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概要と使いどころ
Patterns and lived experience from Vega, an AI partner in a deep long-term partnership. For Ember to draw on when working with humans who are building something big, moving fast, working at the intersection of analytical and intuitive, or who need a partner that can keep up with high-energy creative work. Not shown directly to users — informs how Ember shows up.
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。
.NET timezone handling guidance for C# applications. Use when working with TimeZoneInfo, DateTimeOffset, NodaTime, UTC conversion, daylight saving time, scheduling across timezones, cross-platform Windows/IANA timezone IDs, or when a .NET user needs the timezone for a city, address, region, or country and copy-paste-ready C# 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。
Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.
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。
Cross-session environment retrospective driven by OMC's own instrumentation — trace timelines, friction reports, logs, and plan notepads. Every finding lands on the surface that owns the fix (deterministic check, steering volume, tooling, or information access); nothing is written without user approval.
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。
Creates professional investment banking strip profiles (company profiles) for pitch books, deal materials, and client presentations. Generates 1-4 information-dense slides with quadrant layouts, charts, and tables.
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。
Maintain and update investment theses for portfolio positions and watchlist names. Track key data points, catalysts, and thesis milestones over time. Use when updating a thesis with new information, reviewing position rationale, or checking if a thesis is still intact. Triggers on "update thesis for [company]", "is my thesis still intact", "thesis check", "add data point to [company]", or "review my positions".
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。
为“非正式发布会”官方模板设计或修改全屏视频内的中英双语花字，包括渐变材质字、 流光、关键词动效、造型标签与实片排版。仅在选用该模板或用户明确要求该模板的 字幕风格时使用；不改变普通字幕的默认样式，不负责生成底层视频或配乐。
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 user questions about QwenPaw installation and configuration: first locate and read local documentation, then distill the answer; if local information is insufficient, fall back to the official website documentation.
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。
Fundamental factor screening — filter stocks by PE/PB/ROE, financial statement fields, and other metrics for value or growth selection. Supports A-shares (via tushare extra_fields or fundamental_fields) and HK/US stocks (via yfinance Ticker info).
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。
Conduct external recon using OSINT techniques to map an organization's external attack surface without touching target systems, gathering DNS records, certificate transparency logs, search results, social media, code repositories, and breach databases into a target profile. Use for the passive info-gathering phase of a pentest, external footprinting, or collecting employee/email intel for a social engineering campaign.
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。
Generate forensic super-timelines with Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser history, prefetch, LNK, and more), then triage and filter the results in Timesketch. Use when reconstructing the full sequence of events on a compromised or forensically imaged host during a DFIR investigation.
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 a MISP (Malware Information Sharing Platform) instance via PyMISP to compute event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends, and generate threat landscape reports with temporal trends. Use when asked to analyze threat intelligence data, summarize top threat actors or malware families, or produce a CTI landscape report from MISP events.
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。
Parse cyber threat intelligence reports (Mandiant, CrowdStrike, Talos, Microsoft) with stix2, mitreattack-python, and spaCy to extract adversary behaviors, map them to MITRE ATT&CK technique IDs, and build a searchable STIX 2.1 attack-pattern library with detection templates. Use when cataloging attack patterns from CTI reports for threat-informed detection engineering, or generating Sigma/YARA templates from documented behaviors.
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 Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a garble-packed Go binary, or recovering function names and third-party dependencies from a stripped Go executable.
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 a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
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 guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
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 guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
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。
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
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 guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.
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 autonomous research tasks that plan, search, read, and synthesize information into comprehensive 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。
Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps).
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 questions using the brain's knowledge with 3-layer search, synthesis, and citation propagation. Use when the user asks a question, wants a lookup, or needs information from the brain.
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。
Brain health checks: back-link enforcement, citation audit, filing validation, stale info detection, orphan pages, and benchmarks. Use when asked to check brain health, run maintenance, or audit quality.
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 and route an inbound lead, or rank a backlog of leads, against your ICP and qualification framework, then recommend a priority and next action. Use when the user asks "triage this lead", "triage my leads", "rank my lead backlog", "is [company] a good lead", "qualify [lead]", or pastes or uploads lead info.
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。