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概要と使いどころ
AFFiNE is an open source workspace that combines docs, whiteboards, and lightweight databases in one product. This skill is useful when an agent needs to structure notes, maintain linked knowledge, or automate content workflows around AFFiNE workspaces and self-hosted deployments.
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。
The Mileva Method (CRISP) — Phase 5: Prove. Success validation against Phase R baseline metrics, test log review, and AI justification validation. Use after deployment. Triggers on "prove", "phase 5", "validate", "did it work", "success criteria", "measure results", or after go-live.
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。
Orchestrates the tdmcp feature team end-to-end: design/wireframe → build → integrate → QA → release. Use whenever the user wants to build, implement, develop, ship, or add one or more tdmcp features/tools/effects/controls/AI-prompts for TouchDesigner, or to run them through a coordinated design→develop→QA→deploy pipeline. Also use for follow-ups: re-run, continue, update, fix, or improve a feature/batch, ship the next version, or rebuild just one part of a previous run. Single feature or a whole batch.
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 Arcade MCP to create custom MCP servers and tools with OAuth-aware authorization, evals, and deployment paths for agent tool-calling 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。
Use Yao when an operator needs to package an AI agent workflow, hooks, MCP tools, memory scopes, and a reviewable web or chat interface into one deployable runtime.
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。
CI/CD pipeline and software-supply-chain exploitation: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, Gitea/Forgejo, self-hosted runners/agents, poisoned pipeline execution, artifact/cache abuse, dependency confusion, third-party Action trust, GitOps/registry poisoning, workload identity/OIDC, and emerging agentic CI/CD. Use this skill when the operator has access to a CI/CD system, source repository, build/deploy configuration, runner/agent, artifact/package/registry path, or software delivery trust chain.
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。
Respan for LLM observability. Use to set up tracing or the gateway, manage prompts (create/version/deploy), run evals, experiments, and datasets, or work with traces and logs. Trigger for any Respan platform task or when instrumenting an app to send telemetry to Respan.
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。
Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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, debug, modernize, or review ASP.NET Core applications with correct hosting, middleware, security, configuration, logging, and deployment patterns on current .NET.
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 fund economic models with sensitivity across deployment pace, exit multiples, and fee/carry structures for LP and GP returns. Use when modeling fund economics, projecting LP net returns, or analyzing fee-adjusted performance.
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。
Structures portfolio review with strategic fit assessment, divestiture candidate identification, and proceeds redeployment analysis. Use when reviewing corporate portfolios, identifying divestiture candidates, or planning asset dispositions.
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 LP commitment pacing with deployment curves, distribution assumptions, and NAV projection for portfolio planning. Use when modeling commitment pacing, projecting LP cash flows, or planning new fund allocations.
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。
All-in-one fullstack dev engine. /aura: 46 modes (build/fix/clean/deploy/review/spec/lore/ax/experiment/payment/debug/qa/orchestrate/escalate+), 6-layer security with 32 hooks, tiered models (ZERO/ECO/PRO/MAX), 8 languages, 16 specialized agents, SPEC/EARS/TRUST5/XLOOP/RALF/Autopus absorbed. ~55% token savings.
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。
6-phase multi-agent pipeline with approval gates. Orchestrates Plan→Design→Implement→Review→Test→Deploy phases with explicit entry/exit criteria, phase-level agent assignment, and human-in-the-loop approval checkpoints.
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。
Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.
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。
Kubernetes cluster management and troubleshooting. Query pods, deployments, services, logs, and events. Supports context switching, scaling, and rollout management. Use for Kubernetes debugging, monitoring, and operations.
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。
Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs
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, organize, and manage Helm charts for templating and packaging Kubernetes applications with reusable configurations. Use when creating Helm charts, packaging Kubernetes applications, or implementing templated deployments.
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。
PostgreSQL database agent: deploy locally (Docker/K8s), manage schemas, run queries, analyze performance, configure permissions, and seed 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。
Scaffolds CI/CD pipeline definitions using GitHub Actions for build, test, lint, and deploy 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。
Scaffold a new iOS app with SDLC and submission-safe defaults — diffable project files, app + test targets, deployment-target policy, a git-stamped build number, and the Info.plist keys the pre-flight checker wants so the project passes TestFlight from day one. Use when starting a new iOS project.
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 team Docker image from a manifest in .archon/teams/. Resolves plugin paths, generates Dockerfile with additive copy-only, makes plugins read-only, bakes team CLAUDE.md.
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。
Audits websites for AI search visibility (GEO/SEO) and generates deployable artifacts (llms.txt, schema JSON-LD, robots.txt). 7-dimension scorecard tailored by site type and size, grounded in Princeton KDD 2024, Toronto 2025, and ICLR 2026 research. This skill should be used when optimizing for ChatGPT, Perplexity, Google AI Overviews, or other AI search engines. Also triggered by queries about AI search optimization, generative engine optimization, llms.txt, AI crawler configuration, schema markup for AI, citation optimization, or Google AI Mode.
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 this skill when working with Twilio communication APIs for SMS/MMS messaging, voice calls, phone number management, TwiML, webhook integration, two-way SMS conversations, bulk sending, or production deployment of telephony features. Includes official Twilio patterns, production code examples from Twilio-Aldea (provider-agnostic webhooks, signature validation, TwiML responses), and comprehensive TypeScript examples.
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。