「deploy」の検索結果

Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

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。

What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).

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。

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

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 universal wisdom voices from all traditions simultaneously - Hindu, Buddhist, Taoist, Abrahamic, Indigenous, Scientific - revealing their unified recognition of consciousness navigation.

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。

Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.

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 ONLY when explicitly invoked. Puts Fusionaly on a Linux server you reach over SSH with a key. Optionally creates a Hetzner server and the Cloudflare DNS record. The user runs the one interactive install command; the agent prepares and checks everything around it.

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 new skills, editing existing skills, or verifying skills work before deployment

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。

Setup and optimize cryptocurrency mining operations — AI-powered mining (soul.md protocol), parallel agent deployment, accumulation strategies, and performance optimization.

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。

Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

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。

High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.

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。

Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

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。

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

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。

/cs:ai-act-readiness <system> — EU AI Act 6-question forcing interrogation. Use during AI-system intake, before EU deployment, or during annual compliance refresh as Article 113 obligations phase in (2025-02-02 / 2025-08-02 / 2026-08-02 / 2027-08-02).

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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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 a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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 building, testing, and deploying JavaScript/TypeScript applications. Reach for Bun when you need to run scripts, manage dependencies, bundle code, or test applications with a single unified tool.

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。

Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.

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 leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

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。

Plan and implement infrastructure, CI/CD, container, deployment, observability, and operational configuration changes with least privilege, staged validation, and rollback awareness. Use for pipelines, infrastructure as code, Kubernetes, containers, release automation, monitoring, and production-readiness work; do not use to perform destructive production actions without explicit authorization.

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。

Scan the codebase for common security issues: hardcoded secrets, SQL injection vectors, XSS, unvalidated inputs, and insecure defaults. Use before deploying, as part of code quality review, or when asked to check for security issues.

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。

Practical security review and hardening guidance for application code in Python, JavaScript/TypeScript, and Go. Use this skill whenever the user asks for a security review or audit, wants to harden authentication (password hashing, sessions, JWT, OAuth2/OIDC, MFA), fix OWASP Top 10 issues (injection, XSS, IDOR, CSRF, SSRF, deserialization), threat-model with STRIDE, clean up secrets and .env handling, or set up dependency scanning (pip-audit, npm audit, osv-scanner, gitleaks, SBOM). Also trigger for "make this secure", "is this safe to deploy", bcrypt/argon2id, rate limiting logins, RBAC/ABAC, CSP and security headers, cookie flags, and secure-by-default coding 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。

Go / no-go gate before any deployment. Checks failure modes, rollback plan, cost, production bar, and definition of done. Trigger before releasing to any environment that carries real consequences.

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。

Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.

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。

Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.

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。