「detection」の検索結果

Scope deviation 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。

Transform AI-generated content into human-sounding writing via AI pattern detection, rhythm restoration, and authenticity scoring. Use when content sounds robotic, uses AI cliches, or the user wants to humanize or fix AI writing.

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。

Adversarial verification for AI-generated legal content. Use when fact-checking legal documents, validating citations, detecting hallucinations, scoring document quality, or assessing distribution readiness.

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。

Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.

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。

Environment and secrets management lifecycle: .env scaffolding, validation, leak detection, and rotation across Vault, AWS SSM, 1Password, and Doppler. Use when setting up projects, scanning for leaked secrets, or rotating credentials.

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。

STRIDE threat modeling, DREAD risk scoring, secret detection, and secure architecture design. Use when conducting threat models, reviewing code for vulnerabilities, designing defense-in-depth, or scanning for hardcoded secrets.

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。

MLOps across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD workflows.

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 changelogs and release notes from Conventional Commits with semver bump detection, Keep a Changelog formatting, and monorepo scopes. Use when preparing releases, enforcing commit standards, or automating release notes.

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。

Cut software and services spend through seat-utilisation analysis, redundant-tool detection, and renewal-timing leverage. Use when auditing SaaS spend, preparing a renewal negotiation, or hunting a budget-reduction target.

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 creating, improving, finding, or auditing agent skills - the user says 'create a skill', 'do I have a skill for X', 'improve the X skill', 'which skill should I use', asks whether a skill exists for a task, or wants to validate, test, evaluate, package, or health-check skills. Also use for skill ecosystem maintenance (duplicate detection, stale skills, trigger collisions) and advisor checkpoints.

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。

Connects to Oxylabs remote agent browsers over the Chrome DevTools Protocol (CDP) with Playwright or Puppeteer. Built-in anti-detection, residential proxies, geo-targeting, persistent sessions and profiles, session recording and live VNC inspection for debugging. Use instead of WebFetch or a local browser whenever a site renders with JavaScript, blocks bots (DataDome, Cloudflare, Akamai), needs a real browser session, screenshots or PDFs. Covers connection, retries, error recovery and safe scraping of protected targets without any human help.

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 the user has a judge/grader and human-labeled data, and wants to measure how well the judge agrees with humans, detect systematic biases, determine whether automatic evaluation can replace human review, or build a human-reduction roadmap. Also use when the user mentions calibration, TPR/TNR, judge validation, inter-rater agreement, Cohen's kappa, bias detection, or "is my automatic evaluation trustworthy." Merges the calibrate and align functions into one 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。

Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.

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。

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。

Professional network reconnaissance and port scanning using nmap. Supports various scan types (quick, full, UDP, stealth), service detection, vulnerability scanning, and NSE scripts. Use when you need to enumerate network services, detect versions, or perform network reconnaissance.

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。

Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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 detecting and running project build systems automatically. Supports npm/yarn/pnpm/pip/poetry/gradle/maven/cargo/go/make. Triggers on build, test run, project setup, package manager 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。

Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.

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 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。