「detect」の検索結果

Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/ language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.

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。

Optimizes Claude for proofreading French texts, whether literary, technical, or professional. Grammar and spelling checks, detection of barbarisms, and stylistic suggestions.

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 SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (25 mandatory, 7 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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。

Detects prompt injection hidden in documents from the other side (pleadings, skeletons, bundles, served evidence, opponents' emails and attachments) before an AI reads them, so the model is not turned against your client. Checks word by word that the text an extractor hands the model was visibly drawn on the page: white, near-white, covered, clipped, off-page, tiny, transparent and invisible-mode text in PDF (ink test plus OCR); Word formatting resolved as Word does (styles, shading, theme colours, scaling, off-page frames, fallbacks, unused headers); HTML/CSS, RTF, email with attachments and zips, xlsx/pptx and legacy formats. Flags Unicode smuggling, text addressed to an AI and authorities found only in hidden text. Fails closed. Optional Read-tool hook. Use BEFORE any AI summary or analysis of an opposing or third-party document. Triggers: 'scan for injection', 'is this document safe', 'check the other side's skeleton', 'injection guard'.

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。

A zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware privacy policy. Jurisdiction-first: it detects which laws apply from where your users are, then drafts only the required clauses — GDPR/EU + UK, US (CCPA/CPRA, ~20 state laws, COPPA, sector overlays), and global/MENA (LGPD, Quebec Law 25, India DPDP, China PIPL, UAE/DIFC, Saudi PDPL), plus app-store, cookies, and AI/EU AI Act disclosures. Its rule: state only what you confirm; never invent a statute, citation, fine, or date — every claim is source-cited and QA-gated. Not legal advice.

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 NIST Cybersecurity Framework (CSF) advisor covering CSF 2.0 and CSF 1.1. Use this skill whenever a user asks about NIST CSF, cybersecurity risk management, the six CSF functions (Govern, Identify, Protect, Detect, Respond, Recover), CSF profiles, implementation tiers, gap assessments, organizational profiles, community profiles, CSF core subcategories, informative references, or mapping to other frameworks (NIST SP 800-53, ISO 27001, CIS Controls, COBIT). Also trigger for questions like "how do I implement NIST CSF?", "what does CSF 2.0 change?", "help me build a CSF profile", "how do I assess my cybersecurity posture?", or any request involving organizational cybersecurity risk strategy or framework alignment.

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。

Argentine Supreme Court citation network analysis using JurisRank — a peer-reviewed PageRank algorithm with temporal decay for measuring jurisprudential authority. Ranks precedents by citation influence, traces doctrinal evolution, and detects constitutional drift. Published methodology: JCLLT (DOI: 10.47852/bonviewJCLLT62027951). Activate with: which cases to cite, rank precedents, case authority, doctrinal evolution, Argentine Supreme Court, CSJN jurisprudence, citation network, leading case, legal research Argentina.

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。

Static analysis of UEFI/BIOS firmware dumps using Intel's chipsec framework. Decode firmware structure, detect known malware and rootkits (LoJax, ThinkPwn, HackingTeam, MosaicRegressor), generate EFI executable inventories with hashes, extract NVRAM variables, and parse SPI flash descriptors. Use when analyzing firmware .bin/.rom/.fd/.cap files offline without requiring hardware access.

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。

Probe IoT/embedded targets for exposed SWD/JTAG debug interfaces using a SEGGER J-Link. Detects whether debug is OPEN, LOCKED (readout-protected), or DEAD (fused off). Use when assessing whether a target's on-chip debug port can be reached, identifying the silicon vendor from DPIDR/IDCODE, and confirming halt+memory access for full debugger control.

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。

IoT network traffic analyzer for detecting IoT protocols and identifying security vulnerabilities in network communications. Use when you need to analyze network traffic, identify IoT protocols, or assess network security of IoT devices.

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。

Advanced file finder with type detection and filesystem extraction for analyzing firmware and extracting embedded filesystems. Use when you need to analyze firmware files, identify file types, or extract ext2/3/4 or F2FS filesystems.

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 blueprint for physics queries using RayCast, ShapeCast, and DirectSpaceState. Covers hit detection, volume overlap, mouse picking, and high-performance server-side intersection queries. Use when implementing projectiles, LOS, terrain grounding, or AI sensors. Keywords raycast, shapecast, direct_space_state, intersect_ray, intersect_shape, PhysicsRayQueryParameters, collision mask, mouse picking.

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 blueprint for rhythm games including audio synchronization (BPM conductor, latency compensation with AudioServer.get_time_since_last_mix), note highways (scroll speed, timing windows), judgment systems (Perfect/Great/Good/Bad/Miss), scoring with combo multipliers, input processing (lane-based, hold note detection), and chart/beatmap loading. Based on DDR/osu!/Beat Saber research. Trigger keywords: rhythm_game, audio_sync, timing_judgment, note_highway, combo_system, BPM_conductor, latency_compensation.

