'lua' 검색 결과

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Set up a CARLA driving evaluation environment for Leaderboard 1.0, 2.0, or 2.1. Match the simulator and scenario_runner versions, configure Python paths, and verify that the required components load correctly.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up an environment to evaluate a driving agent
• Checking version differences behind inconsistent scores
• Recreating the Leaderboard 1.0 evaluation environment

Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing prompts for a new AI application
• Improving inconsistent model responses
• Defining JSON response formats

Create formula-based Excel valuation workbooks for public companies using discounted cash flow, a method that converts future cash flows into present value. Include per-share estimates, assumption sensitivities, source records, and checks that flag model limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a DCF valuation workbook for a public company
• Refresh a valuation with updated assumptions
• Compare values across capital costs and growth rates

Improve an evaluated Codex skill by turning plugin-eval findings into prioritized changes. Refine activation descriptions, shorten SKILL.md, move lengthy details into supporting files, fix structural issues, and compare evaluation results before and after the rewrite.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritizing fixes after a skill evaluation
• Shortening SKILL.md and reducing token use
• Clarifying when a skill should activate

Routes listed-company investment research to specialist skills for earnings analysis, valuation, financial model updates, and portfolio risk reviews. Helps organize evidence, assumptions, and investment theses into reports, models, memos, and monitoring trackers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare earnings previews and post-results analysis
• Assess company valuation and update financial models
• Track investment theses and upcoming catalysts

Evaluate local Codex skills and plugins, understand their scores, and identify fixes to prioritize. Get explanations of token estimates, set up benchmark scenarios, and prepare workflows for measuring actual token usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate a local skill or plugin
• Understand scores and prioritize fixes
• Explain token estimates and prepare actual measurements

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[omh] Choosing between coding agents on evidence: compare executor or agent choices on reproducible tasks using quality, cost, time, tool, and evidence metrics. Use when the user says: agent-evaluation, agent evaluation, agent eval, agent benchmark, executor evaluation, executor benchmark, compare agents, compare codex claude.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze how a private SaaS company's ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts (including an AI premium), competition, and investor demand, benchmarked against private-market medians and peers. Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds, or wants to compare a VC-backed software company's funding rounds. Research the rounds rather than answering from memory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when a user wants to create, run, or analyze evaluation suites for Microsoft 365 Copilot declarative agents with the public @microsoft/m365-copilot-eval CLI. Trigger on intents such as "evaluate my agent", "test my agent", "run my evals", "create eval prompts", "add multi-turn tests", "tune evaluator thresholds", "why is my agent failing", or "set up eval environment variables".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when evaluating the model and analyzing results for an Agricultural Systems (AgSy) manuscript so it survives expert systems review — independent model evaluation (observed vs. simulated, fit statistics), sensitivity and uncertainty analysis, and trade-off / scenario analysis across the system. Guides evaluation norms; it does not fabricate results or run the model.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate, select, and contract with vendors and SaaS tools. Use this skill when comparing alternatives, running an RFP, scoring vendors against criteria, negotiating contracts, planning a switch, or assessing a vendor's risk. Triggers on vendor evaluation, RFP, vendor selection, build vs buy, SaaS evaluation, vendor scorecard, vendor comparison, contract negotiation, vendor switch, procurement. Also triggers when a renewal is coming up or when a tool isn't meeting expectations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design a source evaluation protocol using lateral reading and credibility checks for digital information. Use when students need to evaluate websites, online sources, or social media claims.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user has run multiple evaluation skills and wants a comprehensive analysis — maturity assessment, cross-skill signals, trends, prioritized actions, and an executive summary. Also use when the user mentions eval health check, evaluation audit, ship readiness, evaluation maturity, or "how good is my evaluation system itself." This is a read-only analysis skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

在 Skynet 框架下，仅使用 skynet.httpc 和 luasec 库编写 HTTPS 请求代码，避免使用 ltn12 或不存在的 API 接口。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create and check long-running video material evaluation tasks. Use this skill when the user wants to submit videos for evaluation, check an existing video evaluation task list, or fetch the result of a previously created video evaluation task. This skill is not a general-purpose video upload skill because upload is allowed only as an internal step of task creation. Authentication uses an API key passed as an Authorization bearer token.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.