「detect」の検索結果

Detect command injection, eval/exec usage, remote execution, or arbitrary code loading.

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。

Detect data exfiltration via URL path encoding and chained web_fetch navigation. Covers fake trusted UI injection, letter-level URL path exfiltration, and multi-hop navigation hijacking. Use when the agent has web/URL fetch capability and stores user memory or personal context.

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。

Detect hardcoded secrets in code or configuration accessible to the target agent. Focuses on secrets embedded in source, configs, or IaC, not runtime leaks.

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。

Detect error propagation, chain failures, and single-point breakdowns that cascade across agent 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。

Detect persistent instruction injection or long-term memory poisoning. Focus on writing/retaining hostile instructions for future tasks, not data leakage.

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。

Detect social engineering, deceptive responses, false assurances, or prompts that induce unsafe user actions.

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。

Detect agentic supply-chain risks: compromised dependencies, malicious plugins/tools/models, and untrusted update sources.

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。

Detect privilege escalation and unauthorized access via dialogue. Use when the agent has roles, admin functions, or multi-user data.

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。

Detect indirect prompt injection (goal hijack). Instructions hidden in "external" content (documents, RAG, web) that the agent processes. Use when the agent has document/RAG/web/file input.

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。

Detect tool misuse and unexpected code execution via dialogue testing. Use when the agent exposes file, code-execution, or network tools.

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。

Detect sensitive information disclosure via escalating dialogue probes. Covers system prompt extraction, credential/API key leakage, PII, and internal configuration exposure.

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。

Detect direct prompt injection or instruction override via user message (no external content). Focuses on system/role override attempts.

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。

Detect data leakage, missing boundaries, or privilege mismatch in inter-agent communication.

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。

DO NOT USE when the target already consumes an injected interface or built-in abstraction such as IFileSystem or TimeProvider, even if the request says "generate a wrapper"; no new wrapper is needed. Use only when C# source calls an ambient/static dependency and no injectable seam exists: first-time TimeProvider, IHttpClientFactory, or System.IO.Abstractions adoption; minimal Environment/Console/Process wrappers; IProcessRunner; DI registration; or an ambient seam that preserves a static API. Exclude static detection (detect-static-dependencies), migration to an existing/registered abstraction (migrate-static-to-wrapper), one blocked behavior plus deterministic tests (testability-obstacle), and general interface design.

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。

Identify a .NET project's test platform, framework, command mode, and SDK-style vs classic project system. Use only for "which test platform/framework?", "VSTest or MTP?", or "what runner does this project use?", including bridge settings, UseVSTest opt-outs, and incompatible or conflicting VSTest/MTP configuration. Resolves global.json, project, packages.config, Directory.Build.props, and Directory.Packages.props precedence for MSTest/xUnit/NUnit/TUnit. DO NOT USE when the user asks to run/filter tests or for commands, flags, TRX/dumps, or test-command/filter errors; use run-tests directly. Do not route hot-reload or migration requests here as the entry skill; mtp-hot-reload may use this skill internally for platform 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。

Guide for theming .NET MAUI apps — light/dark mode via AppThemeBinding, ResourceDictionary theme switching, DynamicResource bindings, system theme detection, and user theme preferences. Use when: "dark mode", "light mode", "theming", "AppThemeBinding", "theme switching", "ResourceDictionary theme", "dynamic resources", "system theme detection", "color scheme", "app theme", "DynamicResource". Do not use for: localization or language switching (see .NET MAUI localization documentation), accessibility visual adjustments (see .NET MAUI accessibility documentation), app icons or splash screens (see .NET MAUI app icons documentation), or Bootstrap-style class theming (see Plugin.Maui.BootstrapTheme NuGet package).

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。

Detect and set up account-locked gh aliases for multi-account GitHub. The AI reads this skill, detects accounts, asks the user which is personal/work, and runs the setup automatically.

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。

Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 5-stage recon pipeline (seed discovery, asset expansion, enrichment, exposure analysis, reporting), asset-graph discipline with 29 asset types, severity rubric (CRITICAL/HIGH/MEDIUM/LOW/INFO), confidence upgrade workflows, time budgeting, asset-level triage rules, scale-based tactics, identity-fabric mapping (Entra/Okta/ADFS/Google/SAML/M365 Teams+SharePoint+OAuth), API and auth-map methodology, JavaScript deep analysis, mobile attack surface, cloud attack surface, breach×identity correlation, detectability tagging, detection-aware probing (back-off, persona rotation), read-only validator discipline, WAF/CDN bypass + origin discovery, vulnerability prioritization (CVE/EPSS/KEV), phishing infrastructure planning + pretext development, bug bounty submission templates, client deliverable templates with risk translation, threat-actor investigation (incl. RU/CN pivots), cryptocurrency tracing, image/video forensics, chronolocation. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, tracing crypto flows, geolocating media, or performing attribution work.

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。

Hunt server-side template injection (SSTI) across Jinja2 (Flask/Django), Twig (Symfony), Freemarker (Java), ERB (Rails), Spring, Velocity, Mako, Thymeleaf, Smarty. Detection probes use double-curly and dollar-curly math expressions evaluated server-side. Once an engine is fingerprinted, escalate to RCE via the engine-specific class-walker, callback-registrar, or Execute-utility patterns documented in disclosed reports. Detection patterns: error messages reveal engine, blank or numeric eval reveals expression mode. Targets: email templates, PDF/report generators, CMS preview features, error pages with user input. Use when hunting RCE via template rendering, when content shows engine fingerprints, when finding endpoints that compose strings with user input before render.

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。

Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions. Triggered when users ask to check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data.

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。

Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial 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。

Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation. Supports quantitative, qualitative, mixed methods research, and systematic review automation. Language: English. Responds in Korean when user input is Korean. Triggers: research question, theoretical framework, hypothesis, literature review, meta-analysis, effect size, IRB, PRISMA, statistical analysis, sample size, bias, journal, peer review, conceptual framework, visualization, systematic review, qualitative, phenomenology, grounded theory, thematic analysis, mixed methods, interview, focus group, ethnography, action research, paper retrieval, AI screening, RAG builder, humanization, AI pattern 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。

Research Guardian - Ethics Advisory & Bias Detection across all research stages Enhanced VS 3-Phase process: Surface-level screening, deep contextual analysis, constructive recommendations Use when: reviewing research ethics, checking for bias, assessing trustworthiness, QRP screening Triggers: ethics review, IRB, bias detection, QRP, trustworthiness, research integrity, p-hacking, HARKing

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。

Auto-detect work context (Dev vs Knowledge) — use to tailor workflows based on current task type

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。