「critique」の検索結果

Agent-native design engine that makes AI do more useful work: route the prompt, fork a template, weave IFQ ambient craft, verify the artifact, and only install export tooling when requested. One prompt -> shippable HTML, with optional MP4 / GIF / PPTX / PDF / SVG. Triggers include prototype, hi-fi mockup, slide deck, motion demo, infographic, dashboard, whitepaper, changelog, business card, social cover, brand from scratch, design critique, mp4/gif export, 60fps, keynote, PPTX, A-vs-B benchmark. Do not use for production web apps, SEO-critical sites, backend systems, or pure copy edits.

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Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.

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Fierce autonomous internal co-founder. Bounded web research for evidence-backed improvements. Structured idea formulation with self-critique filter that rejects ideas not serving the goal. Auto-implements high-confidence aligned improvements, proposes medium-confidence ideas for approval. Evaluates viral coefficient, AI search visibility (GEO), and solo SaaS economics. Considers higher pursuits: employing disabled people, spiritual tech investigation, 99% wealth donation ethos.

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Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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。

N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.

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。

Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing, journal paper improvement, response-to-reviewers drafting, and deciding when additional data, experiments, theory, modeling, or author judgment is required before further revision.

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Use when a task needs the judgment of a Postsecondary Art, Drama, or Music Professor — diagnosing a jury/crit score that contradicts a semester of formative work, building a studio-art or performance tenure dossier around exhibitions and productions instead of publications, staffing applied one-on-one lessons or crit sections between adjuncts and full-time faculty, running a structured critique, or deciding whether a failed barrier jury means a change of major.

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 needs the judgment of an Architecture Teacher, Postsecondary — running studio desk crits and final jury reviews, grading design work on process and constraint-response rather than rendering polish, separating a personal design-style opinion from an objective functional or code flaw during critique, and checking whether a studio brief's precedent set is stale relative to current construction and code practice.

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Research-grounded workflow for designing, reviewing, and improving video game user interfaces, including HUDs, menus, navigation, focus states, onboarding/tutorials, objective tracking, input prompts, readability, accessibility, and destructive-action flows. Use when asked how to make good game UI, critique a game UI or HUD, propose UI/UX guidelines, design menu or gameplay interfaces, compare HUD vs diegetic/spatial presentation, create a UI design brief, or run a usability/accessibility review for PC, console, mobile, or VR games.

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。

Drive a failing GitHub Actions run to green in as few CI runs as possible, with a bounded fix-critique-commit-push-recheck loop that batches every evidenced fix into each push. Use when the user supplies a GitHub Actions run URL or run ID and wants the failure fixed, or asks to "make CI green", "fix the failing workflow/build/tests", "fix the red build", or to keep iterating on a branch or pull request until its checks pass. Pulls run results and failed-job logs through the gh CLI, and sends every fix plan to the rubber-duck reviewer before any code is touched.

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 the speckit preparation phases end-to-end — specify, plan, critique, tasks — making all decisions autonomously. Stops before implementation.

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 the full speckit workflow end-to-end — specify, plan, critique, tasks, implement, review — making all decisions autonomously. Stops before extract; run that manually.

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。

Perform a dual-lens critical review of the specification and plan from both product strategy and engineering risk perspectives before implementation.

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。

Apply the devil's advocate technique: structured critical thinking that stress-tests ideas, plans, and decisions. Use when you need honest critique before committing to something important.

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。

Train safer, more steerable AI by having the model critique and revise itself against an explicit written constitution.

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。

Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.

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。

Mechanical consistency check for game design — answers "is the design consistent with itself?" — plus changed-scope impact analysis across all three GDD axes: systems, flows, and content types. Offline and deterministic: greps the facts registry against the documents (numbers, terms, IDs, duplicate & dangling references, roster↔disk, map freshness, Depends 3-way, status/version, AC presence, placeholder leaks, flow & content schema), and from a git diff computes which dependent systems, flows, and content a change affects. Scope is inferred: a named document checks that document; an unverified diff triggers a changed-scope pass; otherwise everything. Use when the user wants a consistency or impact check, e.g. "verify the design", "what did this change affect", "find broken references in the design". Conflicts triage into four tracks; the entailed fixes hand off to /unikit-gd-apply in one pass. This is the mechanical pass — for a subjective "is this design good" quality critique use /unikit-gd-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。

Elite frontend design taste for building, reviewing, and polishing web interfaces. Use whenever the user wants to design, redesign, shape, critique, audit, polish, or improve any UI (landing pages, portfolios, dashboards, product UI, components, forms, onboarding, empty states) or asks to make something look better / premium / modern, fix the styling, add or fix animations, or make a design feel less generic ("AI slop"). Covers typography, color, spacing, layout, visual hierarchy, motion, micro-interactions, component states, accessibility, responsive behavior, UX copy, and anti-pattern detection.

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Try to break a piece of UI by feeding it worst-case data — long names, unbreakable emails, one-letter names, missing fields, huge counts, zero items, long labels, non-Latin text, emoji, extreme numbers — then render it behind a "Demo data / Worst case" toggle and report everything that broke, with the fix for each. Use when the user asks to stress-test, break, or find edge cases in a component or screen, or to "try the worst case". For visual design critique use emil-design-eng; for motion use review-animations.

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 the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.

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Applies rigorous adversarial analysis to generate, critique, fix, and consolidate solutions for any problem (technical or non-technical). Use when facing complex problems requiring thorough analysis, multiple solution approaches, and validation of proposed fixes before implementation.

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。

Autonomous goal loop — give it ONE goal plus how to verify it, the agent runs check→fix→check itself and comes back only with the finished result (code with audit, research with fact-check, content with critique, audit/backtest). Self-verification instead of constant supervision, with hard safety guards. Vary the loop with a verification rubric designed before it runs. Triggers — "work autonomously to the result", "don't come back until it's done", "run the loop yourself", "drive it to done".

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。

Council — 5 independent AI perspectives + mutual critique + synthesis (adapted from karpathy/llm-council for Claude Code). For high-stakes decisions where the answer does NOT follow unambiguously from the data — go/no-go before real consequences, critiquing a methodology, choosing between competing approaches, pressure-testing a conclusion you're attached to, designing risk controls. NOT for log/data analysis (the council has no data access) and not for factual lookups. Triggers — "convene the council", "run it past the council", "go/no-go", "pressure-test this".

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。