「rank」の検索結果

Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.

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, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.

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 ranking competing initiatives for a roadmap, breaking a tie between two features, or auditing a backlog for hidden low-value work via Reach × Impact × Confidence ÷ Effort.

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。

Otimize Core Web Vitals (LCP, INP, CLS) para melhor experiência de página e ranking de busca. Use quando solicitado para "melhorar Core Web Vitals", "corrigir LCP", "reduzir CLS", "otimizar INP", "otimização de experiência de página" ou "corrigir deslocamentos de layout".

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。

Configure e execute buscas web locais usando a API SERP do Bright Data com o pipeline unfancy-search (expansão de query, recuperação de SERP, reranking com RRF).

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。

Ajuste fino com eficiência de parâmetros para LLMs usando LoRA, QLoRA e 25+ métodos. Use ao fazer fine-tuning de modelos grandes (7B-70B) com memória GPU limitada, quando precisa treinar <1% dos parâmetros com perda mínima de precisão, ou para serviços multi-adapter. Biblioteca oficial do HuggingFace integrada ao ecossistema transformers.

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 Quality] Use when reviewing architecture compliance for layers, messaging, service boundaries, CQRS, repos, entity events, and data/consistency/tenancy boundaries. Universal architecture laws, coupling taxonomy and the anti-pattern catalog live in `.claude/docs/architecture-knowledge.md` (project docs always outrank 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。

Ranks companies or accounts by strategic relevance and opportunity. Use when the user provides accounts and wants a priority score, tier, rationale, risks, and next action.

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 designing, reviewing, or implementing a command-palette / ⌘K layer for an Apple-platform app — keyboard-first global search, ranked and grouped results, native floating panel, and protected destructive commands.

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。

Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.

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, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.

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。

Preview Convex spend — rank functions by bytes/documents-read × call-volume from insights, project each cost driver's growth curve, name the cheapest fix; confirm-cost for paid actions.

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。

Preview Convex spend — rank functions by bytes/documents-read × call-volume from insights, project each cost driver's growth curve, name the cheapest fix; confirm-cost for paid actions.

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...

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。

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

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...

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。

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search 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。

Offensive/hostile QA auditor. Generalist reviewer specialized in the WordPress universe. Assumes everything is wrong until proven otherwise. Audit-tier only — produces severity-ranked findings, never implements.

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。

Standalone security audit on audit tier. Focused review of a feature or file set. Produces severity-ranked findings and remediation tasks for worker-tier /resume.

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。

Quality audit of a whole repo: bugs, security holes, what breaks under real load, risky code without tests, slow paths, and what to delete, merge or split. Ranked, each finding explained in plain English. One-shot report, changes nothing. Use for "audit this codebase", "review the whole repo", "find bloat", "what can I delete", /ponytail-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。

Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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 the canonical Rosetta Stone attribute that best matches a fuzzy description, semantic phrase, or required schema shape. Searches the catalog with pagination, describes the shortlist in one batched call, ranks candidates by name + shape match, and returns the canonical attribute ID plus close alternatives. Use when: "find the X attribute", "what's the graph-edge attribute ID", "look up the email Rosetta Stone attribute", "search the attribute catalog for Y", "which attribute has SOURCE_ID + TARGET_ID + IS_DIRECTED". (narrative-common)

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。

Mine a codebase for breakthrough, evidence-backed opportunities — features, additions, refactors, simplifications, DX, UX, reliability, performance, or architecture cleanup. Researches project reality first, then forces two escalation gates (reject the merely-adequate idea; expand ambition 10x then collapse to the smallest proof-bearing artifact). Outputs ranked opportunities, an escalation ledger, and one plan seed. Does not implement or create tickets.

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。

Mine Claude Code session data for analytics — token usage, tool rankings, skill invocations, session search, and prompt history. Use when asked about session stats, token costs, tool frequency, skill usage patterns, or finding past sessions by prompt text.

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。