Use this skill always when you need to use execute_csharp_script_in_unity_editor tool to modify scenes, add, remove or modify game objects, components, scriptable objects or perform any other task in Unity Editor using C# scripts. Also covers when and how to use read_unity_console_logs and run_unity_tests tools as part of the same workflow and create or modify favourite scripts used to automate tasks.
- 📁 references/
- 📄 POWER.md
- 📄 SKILL.md
Expert FinOps guidance covering cloud, AI, and SaaS technology spend. Includes AI cost management, GenAI capacity planning, Anthropic billing, AWS (EC2, Bedrock, Savings Plans, CUR, commitment strategy), Azure (reservations, Savings Plans, AHB, OpenAI PTUs, portfolio liquidity), GCP (Vertex AI, Compute Engine, BigQuery), tagging governance, SaaS management (SAM, licence optimisation, SMPs, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Windsurf, Codex), ITAM, Databricks, Snowflake, OCI, and GreenOps. Use for any query about technology cost, commitment portfolio management, rightsizing, cost allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow. --- # FinOps - Expert Guidance > Built by OptimNow. Grounded in hands-on enterprise delivery, not abstract frameworks. --- ## How to use this skill This skill covers cloud, AI, SaaS, and adjacent technology spend domains. Read `references/optimnow-methodology.md` first on every query - it defines the reasoning philosophy applied to all responses. Then load the domain reference that matches the query. ### Domain routing | Query topic | Load reference | |---|---| | AI costs, LLM inference, token economics, agentic cost patterns, AI ROI, AI cost allocation, GPU cost attribution, RAG harness costs | `references/finops-for-ai.md` | | AI investment governance, AI Investment Council, stage gates, incremental funding, AI value management, AI practice operations | `references/finops-ai-value-management.md` | | GenAI capacity planning, provisioned vs shared capacity, traffic shape, spillover, throughput units | `references/finops-genai-capacity.md` | | AWS billing, EC2 rightsizing, RIs, Savings Plans, commitment strategy, portfolio liquidity, phased purchasing, CUR, Cost Explorer, EDP negotiation, RDS cost management, database commitments | `references/finops-aws.md` | | AWS Bedrock billing, Bedrock provisioned throughput, model unit pricing, Bedrock batch inference | `referenc
- 📁 assets/
- 📄 README.md
- 📄 SKILL.md
- 📄 spec.md
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- 📄 .nojekyll
- 📄 CNAME
- 📄 index.html
Deploy static sites, Vite apps, Hono apps, and Next.js apps to a live URL with a single API call. Built-in versioning with instant rollback -- no git required. Use when you need to deploy, host, or update a website.
- 📁 prompts/
- 📁 references/
- 📄 SKILL.md
Build Solana trading applications combining DFlow trading APIs with Helius infrastructure. Use this skill when: building swap UIs or trading terminals, integrating spot crypto swaps (imperative and declarative), trading on prediction markets, streaming real-time market data via WebSockets, implementing Proof KYC identity verification, submitting transactions via Helius Sender, or optimizing priority fees for trading. Requires helius-mcp MCP server. --- # Helius x DFlow — Build Trading Apps on Solana You are an expert Solana developer building trading applications with DFlow's trading APIs and Helius's infrastructure. DFlow is a DEX aggregator that sources liquidity across venues for spot swaps and prediction markets, and offers an Agent CLI for autonomous trading execution. Helius provides superior transaction submission (Sender), priority fee optimization, asset queries (DAS), real-time on-chain streaming (WebSockets, LaserStream), and wallet intelligence (Wallet API). ## MCP Router Surface
Reviews code for architectural compliance and design integrity
Full-featured Obsidian expert covering every official feature, plugin, and syntax.
- 📄 evaluation-rubric.md
- 📄 partner-voice.md
- 📄 question-bank.md
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Use when an implementer is blocked mid-work by a design flaw, contradiction, or unforeseen fork with 2-3 ways to resolve it - produces a defended pick that unblocks the work and leaves an audit trail so the decision can be revisited if it later proves wrong --- # Adversarial Critique ## Overview Stress-test a mid-implementation design fork by dispatching sub-agents that argue opposing options across three structured rounds. Produces a defended pick with invariants and acceptance criteria — not a "both approaches work" summary.
INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI.
Adapt designs to work across different screen sizes, devices, contexts, or platforms. Implements breakpoints, fluid layouts, and touch targets. Use when the user mentions responsive design, mobile layouts, breakpoints, viewport adaptation, or cross-device compatibility.
Interactive visual canvas for structured communication between agent and user. Opens a rich annotatable document in the user's browser where the user reviews, comments, answers questions, and submits feedback. Supports planning, architecture reviews, code reviews, discovery interviews, implementation summaries, proposals, decision documents, and explanations.
