「termine」の検索結果

Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689). Use when asked to determine GPAI model provider obligations, assess Code of Practice compliance, evaluate transparency or copyright requirements for GPAI models, check systemic risk obligations, build a GPAI compliance roadmap, or prepare for AI Office requests. Covers all three Code chapters (Transparency, Copyright, Safety & Security), the 12 commitments and their measures, systemic risk classification (Article 51), provider obligations (Articles 53 and 55), open-source exemptions, enforcement timeline, Commission/AI Office supervision, and DACH-specific overlays such as BSI cybersecurity and works council implications. Triggers on phrases like "GPAI compliance", "Code of Practice assessment", "GPAI model obligations", "systemic risk evaluation", "AI Act transparency requirements", "GPAI copyright policy", "general-purpose AI compliance".

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Audits AI-assisted legal, compliance, governance, and institutional decisions before reliance to determine whether the responsible human or institution has enough access to the evidence, reasoning basis, uncertainty, authority, and review path to genuinely own the decision rather than merely approve, sign, or transmit it.

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Map the epistemic structure of a subject to determine knowledge types and inform curriculum sequencing. Use when designing courses, restructuring programmes, or analysing knowledge architecture.

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Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.

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Use when you need to determine audience awareness level and the correct messaging approach. Trigger on "awareness stage", "cold vs warm audience", "Schwartz", "message to market match".

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本skill是用于单细胞样本性别判定的专用技能，首先需要用户上传需要处理的单细胞样本数据，再判定未标注性别的样本，或纠正标注错误的性别信息，生成最后的修正文件。

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Adversarial code reviewer for Bug Hunter. Rigorously challenges each reported bug to determine if it's real or a false positive. Uses doc-lookup (Context Hub + Context7) to verify framework claims before disproval. The immune system that kills false positives.

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This skill enriches vague prompts with targeted research and clarification before execution. Should be used when a prompt is determined to be vague and requires systematic research, question generation, and execution guidance.

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Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations. Only runs when explicitly invoked; it does not trigger on its own.

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Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.

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

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Conducts retention impact assessments for new processing activities to determine appropriate data retention periods. Covers regulatory requirements scanning, proportionality review, purpose-based retention determination, and retention period documentation aligned with GDPR Article 5(1)(e) and Article 25 data protection by design. Activate for retention assessment, new processing retention, retention period determination queries.

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Conducts pre-DPIA threshold screening to determine whether a full Data Protection Impact Assessment is required under GDPR Article 35. Applies the EDPB WP248rev.01 nine-criteria test, national supervisory authority blacklists, and organisational risk appetite to produce a documented screening decision. Keywords: threshold screening, DPIA trigger, pre-DPIA, WP248, Article 35(1), blacklist, screening decision.

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Guides the Privacy Threshold Analysis screening process to determine whether a full DPIA is required. Provides a quick-screen questionnaire, threshold criteria based on WP248rev.01, escalation triggers, and documentation requirements. Activate when evaluating new processing activities, system changes, or procurement decisions. Keywords: PTA, privacy threshold analysis, DPIA screening, quick-screen, threshold criteria, WP248, escalation triggers.

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Guides the establishment and management of joint controller arrangements under GDPR Article 26, including determination of joint controllership, allocation of responsibilities, and transparency obligations. Activate when two or more controllers jointly determine purposes and means of processing, or when evaluating shared data platforms. Keywords: joint controller, Article 26, shared responsibility, arrangement, joint determination.

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Determines whether a personal data breach triggers notification obligations under GDPR Articles 33 and 34 using structured risk assessment methodology. Covers breach type classification (CIA triad), data sensitivity scoring, volume assessment, identifiability analysis, and consequence severity evaluation. References EDPB Guidelines 01/2021 with 18 breach scenarios. Keywords: breach risk assessment, GDPR, Article 33, Article 34, EDPB, notification threshold.

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Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.

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Generate attention-grabbing hooks for the first 3 seconds of videos. The hook determines if viewers stay or scroll. Creates multiple hook variations for A/B testing. Use when crafting video openings, improving retention, or creating scroll-stopping content for YouTube, TikTok, or Reels.

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Fix NIP-98 HTTP authentication 401 errors caused by URL mismatch between the `u` tag and what the server expects. Use when: (1) NIP-98 auth returns 401 with "URL mismatch" error, (2) Token creation succeeds but server rejects authentication, (3) Server logs show expected vs actual URL difference. CRITICAL: Server implementations vary - some strip query params, some don't. Check the actual error message to determine which behavior applies.

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Fix silent failures in Fastly Compute@Edge when using send_async for fire-and-forget requests. Use when: (1) Background tasks (migrations, webhooks, notifications) triggered from Compute never complete, (2) send_async PendingRequest is immediately dropped, (3) Fire-and-forget pattern works locally but fails in production, (4) An async trigger "logs as triggered" but the target service never receives the request (especially after renaming backend constants — the URL hostname is cosmetic, the backend name determines routing, so a global rename can silently reroute calls to the wrong service). Also covers silent backend-not-found errors when FALLBACK_BACKENDS or other backend constants reference names not configured in the Fastly dashboard.

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Review an openai-guardrails-python release plan or final release candidate against the previous remote tag, determine the compatible release type, audit runtime and packaging risk, inspect current CI, and produce an English ship-or-block report. Use for pre-release readiness checks, not ordinary PR review or implementation.

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Quickly analyzes Python repositories to understand their purpose, structure, and setup requirements. Use when Claude needs to onboard to a new Python codebase, understand project structure, identify entry points, determine dependencies, or generate setup instructions. Trigger when users ask to "analyze this Python repo", "understand this codebase", "how do I run this project", "what does this repo do", or provide a Python repository path for quick start guidance.

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Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.

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Analyze CVE reachability in software repositories by examining how vulnerable dependencies are imported and used. Determines whether vulnerable components, classes, or functions are reachable from project code through call chain analysis, reflection detection, dynamic loading patterns, and configuration-gated behavior. Classifies each CVE as likely reachable, possibly reachable, or likely unreachable with supporting evidence. Use when analyzing security vulnerabilities in dependencies, performing post-disclosure CVE triage, assessing vulnerability impact, or when users ask to analyze CVE reachability, check if vulnerabilities are exploitable, or evaluate dependency security risks.

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。