「uat」の検索結果

Choose the authorized worker channel and hand over complete engineering responsibilities, including in-scope self-check, repair and delivery. Use for substantial investigation, implementation, continuation and recovery — an ordinary substantial goal is enough, no special phrasing required; use swarm for approved independent batches. Respect the operator's standing ACP/native choice, explicit solo, and tiny or nearly finished work.

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在 W8A8 动态量化后，用 MindIE-SD 冒烟测试 DiT 量化权重是否可加载（可选 skill）。与 `quant-tuning-evaluate`（DiT 扩展节）的区别：本 skill 仅做"能跑通 + 权重可用"的轻量验证（4~16 条 prompt，几分钟出结果），不算分；后者产出视频集交 `quant-tuning-score-dit`（AISBench-VBench 16 维度）评分，是调优回路一环。不引入 vLLM-Ascend、不做精度指标评估（FID/CLIP-score 等）。

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Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).

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解析中文自然语言中的 AISBench 测评需求，检查 Python 与 AISBench 运行环境，通过分轮追问确认关键配置，将基准名称映射为仓库内的数据集任务，在用户最终确认后生成并执行 AISBench CLI，并返回精度指标和产物路径。适用于用户要求使用 AISBench 评测模型或运行数据集；不适用于实现新数据集或仅解释基准概念。

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Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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当用户直接要求进行数据集压缩测评，或在量化调优过程中需要使用 AISBench 评测工具输出数据集精度等场景时使用。 本 Skill 负责：使用 AISBench 数据集压缩代码、用 RBF Kernel Herding 算法生成压缩后的数据子集（coreset），并按用户目标交付两种产物之一： ① 接入量化调优主流程：产出独立的 coreset 数据集配置（不覆盖原始数据），回传「全集 config_name + coreset config_name」给编排层，供 evaluation.yaml 切换； ② 仅数据集压缩测评（不做量化调优）：基于 example 模板生成「全集测试脚本」和「子集测试脚本」两份 shell 脚本。 暂时支持 AIME 2025 和 GPQA，其他数据集需要再结合 aisbench tools目录下 herding_coreset_selector 源码进行分析。

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Sector Rotation & Analysis — analyzes sector momentum rankings across all 11 GICS sectors, money flows, economic cycle positioning, relative strength, top stocks per sector, valuations, and rotation signals. Triggered by "trade sector <sector>" or "trade sector rotation".

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Head-to-Head Stock Comparison — compares two stocks across valuation, growth, profitability, financial health, technical setup, sentiment, and risk with category winners and a weighted overall score. Triggered by "trade compare <TICKER1> <TICKER2>".

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30/60/90 Day Onboarding Plan — Day 1 setup, week 1 ramp, month 1 milestones, month 2 contributions, month 3 evaluation criteria

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Employer Brand Audit — Glassdoor/Indeed review analysis, LinkedIn company page assessment, career site evaluation, employee testimonials, competitiveness vs other employers

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Interview Question Generator — produces 40-60 questions across behavioral (STAR), technical, culture, situational categories with scoring rubrics and recommended loop structure

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Use when scoring finished content against quality dimensions, evaluating whether a piece meets publishable standards, or determining which workflow phase needs rework based on quality gaps.

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Calculates CRAP (Change Risk Anti-Patterns) score for .NET methods, classes, or files. Use when the user asks to assess test quality, identify risky untested code, compute CRAP scores, or evaluate whether complex methods have sufficient test coverage. Requires code coverage data (Cobertura XML) and cyclomatic complexity analysis. DO NOT USE FOR: writing tests, general test execution unrelated to coverage/CRAP analysis, or general code coverage reporting without CRAP context.

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Plan AI fine-tuning projects with data preparation, training, and evaluation. TRIGGERS - Use when user needs help with ai-fine-tuning-plan related tasks.

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

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Build text and data classification systems with training and evaluation. TRIGGERS - Use when user needs help with ai-classification-system related tasks.

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Evaluate and implement agricultural technology solutions. TRIGGERS - Use when user needs help with agricultural-technology related tasks.

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Drive the terum-skills CLI from a Claude Code or Codex session: run any verb with --format md and show its board — inspect the Library or the team Marketplace, search, fetch with sync, validate or fix a local skill, install the latest version into Global or an added project, publish a local skill as an immutable version, manage the eval queue — and prepare the verbs that belong in a terminal (project setup, skill move/copy/rename/delete, prune, reconcile, machine uninstall, team administration). Use when the user wants to manage, evaluate, publish or install skills and no narrower terum-skills skill fits.

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Evaluate a local skill with the terum-skills CLI — hygiene, trigger evals and execution cases against a baseline, billed to the user's own Claude Code — after confirming the cost, and show the result as a Markdown board. Use when the user asks to eval, test, score or benchmark a skill, or to see whether an edit made it better.

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Evaluate architecture and module boundaries for volatility-based decomposition using Juval Lowy's framework (from "Righting Software", building on Parnas 1972). Use when reviewing module splits, service boundaries, new abstractions, or any decomposition decision. Trigger on phrases like "where should this boundary be", "how to split this", "module boundaries", "encapsulate change", "volatility", or references to Lowy, Parnas, or "Righting Software". Complements /hickey (interleaved concerns) with a different lens (change encapsulation).

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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The Arbor executor workflow: clone the repo from the git bundle your brief hands you, implement and evaluate exactly ONE hypothesis with the file tools, then report structured results via worker_complete. Preloaded automatically on arbor-executor task workers. Never touch the held-out test split.

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Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.

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Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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。