「rank」の検索結果

Suede AI findings-only code review with full context: changed files, callers, contracts, and deploy surface. Covers TypeScript, React, Next.js, database, Swift/iOS, OWASP, accessibility, SEO, observability, commit hygiene, and deploy risk, ranked P0-P3 with file:line evidence and a fix path. Use when asked to review a diff, PR, branch, or commit range, find the bugs before merge, check a change for security or accessibility problems, or judge whether a change is safe to deploy. Emits findings and a ship gate, not a grade. NOT FOR: the A-F letter grade alone (use suede-code-grader); findings plus grade in one pass (use suede-code); making CI enforce the result on every merge (use suede-ci-gate); root-causing a live bug or failing test (a private Suede Labs companion, not in this pack).

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。

Map meeting-specific power dynamics, influence ranking, and tactical recommendations.

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 Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph Analytics"). Covers the explore → prepare projection views → project-compute-write flow, the strict view/column type rules the graph engine requires, exact SQL CALL syntax, and privilege setup in both modes — app-identity grants and execute-as-user / per-user PAT auth (programmatic access token, set_enable_custom_credentials, register_user_role, caller grants). Does NOT cover Cypher or Neo4j DBMS queries — use neo4j-cypher-skill. Does NOT cover Aura Graph Analytics — use neo4j-aura-graph-analytics-skill. Does NOT cover self-managed GDS — use neo4j-gds-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。

Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher — covers graphdatascience client 2.x, GraphDataScience, gds.graph.project.native, gds.graph.project.cypher, snake_case endpoints, graph catalog operations, stream/stats/mutate/write modes, memory estimation, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and cleanup. Use for Aura Pro, self-managed, local, or offline Neo4j DBMS with the GDS plugin installed. Does NOT cover Aura Graph Analytics GDS Sessions, AuraGraphDataScience, GdsSessions, gds.graph.project.remote, or AuraDB Cypher API projection/session management — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver 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。

SEO + Generative Engine Optimization (GEO) for 2026 — technical SEO, on-page optimization, structured data (JSON-LD, schema.org), Core Web Vitals, citability scoring (llms.txt, entity clarity), AI search engine optimization (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews), and brand authority building. Use when user asks to optimize a site for search engines, improve SEO, write SEO content, implement structured data, optimize for AI search/GEO, audit a page, or improve Google rankings. Do NOT use for content writing strategy (use content-marketing), performance optimization beyond Core Web Vitals (use performance-profiler), or paid ad strategy.

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。

Find, rank, and safely remove accidental codebase complexity by proving real consumers, dynamic entrypoints, compatibility obligations, duplicate representations, speculative surfaces, and lifecycle ownership. Use when asked to simplify or clean up a repository, reclaim code entropy, reduce over-engineering or redundancy, find deletion candidates, collapse duplicate state/APIs, remove dead or added-then-abandoned code beyond static-tool output, or implement an evidence-backed simplification pass in any language or stack. Also trigger for 代码化简、熵回收、删代码、清理冗余、收敛抽象、去除过度设计. Do not use as a performance audit unless simplification is the stated goal.

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。

Investigates a bug, failing test, build error, regression, flaky behavior, performance symptom, or unexpected result by reproducing it, minimizing the case, testing ranked hypotheses, tracing the first incorrect state, and locking the root cause with a regression check. Use before proposing a fix. Not for planning a new feature or reviewing a healthy diff.

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 RAG retrieval for LLM grounding: chunking, BM25+vector hybrid, reranking, GraphRAG. Use when adding embeddings to SQLite FTS5 search or running held-out retrieval eval.

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。

Reinforces an AI agent by turning Monte Carlo's reinforcement loop diagnosis into code fixes. Reads the daily reinforcement loop report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-agent).

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 performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.

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。

Quantifying priority by scoring competing items with ICE/RICE/WSJF/MoSCoW/Cost of Delay/Kano. No code. Use to prioritize features/bugs/initiatives or arbitrate Must vs Should at MVP scoping.

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。

Navigating delivery status read-only: reconciles planned scope (specs/roadmap/PRD) against implemented code for what's built vs left. Not for priority scoring (Rank) or AC conformance (Attest).

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。

Optimizing SEO (meta/OGP/JSON-LD/headings), SMO (social sharing), CRO (CTA/form/exit-intent), and GEO (AI citation optimization). Use for search ranking, conversion, or AI visibility.

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。

Maximal Marginal Relevance rerank — get diverse top-k instead of redundant top-k. Use when standard search returns 5 near-duplicates, or when you want broader coverage of 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。

Hybrid search — BM25 keyword + dense vector fused with Reciprocal Rank Fusion. Use when queries have specific identifiers, code symbols, or proper nouns that pure semantic search might miss.

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。

RAG & Knowledge Architect: Retrieval systems: ingestion, chunking, embeddings, hybrid search, reranking, evals.

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。

Audits an existing lesson against UDL's three principles — engagement, representation, and action/expression. Identifies specific access barriers and suggests concrete modifications ranked by impact.

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。

Whole-repo audit for over-engineering. Like ponytail-review, but scans the entire codebase instead of a diff: a ranked list of what to delete, simplify, or replace with stdlib/native equivalents. Use when the user says "audit this codebase", "audit for over-engineering", "what can I delete from this repo", "find bloat", "ponytail-audit", or "/ponytail-audit". One-shot report, does not apply fixes.

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 audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve it.

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 TikTok ecommerce creator rankings by market and date range. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.

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。

Filter Temu stores by multiple dimensions (store name/ID, country site, backend category, fully-managed/semi-managed, total/weekly/monthly sales and revenue, rating, reviews, followers, product count, store opening time, etc.). Trigger when users mention Temu store, Temu store analysis, Temu seller, Temu store ranking, Temu semi-managed store, Temu revenue, temu stores, Temu store query. Even if the user does not mention the tool name, trigger this skill whenever they are looking for stores on Temu, filtering store data, or analyzing store performance.

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 stores with multi-dimensional filters (sales, favorites, reviews, store opening date, country, primary category, Raving/Star ratings). Trigger when user mentions Etsy store, Etsy shop search, Etsy seller, Etsy shop ranking, Etsy weekly sales stores, Etsy stores, or Etsy store query — even if the tool name is not mentioned, as long as the need is to find stores, filter store data, or analyze store performance 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。

Nexscope Ozon Russia single SKU daily time-series performance. Returns daily sales units, price, stock, rating, and optionally search position/visibility data for one Ozon product by date granularity. Use for validating growth trends, seasonality, and anomaly detection. Trigger when the user mentions Ozon trend, Ozon sales trend, Ozon price trend, Ozon daily data, Ozon stock trend, Ozon search ranking, Ozon product history, Nexscope trend, Ozon daily performance, Ozon time series, Ozon search visibility, Russian marketplace product history. Also trigger when the intent is to view the daily/period trend of an Ozon product, even without explicitly mentioning Nexscope.

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。