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 blueprint for fighting games including frame data (startup/active/recovery frames, advantage on hit/block), hitbox/hurtbox systems, input buffering (5-10 frames), motion input detection (QCF, DP), combo systems (damage scaling, cancel hierarchy), character states (idle/attacking/hitstun/blockstun), and rollback netcode. Based on FGC competitive design. Trigger keywords: fighting_game, frame_data, hitbox_hurtbox, input_buffer, motion_inputs, combo_system, rollback_netcode, cancel_system, advantage_frames.

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 patterns for Godot 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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 wrapping up a session before ending. 4 parallel subagents detect doc updates, repeated patterns, learning points, and follow-ups, then a verifier deduplicates and presents choices. Triggers on /session-wrap, session wrap, session cleanup, end of session.

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。

Analyze session work and automatically convert reusable patterns into Claude Code skills. Use when: "세션을 스킬로", "스킬 만들어", "이거 스킬로", "skill factory", "이 작업 자동화해", "스킬 추출", "make this a skill", "extract skill", "convert to skill", "스킬 팩토리", "자동 스킬 생성". Differs from skill-creator (archived) and manage-skills (drift detection): this skill actively analyzes sessions, checks for duplicates, and creates skills via Agent Teams.

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 blueprint for physics queries using RayCast, ShapeCast, and DirectSpaceState. Covers hit detection, volume overlap, mouse picking, and high-performance server-side intersection queries. Use when implementing projectiles, LOS, terrain grounding, or AI sensors. Keywords raycast, shapecast, direct_space_state, intersect_ray, intersect_shape, PhysicsRayQueryParameters, collision mask, mouse picking.

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 blueprint for rhythm games including audio synchronization (BPM conductor, latency compensation with AudioServer.get_time_since_last_mix), note highways (scroll speed, timing windows), judgment systems (Perfect/Great/Good/Bad/Miss), scoring with combo multipliers, input processing (lane-based, hold note detection), and chart/beatmap loading. Based on DDR/osu!/Beat Saber research. Trigger keywords: rhythm_game, audio_sync, timing_judgment, note_highway, combo_system, BPM_conductor, latency_compensation.

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 blueprint for fighting games including frame data (startup/active/recovery frames, advantage on hit/block), hitbox/hurtbox systems, input buffering (5-10 frames), motion input detection (QCF, DP), combo systems (damage scaling, cancel hierarchy), character states (idle/attacking/hitstun/blockstun), and rollback netcode. Based on FGC competitive design. Trigger keywords: fighting_game, frame_data, hitbox_hurtbox, input_buffer, motion_inputs, combo_system, rollback_netcode, cancel_system, advantage_frames.

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 patterns for Godot 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.

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。

Deploy applications to the Temps platform with automatic framework detection, Dockerfile generation, and container orchestration. Supports Next.js, Vite, React, Node.js, Python, Go, Rust, Java, and C# applications. Use when the user wants to: (1) Deploy their app to Temps, (2) Set up CI/CD with Temps, (3) Configure deployment settings, (4) Create a Dockerfile for Temps, (5) Deploy a containerized application, (6) Set up automatic deployments from Git. Triggers: "deploy to temps", "temps deployment", "push to temps", "containerize for temps", "temps ci/cd".

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。

Manage, deploy, operate, and instrument applications with Temps. Use this skill whenever the user mentions Temps, `@temps-sdk/cli@0.1.38`, deploying or migrating an app to Temps, projects, environments, services, domains, backups, logs, monitoring, analytics, browser Performance Insights/Core Web Vitals, observability, error tracking, OpenTelemetry, tracing, session replay, or Temps Cloud. Also use it when preparing an application for production on Temps even if the user does not explicitly ask for the CLI. Route to focused references, proactively detect missing observability during create/link/deploy journeys, and use pinned `bunx` or `npx` CLI invocations with explicit target contexts.

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 project's infrastructure and SaaS stack, then produce a cost report showing what the user currently pays and what they would save by consolidating onto Temps (self-hosted or Temps Cloud). Detects hosting platforms (Vercel, Netlify, Railway, Render, Heroku, Fly.io), analytics (PostHog, Plausible, Mixpanel, Amplitude, Fathom), error tracking (Sentry, Bugsnag, Rollbar, Honeybadger), session replay (LogRocket, FullStory, Hotjar, Highlight), uptime monitoring (Pingdom, UptimeRobot, Better Stack, Checkly), managed databases (Supabase, Neon, PlanetScale, MongoDB Atlas, Upstash, RDS), and transactional email (SendGrid, Postmark, Resend, Mailgun) from dependencies, config files, and env var names. Use when the user wants to: (1) Know how much they would save by switching to Temps, (2) Audit their SaaS/infrastructure spend, (3) Compare their current stack's cost against self-hosting, (4) Decide whether Temps is worth it, (5) Build a business case for consolidating tools. Triggers: "how much would I save", "temps savings", "cost comparison", "audit my stack", "am I overpaying", "saas spend", "calculate savings", "is temps cheaper".

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。