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The /pareta skill

A Pareta skill teaches an AI coding agent to drive the pareta CLI as a slash command — run metered inference against model="auto" and benchmark auto against frontier models on your own data. It's a single SKILL.md that works in both Claude Code and Codex, because they share the same skill format.

Skill vs. MCP server​

Two ways to put Pareta inside a coding agent — and you can use both:

  • The MCP server gives the agent Pareta as structured tools (chat, run_eval, …) it calls directly. Best when you want first-class, auto-discovered tools.
  • This skill is instructions — a SKILL.md the agent reads and follows, driving the pareta shell command. Best when you want a /pareta slash command and a guided workflow, and you've already installed the CLI.

Prerequisite​

The skill drives the CLI, so install it and set a key:

pipx install "pareta[cli]"            # or: pip install "pareta[cli]"
export PARETA_API_KEY="pareta_sk_…"   # mint one in the dashboard

Install in Claude Code​

Copy the skill into your personal skills directory (available from any project):

mkdir -p ~/.claude/skills/pareta
curl -fsSL https://raw.githubusercontent.com/Pareta-AI/pareta/main/skills/pareta/SKILL.md \
  -o ~/.claude/skills/pareta/SKILL.md

For a single repo, drop it at .claude/skills/pareta/SKILL.md instead. Then /pareta is available — and Claude Code also invokes it automatically when a request matches.

Install in Codex​

Codex uses the same skill format; only the directory differs:

mkdir -p ~/.codex/skills/pareta
curl -fsSL https://raw.githubusercontent.com/Pareta-AI/pareta/main/skills/pareta/SKILL.md \
  -o ~/.codex/skills/pareta/SKILL.md

For a single repo, check it in at .codex/skills/pareta/SKILL.md. Codex loads skills automatically when the task matches. (Codex's older custom prompts in ~/.codex/prompts/ are deprecated in favor of skills.)

What it does​

Once installed, ask in plain language — "check whether Pareta can extract key fields from contracts, prove it on my rows, then run it." The skill walks the agent through:

  1. pareta tasks match — check how the job will be scored: a benchmarked scorer, a general lane, or an honest unsupported.
  2. pareta chat — run inference against model="auto" (Pareta plans, routes, verifies, and falls back to frontier when needed).
  3. pareta evals run --models auto --frontier — benchmark auto against frontier baselines on your own JSONL data.
  4. pareta auto metrics / pareta auto compare — watch spend + projected savings, and run one-prompt frontier side-by-sides.

It bakes in the guardrails: inference / eval / compare spend your org balance, so the agent confirms before spending.

Next steps​