「before」の検索結果

Complete guide for TUnit new-generation testing framework. Use when creating test projects with TUnit or migrating from xUnit to TUnit. Covers Source Generator driven test discovery, AOT compilation support, fluent async assertions. Includes project creation, [Test] attribute, lifecycle management, parallel control, and xUnit syntax comparison. Keywords: TUnit, tunit testing, source generator testing, AOT testing, new generation testing framework, [Test], [Arguments], TUnit.Assertions, Assert.That, Before(Test), After(Test), NotInParallel, TUnit.Templates, Microsoft.Testing.Platform, TUnit vs xUnit, parallel execution

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Use for non-trivial feature, behavior, workflow, or architecture work before execution planning. Create or update a versioned spec under planning/specs/active with design intent, alternatives, risks, acceptance criteria, and planning 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。

Route tasks using the repository's static routing catalog and load only the minimal context pack. Use before execution when the task spans multiple domains or the correct instruction set is unclear.

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 a minimal context pack and recommend the correct specialist path for token-efficient execution. Use when the task is non-trivial, spans multiple domains, or should reduce token usage before implementation.

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。

Mandatory final risk-focused review for any repository change. Checks security, architecture boundaries, test coverage, and release readiness before closeout.

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 and remediate dependency/package vulnerabilities across .NET backend, frontend (npm/pnpm/yarn), and Rust projects with severity-based quality gates. Use when the user asks for SCA checks, dependency hardening, or release security gates before build/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。

Use for non-trivial, design-bearing, or architecture-affecting work before execution planning. Creates or updates a versioned spec under planning/specs/active/ with design intent, alternatives, risks, and acceptance criteria.

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 as the single visible starter and controller for workspace work that may change files, runtime assets, planning state, docs, settings, or governance. Normalize the request into the Super Agent lifecycle: intake, spec registration for non-trivial work, planning registration, specialist routing, execution, testing, review, closeout, and planning-state updates before implementation begins.

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Code implementation, refactoring, and bugfixes for create-vue-dsfr. Use for implementing features, refactoring code, or fixing bugs. Read the shared project skill before acting.

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Documentation and narrative content for create-vue-dsfr. Use for markdown files, Storybook stories, VitePress guides, component documentation, and API documentation. Read the shared project skill before acting.

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Unit and integration testing strategy for create-vue-dsfr. Use for writing, updating, or refactoring Vitest specs and test coverage. Read the shared project skill before acting.

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Git commit, branch, and pull request conventions for create-vue-dsfr. Use before creating commits, branches, issues, or PRs. This skill has priority over other project skills for commit and PR work.

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Use FIRST when someone asks for a new feature or a change to an existing one — before any schema, code or test. Works out what is actually being asked for, whether to build it or integrate a third party (Trustpilot, Stripe, Calendly and the like), where the data lives, who may do what, and what happens when the vendor is down. Produces a decision, then hands to feature-schema.

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 page or the whole yoursite site (or a new site) for indexing readiness — cache headers, sitemap, robots, canonical, JSON-LD, internal links — and check the publish pipeline (Cloudflare purge/warm, IndexNow, GSC, Bing). Use before/after publishing content or launching a site.

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。

Surface the agent's assumptions about a phase approach before planning

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。

Socratic ideation and idea routing — think through ideas before committing to plans

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。

Ingest external plans with conflict detection against project decisions before writing anything.

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。

Gather phase context through adaptive questioning before planning. Use --auto to skip interactive questions (the agent picks recommended defaults). Use --chain for interactive discuss followed by automatic plan+execute. Use --power for bulk question generation into a file-based UI (answer at your own pace).

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 milestone completion against original intent before archiving

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。

Red team engagement planning is the foundational phase that defines scope, objectives, rules of engagement (ROE), threat model selection, and operational timelines before any offensive testing begins.

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Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.

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Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.

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 attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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。

Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.

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。