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概要と使いどころ
Extract and analyze product reviews from e-commerce and review sites. Aggregates ratings, identifies common themes in positive and negative feedback, and surfaces actionable product insights from customer voices across platforms.
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。
Research companies with employee reviews, product reviews, industry classification, and competitive landscape through Akta. Covers company enrichment, employee satisfaction signals, product feedback, industry mapping, and news monitoring.
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。
Discover evidence-backed customer language, pain points, objections, and buying triggers from relevant Reddit communities. Use when asked for Reddit research, voice-of-customer analysis, audience pain points, product feedback, or community-led content opportunities.
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 Grok 4.5 as the required external design advisor when a task needs UI critique, UX critique, component-choice review, interaction-flow review, design imagery markdown, art direction, visual hierarchy judgment, design-system fit, color/type/layout suggestions, HTML mockups, SVG icons, handwritten SVG wordmarks, or file-based feedback on existing UI. Trigger on requests like "design a page", "generate design imagery", "give design advice", "optimize this UI", "improve UX", "optimize user flow", "review interaction", "check usability", "reduce friction", "choose the right component", "review this design", "create an icon", "suggest colors", "UI mockup", "visual direction", "lettering", "wordmark", "handwritten logo", or when another agent needs a second opinion on visual or interaction quality.
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。
Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.
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 the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.
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。
Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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。
Reviewer role. Opens rendered HTML, inspects every slide visually, fixes problems in custom.css or slides.html and rebuilds through build-html.sh. Use whenever the user says "review", "QA", "check slides", "inspect", "audit", "proofread", or wants feedback on a rendered deck.
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 MATLAB Grader assessment items that are suitability-gated, profile-driven, feedback-aware, and validated through MATLAB MCP. Produces Script, Function, Class Definition, Class Inheritance, Object Usage, and Class Methods items with MATLAB Grader assessment setup instructions.
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 learner asks for help with MATLAB homework, labs, projects, graded assignments, take-home exams, quizzes, or any programming task where academic integrity, course policy, or instructor constraints may limit direct solutions. Use to provide policy-aware hints, conceptual coaching, partial feedback, and assignment-safe MATLAB tutoring.
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。
Resolve the currently-unresolved review threads on the current branch's PR in one sweep — assess each, fix it (test-first, verified) or reply-only, then reply and resolve — and return a summary. Use standalone to address PR review feedback, or as the thread-handling step invoked by /watch-pr. Repo is adamayoung/TMDb.
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。
Adversarially review the current implementation plan with three independent critic subagents, reconcile their findings into a consensus, and apply the agreed feedback to the plan. Use after drafting a plan (in plan mode or a plan/design doc) and before starting implementation, or whenever the user asks to pressure-test, critique, or harden a plan.
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。
Edits existing marketing copy in ordered passes (structure, claims, sentences, consistency, proof) and returns the edited text with a change log and a list of questions for the author. Flags claims that need evidence instead of inventing proof, and keeps the writer's voice. Use when someone says "edit this copy", "proofread this page", "tighten this", "polish this email", "review my landing page copy", "make this clearer", "check this before we publish", or asks for copy feedback on a page, ad, email or product description.
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。
Guides a founder from real decisions to a short set of written company values that people can act on: collects incidents, tests each candidate value, writes it as a trade-off with behaviors and a story, and wires it into hiring, feedback and promotion. Use when someone asks to "define our company values", "write a values document", "our values are generic, fix them", "turn our values into interview questions", "what should our culture be before we hire", or wants to check whether the team lives the values it has. Produces VALUES.md, an evidence log and an interview kit.
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。
Memory systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.
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。
Patterns for feedback control systems including PID tuning, state-space control, Model Predictive Control (MPC), trajectory tracking, and stability analysis. Covers both classical and modern control approaches for robotics and automation. Use when ", " mentioned.
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。
Code review specialist for quality standards, design patterns, security review, and constructive feedbackUse when "code review, pull request, PR review, code quality, refactor, technical debt, design pattern, best practice, code-review, quality, patterns, security, refactoring, best-practices, pull-request, review, ml-memory" mentioned.
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。
The fusion of trend prediction and AI content generation: detecting emerging trends before they peak and generating content that rides—or creates—the wave. This is marketing alchemy, transmuting data signals into viral gold. Traditional trend marketing is reactive: see trend, create content, hope you're not too late. AI Trend Alchemy is predictive: detect signal, generate content, be there as the wave crests. The combination of AI trend detection and AI content generation creates a feedback loop that can outpace human competition. This skill combines data science intuition with creative generation, reading the digital tea leaves while simultaneously conjuring the content that captures the moment. It's part analyst, part creator, part prophet. Use when "trend, trending, viral, predict, emerging, what's next, cultural moment, before it peaks, early signal, catch the wave, trend prediction, going viral, trend, prediction, viral, cultural, emerging, timing, alchemy, detection" mentioned.
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。
Specify micro-interactions with trigger, rules, feedback, and loop/mode definitions.
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。
When the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For post-signup activation, see onboarding.
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。
Facilitate structured design critiques with clear feedback frameworks and actionable outcomes.
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 asked to take over, adopt, or babysit a GitHub pull request until it merges — read review feedback, fix it, resolve threads, dismiss unreasonable change requests, and merge once approved and green. Picks between driving the PR live in this session or handing it to the Looper daemon for unattended background runs, confirming with the user when unclear. Triggers on "take over this PR", "接管这个 PR", "持续修复 review 直到合并", or a Looper takeover bot comment. Works in any coding agent (Claude Code, Codex, opencode, Gemini, …) using gh + git.
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。
Edit drafts into natural prose, inspect AI-sounding patterns, or review how a specified audience might respond passage by passage. Zero Slop runs inside the user's existing AI assistant with local tools that protect source details. Use for humanizing or de-slopping writing, polishing outward-facing prose, social drafts, final editorial checks, or an explicit simulated reader review. Preserve facts, voice and format; reader simulations are hypotheses, not human feedback.
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 asks to audit what's wrong with a project, "make it right", "看看项目出了什么问题", "为什么用户的需求还没上线", "为什么没提交App Store", "为什么没新build", or wants a holistic state-of-the-project check covering unmerged branches, stalled PRs, failed GitHub Actions, stale builds, plan drift (TODOS.md / ROADMAP), unreleased commits, and log errors. Runs read-only investigation, presents a grouped checklist, fixes only after explicit user confirmation. Aware of the Cathier iOS app workflow (Xcode + fastlane + auto-merge @claude PRs from in-app feedback).
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。