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概要と使いどころ
AI-powered skill recommendation engine for dotnet-agent-harness. Analyzes project context and recommends relevant skills based on detected frameworks, packages, and patterns. Triggers on: recommend skill, suggest skills, what skill should I use, skill suggestion, project analysis recommendation.
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。
Gates CI on perf regressions. Automated threshold alerts, baseline tracking, trend 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。
Defines baseline C# conventions loaded first -- naming, file layout, style rules.
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。
Extend dev-software-engineer with .NET/C# backend specialization for Clean Architecture, CQRS, EF Core, and production-ready API practices. Use when tasks involve ASP.NET endpoints, handlers, services, dependency injection, MediatR, migrations, or backend performance/resilience and should follow the base implementation workflow.
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.
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 and enforce privacy and data protection controls across application, API, and database layers. Use when tasks involve PII handling, retention/deletion policies, consent, data minimization, auditability, or compliance-oriented architecture changes.
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。
Git commit and pull request conventions. USE FOR: writing commit messages; checking Conventional Commits and gitmoji; branch naming; rebase workflow on develop; PR titles and target branch. PRIORITY: this skill overrides other rules when creating commits/PRs. DO NOT USE FOR: code implementation (use code-implementation skill); testing (use test-writing skill); general documentation (use documentation 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 standing up a fresh deployment of this codebase (or the public vigyan-site-os template) — populates company identity, branding and starting content without committing any of it, and decides for each value whether it belongs in platform env, encrypted DB secrets, DB config, or an admin screen.
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 writing or applying a migration in site-os, adding a table or column, storing files, or when a migration fails with "must be owner of table" — covers the apply procedure, the postgres-ownership rule, money/rate units, and where files belong.
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 tests for a completed phase based on UAT criteria and 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。
Execute all plans in a phase with wave-based parallelization
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 or update project documentation verified against the codebase
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。
Query, inspect, or refresh codebase intelligence files in .planning/intel/
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。
Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.
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。
Identifies and exploits insecure local data storage vulnerabilities in Android and iOS mobile applications including unencrypted databases, world-readable files, insecure SharedPreferences, plaintext credential storage, and improper keychain/keystore usage. Use when performing mobile penetration testing focused on OWASP M9 (Insecure Data Storage) or assessing compliance with MASVS-STORAGE requirements. Activates for requests involving mobile data storage security, local storage exploitation, SharedPreferences analysis, or mobile data leakage assessment.
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。
Tests and exploits deep link (URL scheme and App Link) vulnerabilities in Android and iOS mobile applications to identify unauthorized access, data injection, intent hijacking, and redirect manipulation. Use when assessing mobile app attack surface through custom URI schemes, Android App Links, iOS Universal Links, or intent-based navigation. Activates for requests involving deep link security testing, URL scheme exploitation, mobile intent abuse, or link hijacking.
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。
This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.
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。
Evaluates and selects Threat Intelligence Platform (TIP) products based on organizational requirements including feed integration capability, STIX/TAXII support, workflow automation, analyst interface, and total cost of ownership. Use when conducting a TIP procurement, migrating between TIP solutions, or assessing whether the current TIP meets program maturity requirements. Activates for requests involving ThreatConnect, MISP, OpenCTI, Anomali, EclecticIQ, or TIP procurement decisions.
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。
BloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attac
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。
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.
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 anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
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 DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns
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。