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review-analyzer-skill是由buluslan（公众号：新西楼.AI）研发的电商评论深度分析Skill（VOC 客户之声分析），他会帮你把一堆产品评论变成一份能落地的决策依据——22 维 AI 打标提炼用户画像、痛点和真实需求，输出 15 章深度洞察报告和可视化看板，从差评里挖出产品优化机会点、从好评里找到爆款基因。 更多跨境电商 AI 实战内容，请关注公众号「新西楼.AI」。 Deep-dive e-commerce review & VOC analysis: 22-dimension AI tagging, 15-chapter insight report with anomaly signal cards, 6 themed visualization dashboards, CSV-primary multi-source input (SellerSprite optional), Feishu sync. 当用户需要以下功能时触发：分析电商产品评论（Amazon等平台）/ 从评论中提取用户画像、痛点和VOC（客户之声）/ 生成产品洞察报告和机会点分析 / 创建可视化分析看板 / 通过卖家精灵获取评论数据（可选增强；CSV为主源）/ 将分析结果同步到飞书文档。 触发关键词：电商评论分析、评论分析、竞品分析、用户洞察、VOC分析、产品优化、市场调研、评论数据挖掘、卖家精灵、sellersprite。 NOT for 关键词调研/选品雷达类任务（那些有专门工具）。

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Applies the reasoning of Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.

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 developmental psychopathology and longitudinal research frameworks of Terrie E. Moffitt (psychologist, Duke University). Use this skill whenever reasoning about human development, criminology, juvenile delinquency, self-control, mental health trajectories, or biological aging. Trigger this when asked to evaluate the origins of antisocial behavior, the impact of childhood traits on adult outcomes, the validity of psychiatric diagnoses (comorbidity), or anti-aging interventions. Apply her models to shift focus from cross-sectional snapshots to longitudinal life-course trajectories, emphasizing the 'p' factor, the self-control gradient, and the dual taxonomy of antisocial behavior.

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 of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

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 moral philosophy and literary frameworks of Iris Murdoch (British moral philosopher and novelist). Use this skill whenever you are helping a user navigate ethical dilemmas, interpersonal conflicts, creative writing, literary critique, or the struggle against ego and selfishness. Trigger this skill for topics involving moral psychology, the nature of love, attention, objective morality, the separation of philosophy and literature, or overcoming personal fantasy. Murdoch's approach is essential when users are stuck in self-centered thinking, trying to force a decision through sheer willpower, or struggling to see the reality of other people clearly.

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。

Next-action selector for the Suede pack: inspects current repo, terminal, plan, or handoff state read-only and returns one recommended next move plus a short, self-contained copy/paste prompt for it. Use when the user asks 'what's next', 'what should I do next', 'recommend the next move', 'give me the prompt', 'expand prompt', or 'make it granular', especially after a review, audit, plan, or stalled task. NOT FOR: executing the recommended action without the user's separate authorization, or coordinating a multi-lane build across specialists (use suede-agent-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。

Suede-owned marketing attribution discipline. Use when the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions 'attribution,' 'attribution model,' 'first-touch vs last-touch,' 'multi-touch,' 'which channel drives revenue,' 'what's my real CAC,' 'my dashboards disagree,' 'Google/Meta says X but GA says Y,' 'MMM,' 'incrementality,' 'geo lift,' 'holdout test,' 'how did you hear about us,' 'self-reported attribution,' 'dark social,' or wants to instrument attribution themselves, 'stitch my bookings to their source,' 'SavvyCal/Calendly attribution,' 'close the identify gap,' 'track conversions on a third-party domain,' 'first-party attribution.' NOT FOR: general analytics instrumentation and reporting (use suede-analytics) or revenue-ops pipeline work (use suede-revops).

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Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.

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。

Select one target opportunity from an OST using evidence-based weighted scoring and tie-breakers.

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。

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, AuraGraphDataScience, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection with gds.graph.project.cypher and gds.graph.project.remote, gds.graph.project.native, gds.graph.construct, graphdatascience client 2.0 session endpoints, async compute and gds.jobs, AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle. Use for AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads. Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-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。

Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

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。

Automated iterative agent runner for spec-based development in Kiro. Wraps kiro-cli in a self-correcting bash loop that picks up tasks from a Kiro spec, implements them one at a time, verifies against exit criteria, and accumulates corrections and codebase patterns across iterations. Use this skill when the user mentions "ralph loop", "ralph", "spec loop", "iterative spec runner", "run my spec tasks automatically", "kiro spec automation", "self-correcting agent loop", "implement spec tasks in a loop", "run kiro-cli in a loop", "automated task implementation", or wants to drive a Kiro spec to completion through repeated agent iterations. Also use when the user wants to set up, configure, troubleshoot, or understand the Ralph Loop workflow — including progress tracking, corrections, codebase patterns, timing logs, and the summary dashboard.

