「eval」の検索結果

Evaluate cyber insurance policies and coverage. TRIGGERS - Use when user needs help with cyber-insurance-evaluation related tasks.

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Build automated regression suites — golden sets, threshold alerting, CI integration for model changes. Use when asked to "catch model regressions in CI", "build a golden set", or "automate eval regression".

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Expert guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.

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Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is.

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Evaluate a single skill's quality against its stated purpose. Classifies the skill's purpose type, then scores applicable quality categories — universal dimensions always apply; structural dimensions (scripts, references, assets) are scored only when warranted by the skill's purpose. A focused 20-line behavioral skill with no bundled resources scores fully on all applicable categories. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness, performing pre-publication evaluation.

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Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

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LLM prompt management and evaluation platform. Version prompts, run A/B tests, evaluate with metrics, and deploy with confidence using Agenta's self-hosted solution.

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Evaluate a codebase for agent-friendliness based on autonomous agent best practices. Use when asked to "evaluate for agents", "check agent readiness", "audit for autonomous execution", "assess agent-friendliness", or when reviewing infrastructure for unattended agent operation. Also use when asked about making a codebase more suitable for AI agents or autonomous workflows.

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Evaluate alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.

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Evaluate whether an OSS repo is worth long-term investment before committing time. Assesses project health, governance, community, bus factor, and trajectory. Use when deciding whether to contribute to a specific repo, choosing between multiple repos, or evaluating a project's sustainability. Not for checking if a repo accepts contributions — use oss-find-issue for that.

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Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

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Assesses startup viability through business model canvas analysis, unit economics validation, and market timing evaluation. Use when evaluating startup pitches, analyzing business model sustainability, or assessing product-market fit.

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Evaluates completed agent pipelines across 5 scoring dimensions and produces a composite verdict with actionable recommendations

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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations

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Scaffold and run Karpathy-style autoresearch loops in any git repo. This skill should be used when setting up autonomous code improvement, generating adversarial eval harnesses, running hypothesis-implement-eval-keep/discard loops, or checking autoresearch progress. Triggers on "autoresearch", "autonomous improvement", "eval loop", "hypothesis loop", "self-improvement loop".

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Evaluates and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).

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Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).

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Review user experience and interface design using Nielsen's heuristics, WCAG 2.1 accessibility guidelines, keyboard and screen reader audit, user flow analysis, cognitive load assessment, and form usability evaluation. Use when conducting a usability review before release, assessing WCAG 2.1 accessibility compliance, evaluating user flows for efficiency, reviewing form design, or performing a heuristic evaluation of an existing interface.

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Review software architecture for coupling, cohesion, SOLID principles, API design, scalability, and technical debt. Covers system-level evaluation, architecture decision record review, and improvement recommendations. Use when evaluating a proposed architecture before implementation, assessing an existing system for scalability or security, reviewing ADRs, performing a technical debt assessment, or evaluating readiness for significant scale-up.

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Prepare herbal remedies from Hildegard von Bingen's Physica. Covers plant identification, preparation methods (tinctures, poultices, infusions, decoctions), dosage guidance, contraindications, and safety review based on 12th-century medieval pharmacopeia. Use when needing an herbal remedy for a specific ailment using Hildegardian pharmacopeia, seeking guidance on preparation methods and dosage, researching medieval herbal medicine, or integrating Hildegard's plant wisdom into holistic health practice.

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Data fetching, caching, and revalidation patterns in Next.js App Router. Covers native fetch caching, ISR/SSG/SSR per-route choice, generateStaticParams, unstable_cache, revalidatePath/revalidateTag, Route Handlers, dynamic vs static rendering. Use this skill to: - Pick the right rendering mode (SSG / ISR / SSR / streaming) per route. - Use native fetch caching options correctly. - Build Route Handlers that match REST conventions. - Invalidate cached data after mutations. - Use generateStaticParams for dynamic SSG. Do NOT use this skill for: - RSC vs Client boundaries (see server-component-patterns). - Routing primitives (see nextjs-routing). - General conventions (see nextjs-conventions).

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Generate msmodelslim evaluation YAML configuration (service_oriented + aisbench + vllm-ascend). Use when user asks for evaluation config generation.

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Evaluate AI model performance with metrics, benchmarks, and comparison. TRIGGERS - Use when user needs help with ai-model-evaluator related tasks.

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Design AI evaluation frameworks with benchmarks. TRIGGERS - Use when user needs help with ai-eval-framework related tasks.

日本語の概要は準備中です。原文の説明を表示しています。