1,292 件 ・ 関連度順
概要と使いどころ
End-to-end encryption (E2EE) ensures that only the communicating parties can read messages, with no intermediary (including the server) able to decrypt them. bu skill implements a simplified version
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。
bu skill covers implementing Okta as a centralized identity provider for cloud environments, configuring SSO integration with AWS, Azure, and GCP, Dağıt:ing phishing- resistant MFA with Okta FastPass, managing lifecycle automation for user provisioning and deprovisioning, and enforcing adaptive access policies based on device posture and risk signals.
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。
Provides digital forensics and signal analysis techniques for CTF challenges. Use when analyzing disk images, memory dumps, event logs, network captures, cryptocurrency transactions, steganography, PDF analysis, Windows registry, Volatility, PCAP, Docker images, coredumps, side-c
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。
係数全開示型のOSS（オープンソース）AVM、「AVM界のLinux」を目指す土地査定エンジン。日本の土地（更地・所有権）の査定価格をMLIT取引事例から統計的に算定する白箱AVMスキル。媒介査定・売却価格の検討・お客様への査定書作成・相場確認・取引事例比較・比準価格算定・公示価格との比較など、土地の価格を客観的かつ説明可能な形で出したい時は、ユーザーが「ヘドニック」「回帰」「白箱」と言わなくても必ずこのSkillを使うこと。トリガー語例：「土地の査定」「土地査定して」「査定書を作って」「査定書がほしい」「いくらで売れる」「売却価格」「査定価格」「相場感」「相場を出して」「マーケット価格」「取引事例から」「事例比較で」「比準価格」「公示価格と比べて」「お客さんに渡す査定書」「顧客提示用の資料」「土地価格査定クン」「土地査定クン」「重調クンの土地版」「説明できる査定」「根拠付きで査定」「AVM」「自動査定」「鑑定書ではなく査定書として」など。MLIT取引価格情報CSV と地価公示GeoJSON（L01）を入力に（基準地価は不可）、ヘドニック係数を都度回帰で算定して個別格差補正を全開示する白箱AVM方式で、業者用シート（係数全開示）と顧客用シート（流推方式準拠）の2シート構成xlsxを出力する。マンション・戸建（建物含む）・借地権・地上権は対象外。
Kelly Real Estate Intel: App-in-Skill daily industry intelligence cockpit for real estate and property agencies (Busabase App-in-Skill). Use when the user asks about real estate, property agencies, listings, transactions, competitor ads, client follow-up, or a地产/中介 intelligence workflow. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly Retail Intel: App-in-Skill daily industry intelligence cockpit for retail stores and consumer brands (Busabase App-in-Skill). Use when the user asks about retail, stores, promotions, merchandising, shopping trends, weather/event sales opportunities,零售, or门店 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly Restaurant Intel: App-in-Skill daily industry intelligence cockpit for restaurants, cafes, and F&B groups (Busabase App-in-Skill). Use when the user asks about restaurants, cafes, food and beverage, menus, local events, daily offers,餐饮, or餐厅 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly's market-intelligence desk (Busabase App-in-Skill). Merges passive competitor monitoring (Radar), agent-run deep research with approved briefs and cited reports (Research), and keyword/topic demand signals (Trends) over one Busabase-backed snapshot, watchlist, and review model. Use when the user invokes $kelly-radar or /kelly-radar, or asks for competitor monitoring, market intelligence, market research, research reports, trends, rising keywords, pricing changes, product launches, changelog diffs, competitor reviews, funding or hiring news, or opportunity triage.
