「ses」の検索結果

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).

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。

ALWAYS invoke this skill when the user asks about work done outside this session - "check work", "check the others", "check other sessions" - or to accept one: "the session is done, check it", "can we merge it" - in any language. You are the receiving party: treat the report as a claim, verify each part against machine state, name every gap between claimed and true. Verified-ready work merges in the same pass. End with what is left, one next step, and any reply another session needs, ready to paste.

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。

Connect MATLAB to Databricks via Spark (Databricks Connect) or JDBC (Database Toolbox). Use when setting up the MATLAB Interface for Databricks, configuring authentication (OauthU2M, OauthM2M, PAT), creating Spark sessions with getDatabricksSession(), reading Unity Catalog tables with server-side filtering, creating JDBC connections with databricks.JDBCConnection or StandaloneJDBCConnection, connecting to SQL Warehouses, selecting JDBC drivers (Simba/OSS), or writing data back to Databricks. Triggers on: Databricks Connect, Spark from MATLAB, getDatabricksSession, .databrickscfg, databricks.JDBCConnection, StandaloneJDBCConnection, SQLWarehouse, Databricks JDBC, Databricks cluster, large table server-side filtering.

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Discover when you are most active and most productive with Claude Code by bucketing sessions and events into hour-of-day and day-of-week bins from their timestamps, then flagging peak versus low-output windows. Uses the session list, per-session events, and analytics daily trends. Use when planning a schedule or deciding when to do deep work versus lighter tasks.

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。

Forecast Claude Code spend to the end of the week or month from the daily session trend on the Agent Monitor dashboard — moving average of daily spend × days remaining, added to spend-to-date. Uses /api/analytics daily_sessions, /api/pricing/cost, and /api/sessions for a per-day cost curve. Use when projecting cost or asking "where will my spend land".

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 the full event chain for one Claude Code session into an ordered timeline of every event type with tool_name and summary, highlighting gaps, out-of-order events, and failures. Reads /api/events?session_id= and /api/sessions/:id from the Agent Monitor dashboard. Use when debugging what a session actually did, step by step.

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。

Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.

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。

Designing quizzes, personality assessments, and recommendation tools that segment users into actionable categories rather than generating clicks for clicks' sake. Question architecture, scoring algorithms, result categorization, recommendation mapping, lead capture integration. Honest about clickbait-quiz (engagement only), vanity-result (entertaining, not useful), and actionable-segmentation (genuine categorization that drives next-step recommendations) patterns. Triggers on quiz, assessment, personality test, recommendation tool, scorecard, diagnostic, fit evaluator, what-type-of-X-are-you, persona quiz. Also triggers when an audience needs a categorization-driven lead magnet, when a vanity quiz is producing engagement but no qualified leads, or when an assessment is being scoped for the first time.

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。

Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.

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 templated responses for common legal inquiries with escalation detection. Use when drafting legal responses.

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。

Risk-based vendor assessment framework for IT service providers, technology vendors, and third-party partners under DORA, NIS2, GDPR. Provides three-phase process (Initial Screening / Detailed Assessment / Final Evaluation), six-dimension risk scoring (Financial/Operational/Compliance/Security/Reputational/Strategic) with weighted matrices, full DORA Art. 28-30 contractual checklist, NIS2 Art. 21(2) security measures enumeration, GDPR Art. 28 documentation checks, red flags per dimension, trigger-based review criteria, and document templates. Use when: (1) Evaluating new vendors or technology providers, (2) Conducting critical ICT third-party due diligence under DORA, (3) Performing supply chain security assessment under NIS2, (4) Creating vendor onboarding documentation, (5) Establishing ongoing vendor monitoring, (6) Assessing concentration risk, or (7) Generating executive vendor risk 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。

Assess whether Government of India approval is required for foreign investment in an Indian company under Foreign Exchange Management Act, 1999 and the Non-Debt Instruments Rules, 2019. The skill systematically gathers transaction details, evaluates sectoral entry routes and caps, assesses land border country restrictions under applicable laws, and delivers a preliminary compliance note. Produces clear guidance on whether the investment falls within Automatic Route or requires Government 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。

