「termine」の検索結果

Determines whether a technology qualifies as an AI system under Art. 3(1) of the EU AI Act and classifies its risk tier (prohibited, high-risk, GPAI with systemic risk, limited risk, minimal risk). This skill should be used when the user asks to "classify an AI system under the AI Act", "determine the AI Act risk tier", "check if something is an AI system", "assess prohibited practices", "check high-risk classification", "determine Art. 6 exception applicability", or mentions "KI-Verordnung", "Risikoklassifizierung", Art. 5, Annex III, or GPAI systemic risk.

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。

Structure a student's self-determined project with mentoring checkpoints, competency alignment, and reflection. Use when guiding student-led projects within a competency-based framework.

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。

Structure a student's self-determined project with mentoring checkpoints, competency alignment, and reflection. Use when guiding student-led projects within a competency-based framework.

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。

Determines the organization's role (provider, deployer, importer, distributor, or quasi-provider) and assesses Art. 25 quasi-provider risk of the EU AI Act. This skill should be used when the user asks to "determine AI Act roles", "check if we are provider or deployer", "assess quasi-provider status", "check Art. 25 substantial modification", "check value chain responsibilities", or mentions "Betreiber", "Anbieter", "wesentliche Veränderung", or finetuning implications under the AI Act.

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。

Classify AI systems under the EU AI Act (Regulation (EU) 2024/1689) and determine compliance obligations. Use when asked to assess AI risk level, check if an AI system is prohibited, determine high-risk status, identify GPAI model obligations, map provider/deployer responsibilities, or build an AI Act compliance roadmap. Covers the full classification decision tree (Prohibited → High-Risk → Limited → Minimal → GPAI), obligations by role (provider, deployer, importer, distributor), compliance timelines, and DACH-specific considerations (German works council, BaFin, BSI, BNetzA). Triggers on phrases like "classify this AI system", "is this high-risk under the AI Act", "AI Act risk assessment", "EU AI Act compliance", "prohibited AI practice", "GPAI obligations", "AI Act timeline".

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 should be used when the user needs to calculate consumption tax (消費税) or determine their tax method (2割特例, 簡易課税, or 本則課税). Trigger phrases include: "消費税を計算", "消費税の申告", "消費税申告書", "2割特例", "簡易課税", "本則課税", "課税売上", "消費税額", "インボイス", "みなし仕入率", "課税仕入".

This skill should be used when the user asks to determine what tax filings they need, wants to know if they must file a tax return (確定申告), asks about consumption tax obligations (消費税), or needs help understanding their filing requirements. Trigger phrases include: "確定申告が必要か", "申告の種類", "消費税の届出", "課税事業者かどうか", "何を申告すればいい", "申告要否", "税金の申告", "住民税の申告".

Your first 10 hires determine your company culture. Your first 50 determine your execution capacity. Hiring is the most leveraged activity a founder does, and the most common source of expensive mistakes. This skill covers when to hire, who to hire first, how to sequence roles, compensation and equity, interviewing and closing, and the many ways hiring goes wrong at startups. Use when "hiring, first hire, when to hire, who to hire, compensation, equity, salary, recruiting, interview process, offer negotiation, team building, engineer hire, sales hire, founding team, hiring, team, compensation, equity, recruiting, interviews, offers, startups, founding-team" mentioned.

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。

Apply semantic versioning (SemVer 2.0.0) to determine the correct version bump based on change analysis. Covers major/minor/patch classification, pre-release identifiers, build metadata, and breaking change detection. Use when preparing a new release to determine the correct version number, after merging changes before tagging, evaluating whether a change constitutes a breaking change, adding pre-release identifiers, or resolving disagreement about what version bump is appropriate.

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。

Assesses whether hysterectomy and oophorectomy are medically necessary as part of gender-affirming surgery for transgender patients. Triggered when a patient is planning gender-affirming surgery and clinicians need to determine if total hysterectomy and oophorectomy should be included.

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。

Assesses Tanner stage to determine if physical changes of puberty have begun (stage G2/B2 or higher) for considering pubertal suppression. Triggered when a clinician evaluates pubertal development in an adolescent patient.

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 should be used when the user asks to "最小再現環境を作って", "再現環境を構築して", "問題を切り分けたい", "パッケージの不具合か確認したい", "create a minimal reproduction", "isolate the issue", "build a repro environment", "reproduce this bug", "narrow down the cause", "is this a package bug or my code", "このPRの問題を再現して", "this commit broke something", or wants to determine whether a bug is caused by a package, the environment, or application code. Accepts PR URLs, commit hashes, or branch names as input.

Use when evaluating test quality on modules containing business logic, calculation utilities, or state machines to determine whether the tests provide genuine defect detection.

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。

GATEWAY — load FIRST before composing any HyperFrames animation or video. The high-level motion law that makes a multi-scene video feel like ONE continuous camera move instead of a stack of independently-animated slides. Covers the vector law (how you exit determines how you enter, incl. the Z scale-sign rule), the film's current, carrier elements, causal motion, the Seam Gate (build-gate enforcement), the ban on idle wobble (motion must PERFORM, not breathe), stillness-before-climax, and the sustained-motion routes. Routes to the low-level technique skills (cut-the-curve — the full catalog incl. waterfall entry + nudge curve, oversized-cursor, seam-craft). These rules SUPERSEDE generic / upstream motion guidance. [continuity, direction, vector, momentum, seam, transition, ease, performance, idle-motion, narrative-motion, film-grammar]

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。

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates 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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for forced-auth coercion, relay chains, target selection, NTLM or related acceptance paths, and coercion-to-privilege transitions. Use when the user asks to trace a coercion primitive, follow a relay path, analyze forced authentication, determine which service accepts relayed auth, or connect a coercion step to resulting privilege, enrollment, or code execution. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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 should be used when the user asks to "build an MCP server", "create an MCP", "make an MCP integration", "wrap an API for Claude", "expose tools to Claude", "make an MCP app", or discusses building something with the Model Context Protocol. It is the entry point for MCP server development — it interrogates the user about their use case, determines the right deployment model (remote HTTP, MCPB, local stdio), picks a tool-design pattern, and hands off to specialized skills.

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 a defensible cybersecurity risk assessment using the NIST SP 800-30 Rev 1 methodology: prepare scope and a risk model, identify threat sources and threat events, identify vulnerabilities and predisposing conditions, determine likelihood and impact, compute risk, and communicate results as a prioritized risk register. Use when an organization needs an actual risk *assessment* (not a maturity score), when a control framework (CSF, ISO 27001, RMF, SOC 2, PCI) requires a documented risk analysis as input, when leadership asks "what are our top risks and how bad are they", when assessing risk for a new system or major change, or when building a risk register from scratch. This is the methodology that feeds framework selection, ATO packages, and treatment decisions. Keywords: risk assessment, NIST 800-30, threat modeling, likelihood and impact, risk register, risk analysis, threat sources, vulnerabilities, risk determination, qualitative risk, risk matrix, residual risk, risk treatment.

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 Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a Windows system, confirming whether a suspicious binary ran, or correlating execution evidence with other forensic artifacts during an 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。

Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment 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。

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

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。

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates 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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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。

Filing gate for ALL brain writes. Consulted before creating any new brain page to determine the correct path. Reads the ACTIVE schema pack via `gbrain schema show --json` — no hardcoded directory table. Also runs periodic taxonomy drift detection via `gbrain schema review-orphans`.

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。