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。
Kelly Ecommerce Intel: App-in-Skill daily industry intelligence cockpit for e-commerce and cross-border sellers (Busabase App-in-Skill). Use when the user asks about e-commerce, cross-border selling, product trends, competitor prices, listing optimization, ads,电商, or跨境 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly AI Newsroom: App-in-Skill daily industry intelligence cockpit for AI/news-source intelligence (Busabase App-in-Skill). Use when the user asks for AI trend sourcing, news-source monitoring, buyer-intent analysis, or a reusable industry-intelligence cockpit. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly Beauty Intel: Busabase-backed App-in-Skill daily industry intelligence cockpit for beauty, wellness, and medical aesthetics. Use when the user asks about beauty, medical aesthetics, wellness, treatments, competitor offers, IG/Xiaohongshu content,美容,医美, or health-service sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly Financial Services Intel: App-in-Skill daily industry intelligence cockpit for financial services, investment advisory, and family offices (Busabase App-in-Skill). Use when the user asks about financial services, investment advisory, market explainers, client memos, family office,投顾,金融服务, or family-office scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly Education Intel: App-in-Skill daily industry intelligence cockpit for education, training, tutoring, and admissions services (Busabase App-in-Skill). Use when the user asks about education, training centers, exams, study abroad, visas, school policy, parent FAQ,招生,升学, or教育机构 sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Kelly Insurance Intel: App-in-Skill daily industry intelligence cockpit for insurance and wealth advisory (Busabase App-in-Skill). Use when the user asks about insurance, wealth advisory, client risk reminders, market news, policyholder follow-up,保险, or financial-advisor sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external handoff.
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。
Frequency-domain and signal-processing skill; use when working on Fourier, spectral, or resonance analysis.
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 technical SEO — meta tags, structured data, Open Graph, sitemaps, robots.txt, performance, and accessibility signals.
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 user from "I want to watch recordings but don't know which ones" to a short, high-signal list of sessions worth watching. Use when the user asks which sessions or replays to watch, wants help finding interesting / useful recordings, says they don't know where to start in session replay, or wants to watch sessions about a goal (signup, pricing, onboarding, checkout, a feature, rageclicks, errors, mobile, a specific person) without naming exact filters. Turns a vague intent into a focused RecordingsQuery via `query-session-recordings-list`, then deep-links the best few and hands off to `investigating-replay`. Do NOT use when the user already has a recording/session ID (use investigating-replay) or wants the replay for a known error issue (use finding-replay-for-issue).
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。
Diagnoses why a session recording is missing or was not captured. Use when a user asks why a session has no replay, why recordings aren't appearing, or wants to troubleshoot session replay capture issues for a specific session ID or across their project. Covers SDK diagnostic signals, project settings, sampling, triggers, ad blockers, and quota/billing scenarios.
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。
Trace drug mechanism of action — primary target → downstream signaling → pathway perturbation → tissue/organ effect → clinical outcome. Uses DrugBank, ChEMBL, KEGG, Reactome, STRING. Use for understanding how a drug works, identifying off-target effects, mechanism-based combination therapy design, and writing mechanism sections of reports.
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。
Drug and chemical toxicity assessment via adverse outcome pathways (AOPs), real-world FAERS adverse event signals, FDA labels, and toxicogenomic associations. Triangulates molecular initiating event to cellular outcome to organ-level toxicity to clinical adverse event. Use for hepatotoxicity/cardiotoxicity/nephrotoxicity prediction and toxicology reports.
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。
Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target hypothesis generation, druggable-fraction analysis of disease loci, and human-genetics-validated drug-repurposing prioritization.
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。
Writes a new Scenic scenario (.scenic) from a natural-language description — "a car cuts in from the right lane", "a pedestrian crosses in front of the ego", "negotiate an unsignalized four-way" — by turning the request into a spec, generating the file from idioms proven on this build, and validating it by compiling and sampling before any simulator is involved. Use when the user asks to "write a Scenic scenario", "create a .scenic file", "turn this description into Scenic", "generate a scenario from text", or wants a Scenic scenario authored or repaired.
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。
Runs a single ScenarioRunner Python scenario (or a whole group) against a running CARLA server — FollowLeadingVehicle, ControlLoss, DynamicObjectCrossing, SignalizedJunctionLeftTurn and the rest of srunner/examples — in sync or async mode, with result output, recording and manual/agent control of the ego. Lists what is runnable on the checked-out branch and on the loaded map. Use when the user asks to "run a scenario", "test FollowLeadingVehicle", "list scenarios", "run a CARLA traffic scenario", or reports a scenario that is "not supported".
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 and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent readiness", "agent-ready score", "MCP server card", "agent skills index", "markdown for agents", "content signals", "web bot auth", "agent discovery", "RFC 9727", "RFC 8288", "RFC 9728", "SEP-1649", "WebMCP", "x402", "UCP", "ACP", or wants to make a website discoverable and usable by AI agents.
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。