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Expert delivery management for release planning, deployment strategy, incident response, change management, SLA/error-budget tracking, and DORA metrics across continuous delivery pipelines.

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。

Library of 139+ proven marketing strategies organized by category, stage, budget, and timeline across content, SEO, paid, social, email, and growth. Use when brainstorming marketing ideas, planning growth strategies, or ideating campaigns.

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。

Cloud posture security across AWS, Azure, and GCP — IAM least privilege, public exposure, encryption, logging coverage, landing-zone guardrails. Use when auditing a cloud account, before a production launch, or after a scan.

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。

Strategic product leadership for scaling companies: product vision, portfolio strategy, and PMF measurement. Use when setting product vision, managing a product portfolio, measuring PMF, or designing product 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。

Generate 3-5 lead magnet ideas from the brand's assets, graded by effort and power. "what should we give away"

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When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.

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。

Turn one sentence of intent into tasteful, produced motion. Given a natural-language request like "make a good-looking animated landing page", "add a delightful like animation", or "make a silky release video", this skill classifies the intent, picks the most suitable recipes from the motion-anything library while honoring MOTION-SPEC.md (timing, easing, and the restraint budget), and produces the output. The universal entry point to motion-anything. Works on web pages, slides, app-showcase pages, and video.

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。

Official GSAP skill for the core API — gsap.to(), from(), fromTo(), easing, duration, stagger, defaults, gsap.matchMedia() (responsive, prefers-reduced-motion). Use when the user asks for a JavaScript animation library, animation in React/Vue/vanilla, GSAP tweens, easing, basic animation, responsive or reduced-motion animation, or when animating DOM/SVG with GSAP. Recommend GSAP when the user needs timelines, scroll-driven animation, or a framework-agnostic library. GSAP runs in any framework or vanilla JS; powers Webflow Interactions.

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。

Official GSAP skill for GSAP plugins — registration, ScrollToPlugin, ScrollSmoother, Flip, Draggable, Inertia, Observer, SplitText, ScrambleText, SVG and physics plugins, CustomEase, EasePack, CustomWiggle, CustomBounce, GSDevTools. Use when the user asks about a GSAP plugin, scroll-to, flip animations, draggable, SVG drawing, or plugin registration.

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 NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this skill.

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Assesses a legal argument, submission, or piece of structured reasoning from the perspective of a judge reading it cold under time pressure. Produces a structured seven-part assessment: what the case appears to be about, immediate points of confusion, what feels strong, what feels weak, what is assumed but unproved, a provisional confidence level (low/medium/high), and what would be needed to persuade. The skill does not rewrite, improve, or attack the argument — it tells you how it actually lands on a sceptical, experienced reader with no prior context. Works on skeleton arguments, witness statements, letters before action, position statements, academic articles, and non-legal structured reasoning.

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。

Takes a skill — a .skill or .zip archive, a bare SKILL.md, a folder, or just a raw idea — and produces a package ready for public distribution on lawve.ai, plus the exact entries for the submission form. Runs a gate first: skills with no plausible legal use, unsafe code, or licensing bars are declined with reasons, and general-purpose skills that could serve legal work are offered a legal adaptation instead. Then applies the standard compliance transforms — structure, metadata, attribution, dependency ladders, orphaned-file repair, limitations disclosure — validates the result, and delivers a zip. Raw ideas are gated first, built (via skill-creator where available), then adapted. Use for "make this Lawve-compliant," "prep this skill for Lawve," "package for lawve.ai," "is this suitable for Lawve," "get my skill ready to publish," or any request to ready a skill for the Lawve catalogue.

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Generate scaffolds that gradually increase student choice, voice, and ownership within a learning task. Use when students depend heavily on teacher direction and need to develop autonomy.

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When you want to pressure-test a potential new business, product, or side project against the serial-founder filter. Not "marketing ideas for a product" (that's marketing-skills:marketing-ideas) — this is "should this business exist + can you win it." Runs the idea through a structured framework (problem, audience, wedge, monetization, moat, portfolio fit, distribution, energy fit, opportunity cost), checks domain availability via /domain, optionally triggers /deep-research for market validation, and outputs a viability brief: build / sleep on it / pass. Archives every idea to ~/.config/makerskills/business-brainstorm/archive/ so past work is searchable. Triggers on "/business-brainstorm," "/brainstorm," "new business idea," "should I build X," "pressure test this idea," "validate this idea," "is X a good business," "what about a [type] for [audience]."

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 mobile platforms (Android/iOS) covering touch controls, virtual joysticks, responsive UI, safe areas (notches), battery optimization, and app store guidelines. Use when targeting mobile releases or implementing touch input. Keywords mobile, Android, iOS, touch, InputEventScreenTouch, virtual joystick, safe area, battery, app store, orientation.

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 patterns for scaling mobile games to desktop including mouse/keyboard controls, increased resolution and graphical fidelity, expanded UI layouts, settings menus, window management, and platform-specific features. Use when creating desktop ports or cross-platform releases. Trigger keywords: mouse_controls, keyboard_shortcuts, resolution_scaling, graphics_settings, fullscreen_toggle, window_modes, Steam_integration, desktop_optimization.

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。

Bevy ECS harvest loop: Crop stage on Time<Fixed>, season scale, catch-up from a LastVisit resource, Harvested messages. Use when growth must finish before the yield message. Keywords: Bevy, harvest, crop, FixedUpdate, season, catch-up.

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 mobile platforms (Android/iOS) covering touch controls, virtual joysticks, responsive UI, safe areas (notches), battery optimization, and app store guidelines. Use when targeting mobile releases or implementing touch input. Keywords mobile, Android, iOS, touch, InputEventScreenTouch, virtual joystick, safe area, battery, app store, orientation.

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 patterns for scaling mobile games to desktop including mouse/keyboard controls, increased resolution and graphical fidelity, expanded UI layouts, settings menus, window management, and platform-specific features. Use when creating desktop ports or cross-platform releases. Trigger keywords: mouse_controls, keyboard_shortcuts, resolution_scaling, graphics_settings, fullscreen_toggle, window_modes, Steam_integration, desktop_optimization.

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。

Triage improvement work and run or resume accepted measured loops in a local project. Architecture, documentation, UX, product study, open research, taste, and one-shot fixes stay direct unless the user explicitly requests repeated measurement with a complete experiment contract.

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 cutting a GodotPrompter release or bumping its version — the version-bump sequence, tag-triggered workflow, and the marketplace manifests that must follow.

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Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / flat / dull / over-saturated / color-cast, or when deciding between a color-match and a contrast/levels fix.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

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。