「step」の検索結果

Use when animating characters or objects in Blender through Python, a bouncing ball, walk or run cycle, jump, acting shot, lip sync, eye darts or facial animation, blocking in stepped keys, splining, polish, timing and spacing, arcs, overlap, squash and stretch, graph editor handles, slotted actions or NLA layers, staging a playblast, or when animation looks floaty, stiff, slides, pops, chatters, the subject is too small in frame, or a knee pops and needs an expert review.

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 a task involves running a Scenario workflow through MCP, including anything the user calls a Scenario app, a saved pipeline, or a multi-step generation graph. Triggers include listing workflows, building workflow_run's inputs object, pricing a run with a dry run, approving or rejecting a stuck approval node, a run rejected for input length, or a workflows_list reply flooding the context. Creating or editing graphs is scenario-workflow-authoring. Keywords: workflow, app, approval gate.

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 animating a character or prop in Maya through Python: a walk or run cycle, a jump or landing, a heavy lift, throw or punch, an acting or dialogue shot, lip sync, eye darts and blinks, stepped blocking, splining, polish, Graph Editor tangents, animation layers, mocap cleanup or retargeting, game clips and root motion, or reviewing a playblast. Also when motion looks floaty, weightless, mushy, poppy, posey, sliding or chattery, or feet slap or skate.

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 turning a script or story into previs, layout, storyboards or an animatic in Blender: beats to shot list, camera and lens per shot, staging, camera height, 180-degree rule, screen direction, shot sizes, when to cut, camera moves, timing stills in the Video Sequencer, markers bound to cameras, Story Tools, Grease Pencil or 2D/3D hybrid boards. Also when an animatic shows the wrong frame, blocking keys are not stepped, Rigify operators are missing, or viewport render fails headless.

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。

Capture a hard-won "golden path" from the current session as a reusable Agent Skill, so future sessions start already knowing it. Use it (1) right after non-trivial debugging, after working out a multi-step operational workflow, or after rediscovering project facts you didn't know up front — e.g. how to reach the dev/prod database, where credentials and env vars live, how to deploy, run migrations, or verify a change live; and (2) whenever the user says "remember this", "save this as a skill", "make a skill for this", "don't make me re-explain this next time", or otherwise wants a workflow preserved across sessions. Proactively recognize the moment even when unprompted: if a task took several attempts before it worked, used non-obvious tooling, or is likely to recur, harvest it without asking first. Delegates to a subagent when your tool supports one, or works inline, to extract the proven procedure into a new project-local or global skill.

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。

Designing quizzes, personality assessments, and recommendation tools that segment users into actionable categories rather than generating clicks for clicks' sake. Question architecture, scoring algorithms, result categorization, recommendation mapping, lead capture integration. Honest about clickbait-quiz (engagement only), vanity-result (entertaining, not useful), and actionable-segmentation (genuine categorization that drives next-step recommendations) patterns. Triggers on quiz, assessment, personality test, recommendation tool, scorecard, diagnostic, fit evaluator, what-type-of-X-are-you, persona quiz. Also triggers when an audience needs a categorization-driven lead magnet, when a vanity quiz is producing engagement but no qualified leads, or when an assessment is being scoped for the first time.

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。

Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.

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。

Designing side-by-side comparison tools (plan-compare, product-compare, alternative-compare) that help users decide rather than just listing features. Axis selection, default-comparison logic, recommendation discipline. Honest about feature-list-dump (every feature in a row, no decision support), hidden-recommendation (biased comparison pretending to be neutral), and honest-comparison-with-guidance (genuine comparison plus opinionated recommendation) patterns. Triggers on comparison tool, plan compare, product compare, alternative compare, vs page, decision support tool. Also triggers when conversion through comparison stages is poor, when users are abandoning at the comparison step, or when a comparison tool is being scoped for the first time.

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。

NIST SP 800-53 Rev 5 compliance advisor — all 20 control families (AC, AT, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PM, PS, PT, RA, SA, SC, SI, SR), Low/Moderate/High baseline selection, FIPS 199/200 system categorization, control tailoring and overlays, privacy controls (PT family), supply chain risk management (SR family), assessment procedures (SP 800-53A), OSCAL, RMF integration (SP 800-37), and mapping to FedRAMP, FISMA, CMMC 2.0, and ISO 27001. Use for any federal system security controls, FISMA compliance, RMF step guidance, control narrative writing, or baseline tailoring question.

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。

Reconstruct a readable parametric ForgeCAD model from an existing CAD or mesh file such as STL, OBJ, 3MF, STEP, or STP.

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。

Trace bugs and manual fixes back to kits and prompts; fix at the source so the iteration loop can reproduce the fix autonomously. Six-step revision process plus the single-failure backpropagation protocol. Use when a manual hot-fix has been applied, when convergence stalls, or when the same class of bug keeps reappearing.

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。

Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.

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。

Merge PR, wait for deploy, verify production health. 10-step workflow: pre-flight, CI check, readiness gate, merge (respects merge queue), detect platform, wait for deploy, canary verification, revert option, deploy report. Auto-detects Fly, Render, Vercel, Netlify, Heroku, GitHub Actions.

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。

Report-only QA — same browser-based testing as qa but documents bugs with screenshots and repro steps only, no code changes. Health score formula weighted across 8 categories. Outputs to .gstack/qa-reports/.

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, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin 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。

Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.

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。

Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deployment.

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 a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.

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。

Generate operational runbooks from codebase analysis covering deployment, incident response, scaling, and monitoring, with copy-paste commands and rollback steps. Use when bootstrapping ops docs, preparing for on-call, or post-incident.

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。

Form optimization for lead capture, contact, demo request, application, and checkout forms. Covers field-cost analysis, multi-step form design, validation UX, mobile optimization, and A/B testing 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。

Signup and registration flow optimization covering SSO strategy, progressive profiling, field reduction, multi-step flow design, authentication UX, post-submit experience, and mobile registration patterns.

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。

Import agency procedures as role-assigned SOP steps with approval gates. "import our launch checklist"

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。

Connect a known MCP integration: check the registry and status, return exact setup steps. "connect Google Ads"

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。

Enforces staged execution discipline on large tasks: a written stage plan, parallel delegation where the runtime supports it, a failable verification check at each stage, and a skeptical self-review before delivery. Trigger when the user explicitly asks OR when the task objectively spans multiple files, sources, or sessions. Do NOT trigger on ordinary multi-step requests that a direct attempt handles fine (autonomous triggering only; explicit invocation always runs).

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。