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logfire-setup — Claude Code skill: route Logfire setup to the right

Takes a broad "set up Logfire" request and routes the agent to the right specialist: app instrumentation, infrastructure, evals, frontend, Gateway.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add pydantic/skills --skill logfire-setup -a claude-code
Open on GitHub

What logfire-setup does

This skill handles the case where you ask an agent to wire up Pydantic Logfire without saying what to monitor. It reads AGENTS.md, CLAUDE.md and README.md, checks the language, runtime and package manager, then picks a specialist from a table: logfire-instrumentation, logfire-frontend, logfire-infrastructure, logfire-evals, logfire-query, logfire-ui, logfire-gateway or logfire-coding-agents.

Before that it confirms the project with whoami and derives the CLI target from your Logfire URL. For JS/TS projects without uv it uses the external-prefix npm fallback rather than plain npx, since npx can execute a repository-local binary. If your request is already scoped to one surface, the skill loads that specialist directly and skips the rest of the table.

Ordinary reversible setup runs without asking approval at every step. It pauses for one concrete question on browser auth, project ambiguity, a material jump in cost or telemetry volume, deploy or infra changes. Tokens are never printed or committed; the single exception is reading the token key from .logfire/logfire_credentials.json programmatically to hand a non-native-SDK app its write token.

What it helps with

  • connect Logfire to a project when you don't know which surface to start with
  • decide between app instrumentation and infrastructure monitoring
  • collect traces, logs and metrics from Python, JS/TS or Rust code
  • set up evals for an AI agent against a test-case dataset
  • capture browser errors, RUM and Web Vitals

Example prompts for the agent

  • set up Logfire in this project
  • add Logfire to my project and send as much data as would be useful
  • get me set up properly with Logfire

How to install logfire-setup

1Skills CLI (Vercel)

npx skills add pydantic/skills --skill logfire-setup -a claude-code

2Manually: put the files in place

~/.claude/skills/logfire-setup/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/logfire-setup/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about logfire-setup

How do I install logfire-setup in Claude?

Run npx skills add pydantic/skills --skill logfire-setup -a claude-code. The skill lands at ~/.claude/skills/logfire-setup/SKILL.md.

What does logfire-setup do?

It works out which Logfire surface you want configured and hands the job to the matching specialist skill, from app instrumentation to Gateway and coding agents.

How is it different from logfire-instrumentation?

logfire-setup is the entry point for unscoped requests. If you already named one surface, such as "monitor my Postgres server", it loads that specialist straight away.

Source text

SKILL.md · MIT
---
name: logfire-setup
description: Route broad requests to set up Pydantic Logfire across application instrumentation, infrastructure, evals, frontend observability, Gateway, or coding agents. Use for "set up Logfire", "add Logfire to my project", "get me set up properly with Logfire", "send as much data as would be useful", mentions of Logfire without specific scope, or multi-surface requests; use the matching specialist directly for an already scoped request.
---

# Set Up Logfire

Route each explicitly requested surface to its specialist below; leave installation
and verification to those specialists. Do not expand an otherwise scoped request.

Proceed through ordinary, reversible setup with short updates without asking
approval; no clean tree, branch, commits, or plan is required. Pause for browser
auth, unresolved app/project ambiguity, material production cost or telemetry
increases, deploy/infra changes, or destructive/unrelated work, then ask one
concrete question. Report only
verification observed in this session.

## Step 1: Authenticate and Select the Exact Project

If the request is already scoped to frontend observability, application Gateway,
coding-agent setup, querying, or opening the UI, load that specialist from the
table below first. Its credential requirements differ from ordinary SDK
instrumentation; do not create a project write token merely to route the request.

For ordinary SDK instrumentation, confirm the exact project with `whoami`
before inspecting or running application files.

