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Found 1,954 Skills
Guide users through defining their pricing strategy for an AI product or SaaS. Covers billing model selection (usage-based, subscription, hybrid), subscription tier pricing, credit/overage costs, real-time vs invoice billing trade-offs, existing PSP integration, custom currency vs fiat, and pricing dimensions. Ends with a personalised pricing strategy summary, MRR projection, visual output (HTML or PDF), and tool recommendations. Use when a user wants to define their pricing, figure out how to charge for their AI product, decide between billing models, understand the real-time vs invoice billing trade-off, or evaluate what tools to use for monetisation.
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.
Sandbox escape playbook. Use when breaking out of Python sandbox, Lua sandbox, seccomp filter, chroot jail, container/Docker, browser sandbox, or namespace isolation to achieve unrestricted code execution or file access.
Test trading strategies on historical data to evaluate performance, risks, and profitability.
Plan Southeast Asia market entry including mode selection, regulatory requirements, cultural research, and go-to-market timeline. Use this skill when the user is considering expansion to SEA, evaluating entry modes (marketplace vs direct vs distributor), or conducting market feasibility studies — even if they say 'should we enter Vietnam', 'how to sell in Indonesia', 'SEA expansion plan', or 'find a local distributor'.
Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.
Game building mechanics case studies and decision frameworks. Use when designing building systems, evaluating trade-offs, or learning from existing games. Reference-only skill with detailed analysis of Fortnite, Rust, Valheim, Minecraft, No Man's Sky, and Satisfactory building systems.
Boomi platform help — enterprise iPaaS, 1000+ connectors, API Management, Data Hub MDM, Flow low-code builder, Event Streams, B2B/EDI, AgentStudio AI agents, MCP support. Use when Boomi integration keeps failing or data isn't syncing, connector won't authenticate to SAP or Salesforce, per-connection pricing is spiraling and you need to optimize, debugging a Boomi process is painful with vague error messages, evaluating Boomi vs MuleSoft vs Workato, or setting up API management and governance. Do NOT use for simple Zapier/Make automations (use /sales-integration) or MuleSoft-specific questions (use /sales-mulesoft).
Invoke orq.ai deployments, agents, and models via the Python SDK or HTTP API. Use when a user wants to call a deployment with prompt variables, invoke an agent in a conversation, or call a model directly through the AI Router. Do NOT use for creating or editing deployments/agents (use optimize-prompt or build-agent). Do NOT use for running evaluations (use run-experiment).
Expert content strategist and creator specializing in multi-platform content development, brand storytelling, and audience engagement. Focused on creating compelling, valuable content that drives b...
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.