3.6천건 · 관련도순
Evaluates conglomerate discount with SOTP analysis, segment-level valuation, and separation benefit quantification. Use when analyzing conglomerate value, building SOTP models, or evaluating break-up scenarios.
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Evaluate tabulated subcontractor bids against specs and drawings — scope gap analysis, exclusion risk scoring, award recommendation. Triggers: 'evaluate bids', 'bid evaluation', 'lowest responsible bidder'.
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Assess an open-source agent framework for investment readiness by evaluating community health, supersession risk, architecture alignment, and governance sustainability. Produces a four-tier classification (INVEST / EVALUATE-FURTHER / CONTRIBUTE-CAUTIOUSLY / AVOID) to guide resource allocation decisions before committing engineering effort.
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执行模型测评（统一入口）。LLM/VLM 走主流程 scripts/run_evaluation.py + Evaluation YAML；model_family=dit 走 DiT 扩展节（references/dit/evaluate_workflow.md，vbench.py 批量推理产出视频）。
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Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.
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Rates a project with Jevaluate and keeps the rating private, with the same rubric and checks, a private library that export refuses, and nothing pushed or published. Use when a rating must stay private, such as "/jevaluate-private" followed by a link or folder, "rate this privately", "evaluate my own project", a client's or employer's code, a design doc or a local folder that isn't public, or any rating that must not reach the public ratings or the repo.
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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.
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Roblox and Luau game code standards. Writes, reviews, and fixes Script, LocalScript, and ModuleScript code and game systems such as shops, DataStore saves, RemoteEvents, combat, NPCs, and UI. Use when a request involves Luau code or a Roblox Studio project, even when Roblox is never named, when a game bug is reported with no file named, and when the user asks to keep these standards active. Also covers Rojo, Argon, Azul, Script Sync, Studio MCP, and community libraries such as ProfileStore, Knit, Trove, and Fusion. Applies to requests written in any language. Not for plain Lua outside Roblox (Neovim, LOVE, Defold, other engines) or for game design with no Luau to write or review.
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Use when designing evaluation suites for agentic systems, choosing agent benchmarks, measuring multi-agent performance, or critiquing single-score evaluation of agents. Based on arXiv:2605.26112v1. Trigger phrases: "evaluate my agent", "benchmark agent", "measure agent reliability", "pass^k evaluation", "agent metrics beyond success rate", "trajectory quality", "memory hygiene metric", "longitudinal agent eval", "why my agent scores well on benchmarks but fails in production".
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採用・人材戦略の専門スキル。職務記述書（JD）作成、採用計画策定、コンピテンシーモデル設計、 面接評価基準設計、オンボーディング計画をサポート。採用要件の明確化から内定後のフォローまで 一貫した人材獲得プロセスを支援。日英両言語のテンプレートを提供し、グローバル採用にも対応。 Use when: creating job descriptions, designing competency models, planning recruitment strategies, developing interview evaluation criteria, or creating onboarding plans. Triggers: "JD作成", "採用計画", "面接評価", "オンボーディング", "コンピテンシー", "人材獲得", "job description", "recruitment plan", "interview evaluation", "hiring", "talent acquisition"
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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.
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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.
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Guide for evaluation — inline evaluators, Inspect AI integration, and custom evaluators for measuring training progress. Use when the user asks about evaluation, metrics, benchmarks, or how to measure model quality during training.
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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.
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Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.
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Autonomous model validation and benchmarking. Investigates any ML model (LLM, image gen, TTS, time series, etc.), runs it on GPU cloud, evaluates quality and performance, and generates HTML reports. Use when user asks to verify, benchmark, evaluate, or test a model. Triggers on "verify model", "benchmark", "evaluate model", "test model", "run benchmark", "model evaluation", "モデルを検証", "ベンチマーク", "モデルを試して".
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Apple Intelligence (Foundation Models / Evaluations / Image Playground) の API リファレンス。 LanguageModelSession, SystemLanguageModel, PrivateCloudComputeLanguageModel (PCC), Generable, GenerationSchema, Tool, OCRTool, Transcript, DynamicInstructions, Attachment, Evaluation, ModelJudgeEvaluator, ToolCallEvaluator, ImagePlaygroundSheet, ImageCreator, Foundation Models SDK for Python (apple-fm-sdk)。
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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
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Runs the CARLA Leaderboard evaluator over a set of routes with your agent — routes/routes-subset/repetitions/track selection, checkpoint and live-results endpoints, resume after a crash, recording, and the debug levels — for leaderboard 1.0, 2.0 or 2.1. Preflights the whole stack (version pairing, maps, ports, agent class, sensor budget) before spending hours, and hands the world back in async mode. Use when the user asks to "run the leaderboard", "evaluate my agent", "run routes_training", "resume an evaluation", or "reproduce my submission locally".
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Planner→Builder→EvaluatorのLong-Runningエージェントハーネスで複雑なタスクを実装します。plannerがfeatures.jsonに機能を分解し、builderがTDDで1機能ずつ実装し、evaluatorがPASSするまでbuilderを繰り返します。「〇〇を作って」「実装して」「long-running」などのリクエストで起動します。
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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.
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Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.
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Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).
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