/operator dogfood scenarios from product evidence and active-use cadence, then produce adoption instructions and manual evaluation checks

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

MCP (Model Context Protocol) server build and evaluation guide, including local conventions for tool surfaces, config, and testing

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.