Loading...
Loading...
Found 5,783 Skills
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).
Use this skill when the user mentions creating a payment link, paying a paymentId / a2a_... link, or checking a2a payment status. Wraps `onchainos payment a2a-pay` agent-to-agent payment protocol: seller-side `create`, buyer-side `pay` via EIP-3009 + TEE signing, and `status` query. Buyer-side trust is delegated to upstream — the skill signs whatever the on-server challenge declares. Do NOT use for external HTTP 402 resources — use okx-x402-payment. Do NOT use for wallet balance / transfer / login — use okx-agentic-wallet.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Harness Engineering Phase 3: Establish cross-session state management to solve the problem of agents forgetting previous conversations. Create three files: tasks.json (task list), progress.md (progress record), and init.sh (environment initialization script). Use this skill immediately when the user says phrases like "establish task management", "make agent remember progress", "create tasks.json", "maintain state across sessions", "agent doesn't remember what was done last time", "create progress file", or "initialize state management". Prerequisites: harness-step1 and harness-step2 have been completed (the project has AGENTS.md and docs/ knowledge base).
Interact with the Cargo platform via CLI. Use when the user wants to execute an action, run a workflow, trigger a batch, message an AI agent, query orchestration runtime tables (runs/batches/spans/records) with SQL, fetch segment records, or inspect a model schema.
Use Claude Code's autonomous agent loop with DeepSeek V4 Pro, OpenRouter, or any Anthropic-compatible backend at up to 17x lower cost.
Create and configure configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
Create and improve agent skills following the Agent Skills specification. Use when asked to create, write, or update skills.
AI agent skill for using deepsec, the agent-powered security vulnerability scanner for large codebases
Designs and reviews WebMCP instrumentation for existing web apps, especially SPAs. Use when adding agent-accessible tools, route maps, prompts, or WebMCP workflows to a React, Vue, Angular, or vanilla browser app, or when deciding whether WebMCP is the right fit.
Generates YAML signal configs for agent simulation experiments. Use when the user wants to define what signals to track, how to extract them from run artifacts, and how to aggregate them into experiment-level metrics. Trigger when users say: "generate a signal config", "create signals for my experiment", "I want to track [metric]", "write a signal YAML", "set up extraction for [thing]", "how do I measure [behavior] across runs", "configure signals for [experiment]", "create a signal config", "create signal config file", or "build a signal config".