> ## Documentation Index > Fetch the complete documentation index at: https://docs.orq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Orq Skills for code assistants > Install Orq Skills and slash commands in Claude Code, Cursor, Codex, and other AI assistants to run agent, evaluation, and trace workflows. ## Overview **Orq Skills** are pre-built, reusable workflows from the [orq-ai/assistant-plugins](https://github.com/orq-ai/assistant-plugins) repository. They come in two forms: * **Skills**: multi-step workflows that require reasoning, such as building an agent, running an experiment, or analyzing trace failures. * **Commands**: quick slash-command actions for immediate results, such as listing traces or showing analytics. Both are built on the [Agent Skills](https://agentskills.io) standard format, which means they work with any compatible assistant: [Claude Code](/ai-studio/integrations/code-assistants/claude-code), [Cursor](/ai-studio/integrations/code-assistants/cursor), Gemini CLI, and others. Each skill encodes best practices from prompt engineering, agent design, evaluation methodology, and experimentation into a repeatable, triggered workflow. ## Prerequisites * An active **Orq.ai** account * An [API key](/ai-studio/organization/api-keys) * The **Orq MCP** server connected to the assistant (see [MCP Quickstart](/ai-studio/integrations/code-assistants/orq-mcp)) ## Installation Choose the option that matches the assistant used: ```bash Claude Code plugin theme={"theme":{"light":"github-light","dark":"github-dark"}} # Installs skills, commands, agents, and the MCP server in one step claude plugin marketplace add orq-ai/assistant-plugins claude plugin install orq-skills@orq-claude-plugin ``` ```bash Codex theme={"theme":{"light":"github-light","dark":"github-dark"}} # Skills (writes to ~/.agents/skills/, which Codex scans by default) npx skills add orq-ai/assistant-plugins --agent codex -g -y # orq.ai MCP server (writes [mcp_servers.orq-workspace] to ~/.codex/config.toml) codex mcp add orq-workspace \ --url https://my.orq.ai/v2/mcp \ --bearer-token-env-var ORQ_API_KEY ``` ```bash npx skills CLI theme={"theme":{"light":"github-light","dark":"github-dark"}} # Installs skills only: works with Cursor, Gemini CLI, and other compatible assistants npx skills add orq-ai/assistant-plugins ``` Use **one path only**. The Claude Code plugin install includes the MCP server. Running the Claude Code plugin path alongside any other path will install the MCP server twice. Commands (`/orq:quickstart`, `/orq:workspace`, and others) and agents are only available with the Claude Code plugin. ## Verify **Claude Code:** Run the interactive onboarding command to confirm everything is working: ``` /orq:quickstart ``` **Cursor, Gemini CLI, and others:** Describe a task (e.g., "list my Orq.ai agents") and confirm the skill responds correctly. ## Commands Quick-action slash commands available in [Claude Code](/ai-studio/integrations/code-assistants/claude-code). Use `/orq:` to trigger them. | Command | Description | Usage | | --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | | **quickstart** | Interactive onboarding: credentials, MCP setup, skills tour | `/orq:quickstart` | | **workspace** | Workspace overview: [Agents](/ai-studio/ai-engineering/build-agents), [Deployments](/ai-studio/ai-engineering/deployments), [Prompts](/ai-studio/prompts/prompts), [Datasets](/ai-studio/optimize/datasets), [Experiments](/ai-studio/optimize/experiments) | `/orq:workspace [section]` | | **traces** | Query and summarize [Traces](/ai-studio/observability/traces) with filters | `/orq:traces [--deployment name] [--status error] [--last 24h]` | | **models** | List available AI models by provider | `/orq:models [search-term]` | | **analytics** | Usage [Analytics](/ai-studio/observability/quickstart): requests, cost, tokens, errors | `/orq:analytics [--last 24h] [--group-by model]` | | **orq-manage-skills** | Manage **Orq.ai** [Skills](/ai-studio/ai-engineering/skills) (platform entities): list, get, create, update, retire, delete | `/orq:orq-manage-skills [list\|get\|create\|update\|retire\|delete] [name-or-id]` | ## Skills Skills are triggered by describing what is needed. The assistant picks the right skill automatically. | Skill | Description | Source | | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------- | | **build-agent** | Design, create, and configure an **Orq.ai** [Agent](/ai-studio/ai-engineering/build-agents) with tools, instructions, [Knowledge Bases](/ai-gateway/features/knowledge-bases), and [Memory](/ai-studio/ai-engineering/memory-stores#manage-memories-and-documents) | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-build-agent/SKILL.md) | | **build-evaluator** | Create validated LLM-as-a-Judge [Evaluators](/ai-studio/marketplace#evaluators) following evaluation best practices | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-build-evaluator/SKILL.md) | | **evaluator-alignment** | Align an existing LLM judge (boolean, categorical, or numeric) to human judgment: measure how often it changes its mind, group the least reliable cases, rewrite the judge prompt, and recreate the [Evaluator](/ai-studio/marketplace#evaluators) after approval | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-evaluator-alignment/SKILL.md) | | **analyze-traces** | Read production [Traces](/ai-studio/observability/traces), identify what is failing, build failure taxonomies, and categorize issues | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-analyze-traces/SKILL.md) | | **run-experiment** | Create and run **Orq.ai** [Experiments](/ai-studio/optimize/experiments): compare configurations with specialized agent, conversation, and RAG evaluation | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-run-experiment/SKILL.md) | | **generate-synthetic-dataset** | Generate and curate evaluation [Datasets](/ai-studio/optimize/datasets): structured generation, quick from description, expansion, and dataset maintenance | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-generate-synthetic-dataset/SKILL.md) | | **invoke-deployment** | Invoke **Orq.ai** [Deployments](/ai-studio/ai-engineering/deployments), [Agents](/ai-studio/ai-engineering/build-agents), and models via the Python SDK or HTTP API, with correct variable substitution, streaming, and identity tracking | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-invoke-deployment/SKILL.md) | | **setup-observability** | Instrument LLM applications with Orq.ai tracing. Covers AI Gateway (zero-code traces) and OpenTelemetry/OpenInference. Guides from framework detection through baseline verification to trace enrichment | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-setup-observability/SKILL.md) | | **compare-agents** | Run cross-framework agent comparisons: compare any combination of Orq.ai, LangGraph, CrewAI, OpenAI Agents SDK, or Vercel AI SDK agents head-to-head on the same dataset using `evaluatorq` | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-compare-agents/SKILL.md) | | **red-team** | Run adversarial attacks against deployed agents or static datasets with the `evaluatorq` red team CLI. Covers OWASP-ASI (agentic: goal hijacking, tool misuse) and OWASP-LLM (model-level: prompt injection, system prompt leakage) | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-red-team/SKILL.md) | | **evaluatorq** | Write and run `evaluatorq` evaluation scripts (Python or TypeScript) for a single agent or deployment. Supports custom scorers, dataset-driven runs, and LLM-as-a-Judge [Evaluators](/ai-studio/marketplace#evaluators) | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/evaluatorq/SKILL.md) | | **simulate-agent** | Run multi-turn simulations with `evaluatorq` primitives (`simulate()`, `generate_and_simulate()`, `wrap_simulation_agent()`): drive an agent under test with a simulated user and score each turn with a built-in judge | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-simulate-agent/SKILL.md) | | **manage-skills** | List, inspect, create, update, retire, and delete **Orq.ai** [Skills](/ai-studio/ai-engineering/skills) (platform entities). Handles naming rules, template integration (`{{skill.key}}`), reference scanning, and safe deletion | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-manage-skills/SKILL.md) | | **orq-cli** | Drive the `orq` command-line interface: install check, authentication, workspace selection, `orq doctor` troubleshooting, and read/write commands with JSON output | [SKILL.md](https://github.com/orq-ai/assistant-plugins/blob/main/skills/orq-cli/SKILL.md) | ## Example workflows ### Instrument an existing app ``` "Add orq.ai tracing to my app" → setup-observability /orq:traces --last 1h # Verify traces are flowing "Analyze these failures" → analyze-traces ``` ### Build a new agent ``` "I need a customer support agent" → build-agent "Create test cases for it" → generate-synthetic-dataset "Build an evaluator for response accuracy" → build-evaluator "Run an experiment to get a baseline" → run-experiment ``` ### Debug production issues ``` /orq:traces --status error --last 24h # Find errors "Analyze these failures" → analyze-traces "Re-run the experiment to verify the fix" → run-experiment ``` ### Improve an existing agent ``` /orq:analytics --group-by deployment # Spot high error rates "Analyze traces for the checkout agent" → analyze-traces "Build evaluators for the failure modes" → build-evaluator "Generate a dataset covering edge cases" → generate-synthetic-dataset "Run an experiment and compare" → run-experiment ``` ### Improve an existing prompt ``` "Create test cases to compare before and after" → generate-synthetic-dataset "Build an evaluator for a specific dimension" → build-evaluator "Run an experiment: current vs optimized prompt" → run-experiment "Analyze traces for failure cases" → analyze-traces ``` ### Red team and simulate a new agent ``` "I need to simulate user conversations with my agent" → simulate-agent "Run adversarial tests against it" → red-team "Build evaluators for the discovered failure modes" → build-evaluator "Run an experiment to compare patched vs original" → run-experiment ``` ### Evaluate an agent with custom scorers ``` "Write an evaluatorq script for my support agent" → evaluatorq "Simulate edge-case personas against it" → simulate-agent "Red team the agent on prompt injection" → red-team ``` ## Resources Source repository for all skills, commands, and agents