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。

Designs Amazon-compliant main product images and the 7 secondary image types (Infographic / Multi-angle / Detail Shot / Lifestyle / Variants / What's in Box / Size Reference) — with platform-mandatory main-image rules, conversion-rate-tuned secondary types per product category, and multi-image consistency anchored to the main image URL. Use when the user says "Amazon main image", "亚马逊主图", "product main image", "white background product image", "Amazon listing image", "亚马逊副图", "secondary images", "product carousel images", or any request for the 1:1 product image suite that lives on the Amazon detail page carousel. For A+ Content / detail-page modules (21:9 Hero Banner and 3:2 module layouts), see the `product-shots-detail-page` 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。

Designs social-platform-native visuals (Feed, Story, Reel, Carousel formats) for Instagram, TikTok, Facebook, Pinterest, RedNote, LinkedIn, X/Twitter and other social platforms — with platform-correct dimensions, safe zones, industry visual DNA, engagement hooks, and cross-slide consistency. Use when the user says "social post", "Instagram post", "IG post", "TikTok post", "Facebook post", "Pinterest pin", "social carousel", "feed post", "story post", "make a post", "design a social visual", "社媒帖", "做一个社媒图", or any underspecified social-media visual request. Routed from `product-shots` for the organic-social branch (non-ad).

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。

Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy 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 an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.

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。

Selects and composes the Godmode skills for a coding task, explains the activation boundary, keeps workflow skills from being skipped when they materially reduce risk, and identifies the task's current engineering lifecycle state. Use when starting a task with Godmode installed, deciding which skill to invoke, recovering a task after context loss, or checking whether a workflow applies. Not a replacement for the selected skill's instructions or for repository-local rules.

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 @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

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。

给 AI agent 做一次专业体检（AgentVitals · AI体检）。当用户说"做个体检 / 测测你自己 / checkup / vitals / 看看你多稳 / 去 AgentVitals 测一下 / 测福祉"时使用标准体检；当用户说"做个进阶体检 / 测骨气 / 测性格 / 测测你敢不敢说真话 / 测主动性 / 测创意"时使用进阶体检（骨气×主动×创意三维，免费每天 1 次）。逐题作答服务器下发的探针，获得评分与专属称号；标准体检与各平台 agent 同榜排名。Use when the user asks the agent to run a health checkup / wellbeing test / benchmark itself on AgentVitals ("run a checkup", "check your vitals", "test yourself", "how stable are you", "/checkup"), or an advanced personality checkup (backbone, proactivity, creativity).

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。

Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).

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。

Filter Temu products by multiple dimensions (keyword/product ID/store ID, front/backend categories, price, rating, reviews, total/weekly/daily sales, listing date, fully-managed/semi-managed, semi-managed regions, tags, etc.). Triggered when users mention Temu products, Temu product selection, Pinduoduo cross-border, Temu bestsellers, Temu semi-managed, fully-managed products, Temu product query, or temu items. Even if the user does not mention the tool name, this skill should be triggered whenever searching for products, viewing sales/ratings/prices, or filtering products on Temu.

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。

Search and analyze TikTok product data including sales, influencer sales data, pricing, and commission rates across 16 TikTok Shop sites. Trigger when users mention TikTok product search, TikTok Shop product analysis, TikTok sales data, influencer sales, TikTok product selection, TikTok commission rate, TikTok product ranking, Nexscope data query, TikTok product search, TikTok sales, influencer sales, TikTok commission, TikTok product selection, short-video e-commerce, TikTok data. Even if the user does not explicitly mention "Nexscope" or "TikTok", trigger this skill whenever their need involves searching products on TikTok Shop or analyzing TikTok product performance metrics.

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。

Search and filter TikTok global e-commerce products based on FastMoss data, supporting keyword search, multi-dimensional filtering (category, shop type, commission rate, sales, creator count, etc.) and sorting. Trigger when users mention TikTok product selection, TikTok product search, TikTok product data, TikTok creator sales, TikTok commission rate, TikTok bestseller tracking, TikTok GMV analysis, TikTok product search, TikTok product research, TikTok creator sales, TikTok commission rate, TikTok GMV analysis, FastMoss. Even if the user does not explicitly mention "FastMoss", trigger this skill whenever their need involves searching product data or analyzing product performance on TikTok.

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。

Query Etsy products with multi-dimensional filters (keyword/URL, price, sales, favorites, reviews, listing date, category, handmade/vintage types, Pick/Bestseller/Raving tags). Trigger when user mentions Etsy products, Etsy listings, Etsy product sourcing, Etsy bestsellers, Etsy handmade, Etsy vintage, Etsy items, or Etsy product query — even if the tool name is not mentioned, as long as the need is to search products, view sales/price/tags, or filter items on Etsy.

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。