「aria」の検索結果

Fetch customer reviews for a Taobao or Tmall product by itemId, returning reviewer name, date, purchased variant, review text, and photo URLs. Use when user asks to get product reviews from Taobao, scrape Taobao customer feedback, extract buyer reviews by item ID, collect Tmall ratings and comments, 采集淘宝商品评价, 抓取淘宝买家评论, 获取淘宝商品评论, 天猫商品评价抓取, 按商品ID获取评价. Also applies to sentiment analysis of product reviews, building review datasets, and monitoring product rating changes.

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。

Walmart product reviews scraper: given a walmart.com product item ID, navigate to the reviews page and extract paginated customer reviews including reviewId, rating, title, review text, author nickname, submission date, verified purchase status, helpful votes, variant selected (color/size), badges, fulfilled by, seller name, and photo count. Use when user mentions walmart reviews, walmart product reviews, walmart customer reviews, scrape walmart reviews, extract walmart reviews, walmart review scraper, walmart ratings and reviews, walmart review data, walmart review text, walmart review pagination, get reviews from walmart, walmart review collector, walmart verified purchase reviews, walmart review analysis, walmart sentiment analysis, walmart review export, walmart buyer feedback. Also applies to bulk collection of walmart product reviews across multiple items, sentiment analysis on walmart reviews for market research, competitor product review benchmarking on walmart, and monitoring new walmart reviews over 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。

Frontend design skill fused from Impeccable + custom extensions. Covers design philosophy, anti-AI-slop patterns, typography, color (OKLCH), spatial design, motion, interaction, responsive, UX writing, state management, engineering, and 4 style variants. Includes 20 command skills for audit/critique/polish/animate/etc.

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 organization patterns, file structure guidelines, WASM build variants, and string processing conventions for gh-aw Go code.

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。

Helps users configure mirrord.json for database branching, enabling isolated database copies for safe development and testing. Use when the user wants to set up MySQL, MariaDB, PostgreSQL, MSSQL, MongoDB, Redis, DynamoDB, ClickHouse, Google Spanner, Amazon S3, turbopuffer, or generic branches, configure copy modes, connection sources, schema migrations, IAM authentication, or manage database branches.

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。

Scan a codebase for missing, janky, or inconsistent UI motion and fix it with transitions.dev recipes. ALWAYS use this skill when the user's message is or contains a transitions-agent command (npx transitions-agent, npx transitions-agent fix, any variant) - the command is a request for the full guided flow, not just command execution. Also use when the user asks about UI transitions, animations, motion quality, a motion score, janky or missing transitions, "polish the motion", "fix the animations", or whether the motion is good after UI changes.

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。

Hunting skill for csrf vulnerabilities. Built from 15 public bug bounty reports including modern variants — SameSite=Lax sibling-subdomain bypass (Argo CD CVE-2024-22424), GraphQL mutations-via-GET (GitLab $3,370), framework-wide CSRF middleware disabled (Stripe Dashboard $5,000), path-traversal CSRF-token bypass (GitHub Enterprise CVE-2022-23732 $10k), Origin-omission bypass (TikTok $2,500), OAuth-state null-byte (Streamlabs), WebSocket CSRF / CSWSH (Coda), default-SameSite email-change → ATO (YoYo Games $400), social-account-link CSRF (HackerOne), JSON-CSRF via text/plain on email-change (TikTok $500). Use when hunting modern CSRF — heavy emphasis on chain-to-ATO 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。

Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.

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。

Hunt Node.js specific vulnerabilities — Prototype Pollution → RCE chains (lodash/merge/assign), Express trust proxy misconfiguration, child_process/eval injection, template engine SSTI (EJS/Pug/Handlebars), path traversal in file servers, require() injection, environment variable exfil via /proc/self/environ. Use when target runs Node.js/Express/Fastify/NestJS/Koa.

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。

Chinese-first academic Word and PowerPoint workflow for paper reading reports, thesis or group-meeting PPTs, editable DOCX/PPTX generation, Office file inspection, template matching, speaker notes, and layout quality checks. Use when the user asks to read papers into Word reports, create or polish PPT/PPTX, convert paper/thesis materials into slides, edit DOCX/PPTX, inspect Office files, or produce Chinese academic presentation/report deliverables. Preserve English paper titles, formulas, variable names, software commands, and references.

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。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

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 this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

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。

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring regulatory or coding regions across species.

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。

Compose one publication-grade multi-panel figure. Start from a one-line claim plus immutable data Artifact Version references, or inspect an existing figure and draft its outline directly. Plan a 12-column panel outline, delegate one worker per panel, compose and inspect the result, then run at most three adversarial review rounds while regenerating only affected panels. For a standalone plot use `figure-style`; for whole-paper figure ordering use `paper-narrative`.

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。

Prepare reproducible setup instructions and validate a user-managed named software environment on an Open-Science SSH Compute Host, including direct SSH and Slurm hosts. Use when a remote job needs packages, modules, cache variables, or a repeatable activation that the host does not already provide.

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。

Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 20 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time-anchor, curiosity-gap, contrarian, comment-gate, emotional cold-open, named-gratitude, and 11 more), why it worked, and a blank template. Use to learn from a competitor's post, not to write your own (use linkedin-post-writer).

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。

Draft a LinkedIn comment on someone else's post from its URL, or reshare (repost) it to your feed with optional commentary. Use when the user pastes a post URL and asks to comment, engage, be first commenter, or repost with their thoughts. Produces 1-3 variants in the user's voice, picks a reaction, and publishes via Publora on approval. Not for replying to existing comments (use linkedin-reply-handler).

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。

Draft a new LinkedIn post from scratch using one of 20 2026 hook formulas (anaphora, R.I.P., time-anchor, curiosity-gap, contrarian, controlled A/B, false-binary, and more) plus a founders-edition angle library, picked by engagement goal (comments, reposts, likes, saves). Runs the humanizer pass and schedules via Publora on approval. Use to write a post, find a hook or proven format, or get founder-specific angles. Not for reviewing existing drafts (use linkedin-humanizer --mode audit).

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。

Structured literature review using a parallel fleet of Librarian agents. Searches top journals, working paper repositories (NBER, SSRN, IZA), and traces citation chains from key papers. Make sure to use this skill whenever the user wants to survey existing research on a topic — not to find datasets or write a paper. Triggers include: "review the literature", "find related papers", "what's been done on X", "search for papers on", "do a lit review", "find papers about", "what papers should I cite", "who has written about this", "survey the literature", "find prior work on", or any request to locate and summarize academic publications on a topic.

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。

Divergent research ideation — generate many candidate directions, then adversarially filter to the strongest

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。

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

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。

Econometrics skill for descriptive statistics and summary tables. Activates when the user asks about: "descriptive statistics", "summary statistics", "summary table", "Table 1", "balance table", "means and standard deviations", "correlation matrix", "data summary", "sample characteristics", "variable distributions", "描述性统计", "描述统计", "汇总统计", "统计表", "均值标准差", "平衡性检验", "相关矩阵", "样本特征", "变量分布"

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。

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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。

Document datasets, variables, sources, and merge keys for replication

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。