Audits a legal document against legal design principles. Scores the document on six pillars: language patterns (bureaucratese, archaisms, passive voice, nominalization), readability (Flesch Reading Ease and language-specific equivalents), structure and navigation, hidden conditions (material obligations buried away from their headings), statutory duplication (restating the law instead of citing it), and visual hierarchy where layout is available. Assessment-only; does not rewrite the document. Works on English, French, and German documents, inferring the applicable jurisdiction from the document's language and textual signals: governing-law clause, statute citations, party addresses. Use when a user shares a legal document (contract, NDA, T&C, privacy policy, internal policy) asking how good the drafting is from a legal design perspective, whether it is readable, whether it contains buried clauses or restates the law, or how it scores on Flesch / LIX / Amstad / Kandel-Moles readability metrics.

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。

Fast 15-25 minute triage for preliminary EU AI Act classification and compliance assessment. This skill should be used when the user asks to "do a quick AI Act assessment", "check if the AI Act applies to us", "run a preliminary classification", "do an AI Act triage", "quick check", "preliminary assessment", "Schnellprüfung", "Ersteinschätzung", or needs a fast initial assessment before committing to full 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。

Assess and operationalize implementation readiness for high-risk AI systems under the EU AI Act Annex III, including provider and deployer obligations, conformity assessment, post-market monitoring, and EU database registration. Use when users say things like “we classified this as high-risk, what now?”, “build an EU AI Act readiness plan”, “assess our Annex III compliance gaps”, “what do providers/deployers of high-risk AI need to implement?”, “prepare for conformity assessment”, or “create a high-risk AI implementation roadmap.”

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。

Assess whether a Fundamental Rights Impact Assessment (FRIA) is required under Article 27 EU AI Act, and structure or draft that assessment for a specific high-risk AI deployment. Covers deployer scope gating (public bodies and private entities providing public services), affected group mapping, Charter rights analysis, proportionality, safeguards evaluation, residual risk, DPIA/FRIA cross-referencing under the amended Article 27(4), the unconditional notification duty under Article 27(3), and DACH-specific considerations. Use when asked about FRIA obligations, Article 27 scope, fundamental rights and AI, or deployer assessment duties.

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。

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".

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 a single-point rubric with one criterion and open columns for evidence. Use for student self-assessment, peer feedback, teacher formative feedback, or pre-task planning. Works with any learning target, with or without a band system.

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 multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.

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 a proposed assessment for construct validity, reliability, and alignment to learning objectives. Use when reviewing or quality-assuring assessments before deployment.

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。

Build .NET AI agents, harnesses, and multi-agent workflows with Microsoft Agent Framework using the right agent type, sessions, tools, workflows, hosting protocols, and enterprise guardrails. USE FOR: building or reviewing .NET code that uses Microsoft.Agents.*, Microsoft.Extensions.AI, AIAgent, HarnessAgent, AgentSession, or Agent Framework hosting packages; choosing agent, harness, workflow, and hosting shapes. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

Protect Claude Code sessions from context overflow by running a background daemon that monitors session size and auto-prunes before compaction hits. Use when the user says "guard", "protect session", "context getting long", "prevent compaction", "session management", or is running agent teams that need continuous context protection.

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。

Background context for invoice-system input tax credit rules (仕入税額控除) in the shinkoku tax filing plugin. Contains eligibility requirements, the 6 permanent exceptions (帳簿のみ保存の恒久特例), transitional measures for purchases from tax-exempt businesses, and storage requirements. This skill is not user-invocable — Claude loads it automatically when responding to input tax credit questions under the invoice system.

This skill should be used when the user asks about the invoice system (インボイス制度), qualified invoices (適格請求書), registration numbers (登録番号), input tax credits (仕入税額控除), the 20% special measure (2割特例), the 30% special measure (3割特例), tax-exempt businesses (免税事業者), transitional measures (経過措置), small-amount exceptions (少額特例), corrected invoices (修正インボイス), or any related topics. Trigger phrases include: "インボイス", "適格請求書", "登録番号", "仕入税額控除", "2割特例", "3割特例", "免税事業者", "経過措置", "少額特例", "修正インボイス", "返還インボイス", "簡易インボイス", "インボイス登録", "T番号", "適格請求書発行事業者".