「exemplar」の検索結果

Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.

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Extracts writing style patterns from exemplar text into a reusable profile. Use when creating a style guide or learning a specific author's voice.

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。

Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution, implementing distributed tracing, or moving from siloed tools to a unified observability platform.

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。

Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.

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。

Selecting WHO handles work is the Routing table; selecting HOW they handle it (Direct, Lightweight, Standard, Full) is Response Mode. This skill contains the complete decision table, exemplar prompts for each mode, the Lightweight spawn template, and the upgrade rules. Squad coordinator loads this on demand once routing has identified the agent — to pick the right ceremony level for the task.

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Use this skill when the operator is authoring, building, loading, or debugging a custom doca-bench plug-in — a versioned shared library with DOCA_EXPERIMENTAL-marked C entry points that doca-bench loads to measure a workload class its built-in modes do not cover, with doca_bench_cuda as the shipped reference exemplar. Trigger even when the user does not say "doca-bench-extension" or "doca_bench_cuda" — typical implicit phrasings include "no built-in doca-bench mode fits my workload", "how do I benchmark a CUDA GPUNetIO RX/TX kernel", "doca-bench cannot find or load my custom .so", "extension exported symbols do not match what the parent expects", "soversion mismatch after a DOCA upgrade", or "my GPU kernel hangs because stop_flag was never set". Refuse and route elsewhere for questions about which built-in doca-bench mode to pick, DOCA GPUNetIO programming semantics, CUDA toolkit installation, or contributor work on in-tree extensions — those belong to other skills.

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Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.

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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.

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Refreshes README structure favoring concision (an index, not a manual). Use when the README needs a structural update after significant project changes.

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 original short videos using the 样本倍增｜机器开启后连续输出同类群体 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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Greenfield project architecture + harness scaffolding for AI Agent productivity. Interview-driven decisions → requirements.md → execute. Produces: Code Structure (vertical slice exemplar), Test Infrastructure, Guard Rails, conditional extensions, AND Harness (CLAUDE.md with domain/team context, rules, skills, hooks). L2: architecture decisions, L3: harness setup, L4: requirements + harness decisions (tasks generated later by /execute into plan.json). Use when: "/scaffold", "scaffold", "new project", "set up project", "프로젝트 세팅", "초기 구조"

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。

Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.

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Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.

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Knowledge base from MIL-STD-40051-2C (15 Dec 2015), Preparation of Digital Technical Information for Page-Based Technical Manuals. Use for TM/TDP structure, work packages, front/back matter, style/format, warnings/HAZMAT, change packages, content selection matrices, and plate exemplars. Primary Training & Documentation pack (cluster 25) with Ops & Maintenance procedure use—page-based (-2C) only, not IETM 40051-1.

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Knowledge base from IS-GPS-200 Rev N (NAVSTAR GPS Space Segment / Navigation User Segment Interfaces). Use as a worked ICD/IS exemplar: what an interface specification is, DIST-A and IRN/CCB change control, interface definition vs identification, composite-signal criteria patterns, NAV data as payload families, and time/definition hygiene. Covers Rev N synthesized notes only; does not transcribe Apps II–IV bit fields or PRN/Gold-code tables, and does not replace faa-std-025 preparation rules.

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。