Use [Authenticate and Select the Exact Project](https://pydantic.dev/.well-known/agent-skills/logfire-instrumentation/references/auth.md) to derive the CLI target from the supplied Logfire URL and run its target-aware `whoami` check with a verified CLI path — for JS/TS projects without `uv`, use the external-prefix npm fallback instead of plain `npx`, which can execute a repository-local binary. Skip to Step 2 if that already reports the right project and resolved `--region` or `--base-url` target; otherwise, continue through the full authentication and project-selection sequence there.


## Step 2: Understand the Repo

Read `AGENTS.md`/`CLAUDE.md`/`README.md` and skim the language, runtime, and package manager. Then match what you find against the table below to decide what to fetch next:

| Surface | Covers | Skill |
|---------|--------|-------|
| App instrumentation | Traces, logs, metrics, and AI/agent spans from application code — Python, JavaScript/TypeScript, Rust, or any OpenTelemetry language | [`logfire-instrumentation`](https://pydantic.dev/.well-known/agent-skills/logfire-instrumentation/SKILL.md) |
| Frontend observability | Browser errors, RUM, Web Vitals, and session replays | [`logfire-frontend`](https://pydantic.dev/.well-known/agent-skills/logfire-frontend/SKILL.md) |
| Infrastructure monitoring | Hosts, Docker, Kubernetes, database/queue/cache servers, cloud-provider metrics — no application code | [`logfire-infrastructure`](https://pydantic.dev/.well-known/agent-skills/logfire-infrastructure/SKILL.md) |
| Evals | Set up and run AI/agent evaluations against test-case datasets in Python or Node.js | [`logfire-evals`](https://pydantic.dev/.well-known/agent-skills/logfire-evals/SKILL.md) |
| Querying telemetry | Search traces/logs/spans/metrics, summarize errors, find root cause | [`logfire-query`](https://pydantic.dev/.well-known/agent-skills/logfire-query/SKILL.md) |
| Live UI | Open project pages, the live view, trace links, or the Explore page in a browser | [`logfire-ui`](https://pydantic.dev/.well-known/agent-skills/logfire-ui/SKILL.md) |
| Feature flags | Runtime-managed variables (`logfire.var()`, `logfire.template_var()`) | no dedicated skill yet — see the product's own docs |
| Application Gateway | Route an application's model calls through Pydantic AI Gateway | [`logfire-gateway`](https://pydantic.dev/.well-known/agent-skills/logfire-gateway/SKILL.md) |
| Coding agents | Record Claude/Codex sessions or configure coding-agent Gateway routing | [`logfire-coding-agents`](https://pydantic.dev/.well-known/agent-skills/logfire-coding-agents/SKILL.md) |

- With no specific scope, get one representative application service to verified first data. Incidental Docker/eval files do not expand the task. Route infrastructure-only repositories to `logfire-infrastructure`.
- A request already scoped to one surface ("monitor my Postgres server", "set up evals for this agent") → fetch that skill directly, skipping the rest of this table.
- Genuinely ambiguous between two adjacent surfaces (e.g. "watch my Postgres" could mean Collector-level infrastructure metrics or app-level query instrumentation)? Ask one clarifying question rather than guessing — loading the wrong skill wastes the user's time reading instructions for a job they didn't ask for.

## Step 3: Fetch the Right Skill(s)

Load the selected skill and follow its credential workflow. SDK specialists
verify the project with `whoami`; CLI specialists preserve prepared handoffs,
and query/UI skills can use existing native MCP access. Do not force one
authentication method across all surfaces.

Never print, log, hard-code, commit, or echo a token, in any of these skills, at any point. The one exception — reading `.logfire/logfire_credentials.json`'s `token` key programmatically to hand a non-native-SDK application its write token, never to display it — is in [auth.md](https://pydantic.dev/.well-known/agent-skills/logfire-instrumentation/references/auth.md#if-the-calling-skill-needs-a-write-token-not-just-a-cli-session).

© pydantic · MIT · pydantic/skills

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