「benchmark」の検索結果

Testing standards for LoongCollector: unit tests, e2e tests, benchmarks. Reference when writing or reviewing tests.

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。

Run inference on Fireworks AI — direct, through the Leanmcp AI Gateway proxy (aigateway.leanmcp.com) for observability, or via LiteLLM / the OpenAI SDK / raw HTTP. Covers the endpoint-and-key matrix for each route, serverless vs dedicated deployments and their cold starts, browsing and filtering the model catalogue, reasoning models that return content:null with the answer in reasoning_content, function calling, concurrency and load testing, and runner scripts where many models share one protocol with a --use-proxy toggle. Use this skill whenever the user mentions Fireworks, FIREWORKS_API_KEY, api.fireworks.ai, fireworks_ai/ model ids, accounts/fireworks/models/..., a Fireworks deployment id, the Leanmcp gateway or LEANMCP_API_KEY, app.leanmcp.com observability, or gpt-oss / kimi / glm / qwen / deepseek served on Fireworks; wants to route LLM calls through a proxy for logging or cost tracking; is debugging empty responses, 404s, scope errors, cold starts, or "the gateway isn't working"; wants to pick a Fireworks model with tool calling; or wants to sweep several models over one benchmark. Reach for it even on vague asks like "why is this model returning nothing" or "set up inference for these models" when Fireworks is in play.

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Reads a set of financial statements and establishes what changed and why — fluctuation analysis against prior period and against budget, profitability, liquidity, solvency and efficiency ratios, benchmarking, and the non-GAAP measures presented alongside them. Use this to interpret results, review a counterparty's or target's financials, diagnose a deteriorating trend, explain a variance to someone who will act on it, or test whether an adjusted number is honest.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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。

Go testing patterns including table-driven tests, subtests, benchmarks, fuzzing, and test coverage. Follows TDD methodology with idiomatic Go practices.

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Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.

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Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious-case filtering, suspect-drug and concomitant-exclusion logic, reporting odds ratio analysis, comparator-drug benchmarking, cross-drug comparison, and cautious signal interpretation without causal overclaiming. Covers five study patterns (single-drug disproportionality workflow, multi-drug class comparison workflow, indication-stratified workflow, comparator-controlled signal screening workflow...

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Establish project governing principles including dev guidelines, code quality standards, testing policies, UX requirements, performance benchmarks, and security constraints.

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Evaluation harness for testing agent and skill quality through structured benchmarks, regression tests, and quality scoring.

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SkillsBench contribution workflow. Use when: (1) Creating benchmark tasks, (2) Understanding repo structure, (3) Preparing PRs for task submission.

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SkillsBench task authoring — walk a contributor from idea to submission-ready task following CONTRIBUTING.md and the task-implementation rubric. Use when the user wants to create a new SkillsBench task, scaffold a task from an existing workflow (notebook, Excel workbook, document, dataset), convert a prompt or a benchmark item into a SkillsBench task, write skills for a task, or prepare a SkillsBench PR. Pairs with `task-review` (run that as a self-check before submitting).

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Operational workflow for hard integer-programming optimization tasks: selecting an installed solver, preserving solver/incumbent certificates, extracting feasible schedules, recomputing metrics from final outputs, and writing consistent reports. Use when a task requires a MIP, solver status, objective value, bound, gap, formulation write-up, or benchmark output files.

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Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

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Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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。

Create, refine, and benchmark agent skills. Use when building a new skill, updating an existing one, running evals, checking trigger quality, or improving a skill description.

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Produce a polished local talking-head or narrated screen-recording rough cut without a cloud editor. Use when Codex must clean Chinese or mixed Chinese-English speech, correct product terminology before semantic editing, compress pauses without making speech breathless, preserve source resolution and frame rate, normalize dialogue loudness, generate final-audio subtitle artifacts, or benchmark local output against ChatCut/video-use/chengfeng/AI剪口播.

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。

视频标题生成与落盘工作流。Use when the user asks to title a finished or planned video, especially 抖音/小红书视频标题, 视频标题.md, 成片同级目录, 对标标题共性, 两段式标题, 基于视频主题起标题, 洗稿后标题, or replacing generic dbs-xhs-title for video titles. It derives the real video theme first, studies benchmark title patterns when provided, generates 8-12 theme-first two-part candidates, recommends/selects one, and writes 视频标题.md next to the final MP4 when requested.

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。

小红书图文/泛内容标题公式工具。从 75 个验证过的爆款公式中，帮你挑对的、用对的、理解为什么用这个。 不用于视频成片标题、视频洗稿标题、两段式抖音标题、成片目录 视频标题.md 写入；这些场景使用 ra-video-title。 触发方式：/dbs-xhs-title、/小红书标题、「帮我起个小红书标题」「小红书标题公式」 Xiaohongshu article/general title formula tool. Pick the right formula from 75 proven templates. For finished video titles or two-part benchmark-driven titles, use ra-video-title instead. Trigger: /dbs-xhs-title, "xiaohongshu title", "RED title formula"

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。

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.

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Microsoft Cloud Security Benchmark (MCSB v2) control-domain taxonomy and NIST 800-53 / CIS Controls crosswalk for planning and reviewing Azure cloud resources.

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Read-only evo run reporting. Use when the user invokes /evo:report, asks what happened overnight, asks what improved recently, asks for the best/frontier candidates, asks for a quick score chart without opening the dashboard, or wants the scatter plot in chat output. Never run benchmarks, gates, Slurm commands, evo run, or ad-hoc verification scripts for report requests.

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Initialize evo for the current repository by exploring the codebase, proposing unexplored optimization dimensions, constructing the benchmark inside a baseline worktree, and running the first experiment. Use when the user invokes /evo:discover, mentions setting up evo, wants to instrument a codebase for autonomous optimization, or asks to start a new evo run on a project.

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。

Creates, edits and benchmarks skills; supersedes the official skill-creator plugin, so when both are listed, use this one. Use when the user wants to create or improve a skill or run skill evals, or to distill work into a skill even without saying "skill": 把这次 session 做成一个 skill / 把以前的对话沉淀到 skill 里 / 从我认可的样例里提炼我的喜好.

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。