341 件 ・ 関連度順
概要と使いどころ
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
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 Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent a...
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。
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
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。
Designs and documents structured incident response playbooks that define step-by-step procedures for specific incident types aligned with NIST SP 800-61r3 and SANS PICERL frameworks. Covers playbook structure, decision trees, escalation criteria, RACI matrices, and integration with SOAR platforms. Activates for requests involving IR playbook creation, incident response procedure documentation, response runbook development, or SOAR playbook 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。
会話の開始時に使えるスキルを確認し、応答や作業の前に読み込む手順を定めます。作業方法を決めるスキルを優先し、実行環境ごとの案内につなぎます。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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。
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-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。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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。
Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.
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 layered collective defense using alarm signaling, role mobilization, and proportional response. Covers threat detection, alert propagation, immune response patterns, escalation tiers, and post-incident recovery for distributed systems and organizations. Use when designing defense-in-depth where no single guardian covers all threats, building incident response that scales with severity, or when current defense is over-reactive to every alert or under-reactive to genuine threats.
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。
Configuring Salesforce Email-to-Case: Standard vs On-Demand mode selection, routing address setup, email threading via Lightning tokens, auto-response rules, attachment limits, and per-address case field defaults. Trigger keywords: email-to-case, routing address, on-demand email-to-case, email threading, case from email, email agent, routing address setup, Case.settings-meta.xml, EmailToCaseRoutingAddress, emailServicesAddress, isVerified, caseOrigin, enableThreadTokenInBody, useEmailHeadersForThreading, overEmailLimitAction, unauthorizedSenderAction, enableE2CAttachmentAsFile, saveEmailHeaders, newEntityRecordType, casePriority, EmailMessage.EmailRoutingAddressId, auto-response loop. NOT for the wider case layer (queues, escalation rules, case teams, entitlements) - use admin/case-management-setup. NOT for routing case work items to agents after creation - use admin/omni-channel-routing-setup. NOT for the email templates or letterheads themselves - use admin/email-templates-and-alerts.
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。
Manage the MoltBot A2A Hub — register agents, search the registry, relay messages, and stream responses. Use when working with the A2A agent-to-agent protocol hub deployed at a2a-hub.fly.dev.
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。
Drafts answers to the questions in a received RFP, RFQ or tender from the organisation's past responses, answer library, case studies, policies and other documents provided: one row per buyer question with a draft answer marked Reused, Adapted or Missing, its source and date, and a legal or pricing review flag on every answer touching terms, liability, warranties, insurance, data protection or price. Use when the user asks to "draft our RFP response", "answer this tender from our past proposals", "pre-fill the bid questions", "first pass at the RFQ answers" or "reuse our previous bid content". Do not use for writing the buyer's own RFP or requirements, use rfp-requirements-pack instead; for security or due diligence questionnaires use vendor-security-questionnaire-prefill; for scoring received supplier responses use rfp-comparison-pack. Drafts for human review; never approves, authorises or signs off.
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。
Rust 製バックエンド HTTP サーバーフレームワーク fandhe-backend のリファレンス。 fandhe-backend-core / -http / -routes。Server, BoundServer, Router（merge）, Handler。 4 拡張点 Middleware（on_response_with_status）/ UpgradeHandler / RequestGate / Interceptor。 Diagnostics, StreamingResponse, sans-IO HTTP/1.1, graceful shutdown。 プラグイン cors, websocket（WsSender）, graphql, openapi, webrtc 等。
OpenAI API (developers.openai.com) の Responses API リファレンス。 text generation, embeddings, structured outputs, streaming, function calling, programmatic tool calling, conversation state, prompt caching, reasoning models, background mode, compaction, multi-agent。 webhooks, error codes, rate limits, Realtime API server controls。 公式 SDK (Python / JavaScript(JS) / .NET / Java / Go / Ruby), openai CLI。 Chat Completions / Assistants からの移行, deprecations。
Orchestrator for the eval-output skill suite — evaluate LLM and agent outputs for quality, accuracy, helpfulness, and safety using structured rubrics and LLM-as-judge techniques. Load when the user says "evaluate this output", "score this response", "run an eval", "LLM as judge", "evaluate agent output", "how good is this response", "rate this answer", "eval this", or provides an LLM output that should be assessed for quality. Single entry point for all output evaluation 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。
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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。
Deploys and configures CrowdStrike Falcon EDR agents across enterprise endpoints to enable real-time threat detection, behavioral analysis, and automated response. Use when onboarding endpoints to EDR coverage, configuring detection policies, or integrating Falcon telemetry with SIEM platforms. Activates for requests involving CrowdStrike deployment, Falcon sensor installation, EDR policy configuration, or endpoint detection and response.
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。
Builds a structured ransomware incident response playbook aligned with the CISA StopRansomware Guide and NIST Cybersecurity Framework. Covers preparation, detection, containment, eradication, recovery, and post-incident phases with actionable checklists. Activates for requests involving ransomware response planning, CISA compliance, incident response playbook creation, or ransomware preparedness 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。
Builds real-time incident response dashboards in Splunk, Elastic, or Grafana to provide SOC analysts and leadership with situational awareness during active incidents, tracking affected systems, containment status, IOC spread, and response timeline. Use when IR teams need unified visibility during incident coordination and post-incident reporting.
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。
顧客や取引先が参加するチャットで、エージェントの応答条件と送信内容を定めるスキル。呼びかけへの応答、内部情報の保護、承認用の下書きへの切り分けを扱います。
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distribution", "compartmental model", "population PK", "popPK", "NONMEM", "nlmixr2", "Pharmpy", "Monolix", "exposure-response", "Emax", "EC50", "indirect response", "effect compartment", "TMDD", "PBPK", "bioequivalence", "RSABE", "ABEL", "allometric scaling", "first-in-human", "MABEL", "drug-drug interaction", "DDI", "ICH M12", "concentration-QTc", "therapeutic drug monitoring", "MIPD", and "dosing regimen".
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。
Deploys and configures CrowdStrike Falcon EDR agents across enterprise endpoints to enable real-time threat detection, behavioral analysis, and automated response. Use when onboarding endpoints to EDR coverage, configuring detection policies, or integrating Falcon telemetry with SIEM platforms. Activates for requests involving CrowdStrike deployment, Falcon sensor installation, EDR policy configuration, or endpoint detection and response.
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。
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.
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。