Build with AI
The Ratel skills suite: five open-source skills that audit your agent codebase, plan observability, integrate Ratel, and fix what the audit finds.
The Ratel skills suite is 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. The Quick start has the one-liner and the two prompts that kick it off; this page is the reference for what each skill does and how they fit together.
This page covers the skills suite, Ratel's installable skills for coding agents; for the skills concept, reusable playbooks registered in a SkillCatalog, see Tools, MCP & Skills.
Install the skills
npx skills add ratel-ai/skills -y --allpnpm dlx skills add ratel-ai/skills -y --allyarn dlx skills add ratel-ai/skills -y --allbunx skills add ratel-ai/skills -y --allThat 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-assessmentpnpm dlx skills add ratel-ai/skills --skill ratel-assessmentyarn dlx skills add ratel-ai/skills --skill ratel-assessmentbunx skills add ratel-ai/skills --skill ratel-assessmentThe 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.
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.
| Skill | What it does | When to fire |
|---|---|---|
ratel-assessment | Static read of the agent codebase (TypeScript or Python). Writes a 12-dimension scorecard with evidence-backed findings to <repo>/.ratel/ — markdown plus a scored HTML version. | First touch. Zero setup. |
ratel-observability-assessment | Detects the OpenTelemetry backend you run, plans turning on Ratel's native OTLP telemetry, and proposes the dashboards that prove value. | After assessment flags Observability as Weak / Missing. |
ratel-integrate | Plans the rollout: integration mode (direct SDK / Ratel Local / hybrid), pilot scope, and an A/B test tied to Ratel's native-telemetry metrics. Covers both SDKs. | After observability is in. |
ratel-decompose-prompt | Breaks a monolithic system prompt into a lean core plus extractable skills registered in a SkillCatalog. | When assessment flags Prompt decomposition as Weak / Missing. |
ratel-tune-definitions | Rewrites tool and skill definitions — descriptions, names, schemas, tags — for retrievability and model usability. Writes a before/after plan. | When assessment flags Definition quality as Weak / Missing. |
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-definitionsThe 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.