「process design」の検索結果

Designs and tests controls over financial reporting — segregation of duties, approval limits, evidence, and preparing for audit. Use this to design controls for a process, prepare for an external audit, respond to an audit finding, set approval thresholds, or assess where a small team's segregation of duties is genuinely broken.

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。

Design any process, idea, problem, or body of knowledge into an ICM (Interpretable Context Methodology) workspace — folder structure as agent architecture — or restructure an existing folder, repo, or vault into one. Use when the user wants to (1) turn a recurring workflow into an agent-runnable folder pipeline, (2) organize scattered notes, files, or knowledge into a library one AI agent can walk, (3) map a team or company as connected context ("context map", "second brain", "team brain", "knowledge base for AI"), (4) audit a codebase or mixed folder into a walkable edit map (objects, processes, change-impact) so later agents can change it without slurping the tree, (5) audit or restructure an existing workspace to ICM conventions, or (6) says "make this an ICM", "ICM this", "map this repo", "audit this folder", "what would a change hit", "build me a workspace", or "structure this for agents".

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。

For a repository in the babysitter-users catalog, locate its babysitter processes and any committed runs (.a5c/runs/<runId>/) and perform a retrospective on a chosen run -- what went well, what failed, process suggestions, quality of effect design, breakpoint patterns -- mirroring the /babysitter:retrospect workflow but applied to an external repo. Invoke when asked to "retrospect on repo X's run", "analyze how someone else used babysitter", or "review an external babysitter run".

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。

Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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。

Design a 360-degree feedback survey or write a structured 360 feedback report. Use when asked to build a 360 feedback process, write 360 feedback for a colleague, design a feedback survey, or produce a feedback report. Produces either a complete survey instrument with rating scales and open-ended questions, or a structured narrative feedback report with themes, strengths, and development areas.

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。

Deploys and operates a LiveKit agent in production: shipping a version to LiveKit Cloud and rolling it back, secrets and configuration, the worker process model and prewarming, safe async inside worker processes, provider timeouts and degradation, graceful shutdown, SDK upgrades, and observability. Use when the user says "deploy my agent", "roll back the deployment", "tail the agent logs", "the first call after a restart is slow", "attached to a different loop / event loop is closed", "prewarm the VAD", "shut down without dropping calls", "upgrade livekit-agents safely", "correlate logs by session", or is changing an agent codebase that is already live. Not for designing the agent (building-livekit-agents) or reproducing one bad conversation locally (debugging-livekit-agents).

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Model, simulate, and optimize Serializer/Deserializer (SerDes) systems — serial and parallel links — using MATLAB SerDes Toolbox. Design NRZ and PAM-N links (PAM3 through PAM16) — explore equalization architectures (FFE, CTLE, DFE), sweep or optimize parameters with genetic algorithms, and characterize channels from loss models, S-parameter files, or crosstalk scenarios. Process captured waveforms through equalization chains, build eye diagrams, and decompose jitter. Deliver IBIS-AMI models for Tx, Rx, Redriver, or Retimer by exporting to Simulink and compiling .ami/.ibs/.dll/.so files. Covers the full arc from initial design exploration and parameter optimization to compliance testing and compiled model validation, including custom datapath blocks for nonstandard equalization.

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。

Builds and edits a model-driven Power Apps app from a natural-language intent — tables, columns, relationships, adaptive forms with sub-grids, views, Choice-column charts, business rules, business process flows, generative page intents for overview/dashboard surfaces (page `.tsx` generated in generate-pages after plan approval), and an app module + sitemap — via the headless cds-maker-sdk. Runs an interactive, multi-turn authoring flow (env selection, jobs-to-be-done first, then design-only App Spec authoring across confirmed levels, guardrail lint, plan-mode approval, generate-pages, full build) and a narrated build, and can download a deployed app back into an editable spec to change it. Use when the user says "build an app for X", "create a model-driven app", "make me an app to manage Y", "add a business process flow", or "edit/add to my app". This skill stands alone and does not require /genpage — but for a standalone generative page added to an app that already exists, use /genpage instead.

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 premium hand-drawn architecture and process diagrams in the Lanshu animated GIF style, with editable .excalidraw files, static PNG previews, and genuinely animated GIFs with moving flow highlights. Use this skill whenever the user asks for 岚叔动态架构图, Excalidraw-like diagrams, DailyDoseOfDS-style black-background sketches, animated architecture/process GIFs, polished flowcharts, visual explanations of articles or system designs, or asks to replicate or improve a reference diagram with hand-drawn animated effects.

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。

Operations management across process optimization, efficiency, and continuous improvement. Use when designing workflows, building capacity plans, evaluating vendors, running Lean Six Sigma DMAIC projects, or optimizing cost-per-unit.

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。

Design a pedagogical documentation protocol for making children's learning processes visible and shareable. Use when documenting learning, communicating with families, or planning next steps.

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。

Design a worked example fading sequence from fully worked examples through to independent practice. Use when teaching procedures, algorithms, or multi-step processes to novice learners.

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。

Expert Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-processing.

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。

Expert blueprint for GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.

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。

oh-my-design 사용 중 불만·결함·아쉬운 점을 구조화된 GitHub 이슈로 접수하고, 쌓인 이슈를 배치로 처리한다. 사용자 모드('이슈 등록해줘', '이거 불편한데 신고할래', '버그 리포트', 'file an issue', '기능 요청')와 내부 도그푸딩 모드(스킬을 돌리다 미흡한 부분을 발견한 오케스트레이터·서브에이전트가 직접 접수) 둘 다 지원. '이슈 처리해줘', 'process feedback', '쌓인 피드백 정리'는 배치 처리 verb로 트리거.

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。

Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.

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Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.

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。

DO NOT USE when the target already consumes an injected interface or built-in abstraction such as IFileSystem or TimeProvider, even if the request says "generate a wrapper"; no new wrapper is needed. Use only when C# source calls an ambient/static dependency and no injectable seam exists: first-time TimeProvider, IHttpClientFactory, or System.IO.Abstractions adoption; minimal Environment/Console/Process wrappers; IProcessRunner; DI registration; or an ambient seam that preserves a static API. Exclude static detection (detect-static-dependencies), migration to an existing/registered abstraction (migrate-static-to-wrapper), one blocked behavior plus deterministic tests (testability-obstacle), and general interface design.

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。

Use when reviewing, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit

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Conducts retention impact assessments for new processing activities to determine appropriate data retention periods. Covers regulatory requirements scanning, proportionality review, purpose-based retention determination, and retention period documentation aligned with GDPR Article 5(1)(e) and Article 25 data protection by design. Activate for retention assessment, new processing retention, retention period determination queries.

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。

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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。

Design safe queue consumers and retries. Use when a mid-level developer needs reliable background processing.

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。

Establish design review gates with criteria, checklists, and approval workflows.

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。

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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。