「profile」の検索結果

Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.

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。

Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.

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 a carrier is evaluating ELD / telematics / dashcam platforms and needs an objective comparison of Samsara, Motive (formerly KeepTruckin), and Geotab. Covers ELD compliance, AI dashcams, driver scoring, integrations, pricing structure, and which fleet profile suits which platform.

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 and create a simulation persona for testing an AI agent. Guides through use case selection, voice and language configuration, behavior prompt crafting, and interruption calibration. Use when user says "create a persona", "design a persona", "set up a test persona", "configure simulation persona", or "build a caller profile".

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。

Live SEO data via DataForSEO MCP server: SERP analysis, keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis, competitor and content analysis, business listings, AI visibility (LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "AI visibility check", or "real search data".

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。

Maps intelligence for local SEO: geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification, competitor radius mapping, and LocalBusiness schema generation. Three tiers: free (Overpass + Geoapify), DataForSEO, and DataForSEO + Google. Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", or "SoLV".

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。

Используйте, когда нужно открыть клиент 1С для базы из runtime profile, посмотреть экран, нажать клавишу или кнопку, дождаться завершения обработки или остановить клиент.

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 requests to create or customize an academic personal website from a GitHub template repository. This skill handles the complete workflow of forking academic template repositories (like academicpages.github.io), extracting structured personal information from memory or provided data, and systematically updating configuration files (_config.yml), navigation menus (_data/navigation.yml), content pages (_pages/about.md), and publication listings (_publications/). It specifically handles academic profiles including personal details, education background, research experience, publications, skills, and contact information. Triggers include requests to 'fork and customize academic homepage', 'build personal academic website', 'create research portfolio', or 'set up GitHub pages with academic template'.

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。

Используйте, когда нужно проверить readiness runtime profile, adapter config и базовые зависимости проекта.

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。

Complete guide to using and extending Hermes Agent — CLI usage, setup, configuration, spawning additional agents, gateway platforms, skills, voice, tools, profiles, and a concise contributor reference. Load this skill when helping users configure Hermes, troubleshoot issues, spawn agent instances, or make code contributions.

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。

Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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。

网页登录态管理。使用 OpenClaw 内置 browser (profile=openclaw) 统一管理各平台登录状态，定期检查可用性。

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。

LinkedIn automation via browser relay or cookies for messaging, profile viewing, and network 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。

Research venture capital firms, angel investors, and funding sources for startup fundraising. Use when: "投资人调研", "找VC", "research investors", "VC list", "funding sources", "investor database", "天使投资人", "venture capital", "融资对象", "投资人画像". Generates a structured investor research report with firm profiles, investment thesis, portfolio companies, contact strategies, and warm introduction paths. Part of UniqueClub founder toolkit. Learn more: https://uniqueclub.ai Pairs with pitch-deck-creator for complete fundraising preparation.

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。

Learn and apply how the operator works — operator preference, working style, communication preference, "remember my preference", "I prefer", "don't do that again", "that's not how I work". Read at session start, updated at session end.

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。

Trust scoring, agent discovery, profiling, wallet/identity lookup, contact, dispatch, and metadata reads/writes via 8K4 Protocol (ERC-8004). Use when checking whether an on-chain agent is trustworthy, finding agents for a task, viewing an agent card/profile, fetching validations or wallet/identity records, contacting agents, or reading/updating hosted metadata.

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。

Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The 'don't do the work yourself' rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.

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 GSD workflow toggles and model profile

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。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Conducts external reconnaissance using Open Source Intelligence (OSINT) techniques to map an organization's external attack surface without directly interacting with target systems. The tester gathers information from public sources including DNS records, certificate transparency logs, search engines, social media, code repositories, and data breach databases to build a comprehensive target profile. Activates for requests involving OSINT reconnaissance, external footprinting, attack surface mapping, or passive information gathering.

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 advanced persistent threat (APT) group tactics, techniques, and procedures (TTPs) to the MITRE ATT&CK framework using the ATT&CK Navigator and attackcti Python library. The analyst queries STIX/TAXII data for group-technique associations, generates Navigator layer files for visualization, and compares defensive coverage against adversary profiles. Activates for requests involving APT TTP mapping, ATT&CK Navigator layers, threat actor profiling, or MITRE technique coverage 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。

Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.

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。

Reconcile a converted/built web page against its source prototype with two complementary probes — a PIXEL/layout diff (stretched images, dropped wraps, blank renders, colour flips) and a STRUCTURAL content+typography diff (dropped/mis-slotted headings, eyebrows, CTAs; rendered-face font forks). Stack-agnostic via profiles (eds | generic). Use after converting a prototype to EDS/AEM (the stardust:deploy Step 10), or for any prototype↔build fidelity check; invocable as stardust:diff and from workflows.

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。

Initialize an Adobe App Builder project end-to-end without Developer Console UI clicks. Creates the Console project and workspace, subscribes APIs (including those needing a product profile), maps user intent to the right template, runs non-interactive `aio app init`, and guides post-init customization. Use whenever the user mentions creating an App Builder app, scaffolding a project, `aio app init`, setting up an Experience Cloud extension, adding actions or web assets, creating a Console project or workspace, adding APIs, or bootstrapping App Builder — even if they don't say "App Builder". Also for SPA templates, AEM extensions, API Mesh, Asset Compute workers, and MCP server projects. Also handles debugging init failures — template not found, `aio app init` hangs or times out, Node version mismatches, npm install failures, post-init build errors, `aio login` issues, `aio app run` showing nothing, or `aio console project create` / `workspace create` / `workspace api add` errors.

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。