Quick start

The fastest way to adopt Ratel is to not write the integration yourself. Ratel ships five open-source skills for coding agents — Claude Code, Cursor, Codex, and 40+ others — that audit your agent codebase, plan its observability, integrate Ratel, and fix what the audit finds.

Prefer to write the code by hand? Jump to the SDK quickstarts.

Install the skills

npx skills add ratel-ai/skills -y --all

pnpm dlx skills add ratel-ai/skills -y --all

yarn dlx skills add ratel-ai/skills -y --all

bunx skills add ratel-ai/skills -y --all

That installs all five skills into the current project (./.claude/skills/ for Claude Code, the equivalent location for other supported agents). The -y flag accepts all prompts; --all installs all skills into all agents; add -g to install globally (~/.claude/skills/).

Per-skill install:

npx skills add ratel-ai/skills --skill ratel-assessment

pnpm dlx skills add ratel-ai/skills --skill ratel-assessment

yarn dlx skills add ratel-ai/skills --skill ratel-assessment

bunx skills add ratel-ai/skills --skill ratel-assessment

The skills CLI is Vercel Labs' open agent skills tool — compatible with Claude Code, Cursor, Codex, OpenCode, Gemini CLI, and 40+ other coding agents. The suite itself is MIT-licensed at ratel-ai/skills.

Paste this into your coding agent

Want a read on your agent first? The assessment is free and static — no setup:

Run npx skills add ratel-ai/skills --all and use the skills to assess the agents in this codebase

Ready to wire Ratel in? Go straight to the integration plan:

Run npx skills add ratel-ai/skills --all and use the skills to integrate Ratel in this project.

Either entry point pulls in the whole suite. The assessment writes a report to .ratel/ratel-assessment-<date>.md, plus a branded, scored HTML version alongside it. The report ends with "Recommended next steps": every finding routes to the right follow-up skill, so you don't have to know the arc up front.

What's inside

Five skills. The first three run the engagement arc in order; the last two are the fix-skills the assessment routes to when it finds a long system prompt or weak tool/skill definitions.

How the skills chain

ratel-assessment                 → "here's what's weak; here's where Ratel fits"
        ↓ (Observability Weak/Missing)
ratel-observability-assessment   → "turn on Ratel's native OTLP telemetry"
        ↓
ratel-integrate                  → "here's how to roll Ratel out + A/B it"

ratel-assessment also branches to two fix-skills, conditional on findings:
        ├─ Prompt decomposition weak → ratel-decompose-prompt
        └─ Definition quality weak   → ratel-tune-definitions

The arc isn't a forced sequence — each report names which sibling to run next based on what it found. All reports land under .ratel/ in your repo, so they accumulate and stay diff-able.

Observability

Ratel telemetry is OpenTelemetry: the SDKs emit the retrieval-and-tool funnel natively as gen_ai.* spans plus a ratel.* overlay, exported as stock OTLP. Point those spans at the backend you already run — Langfuse, LangSmith, your own collector, or Ratel Cloud, coming soon.

ratel-observability-assessment plans that setup — greenfield or dual-exported into an existing provider — and the dashboards that prove value.

Write the code yourself

Everything the skills plan and apply, you can also write by hand: register your tools and prompt playbooks in Ratel catalogs, then hand your agent the three capability tools — search_capabilities, invoke_tool, get_skill_content. The per-language quickstarts cover both starting from scratch and migrating an existing agent: