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Logfire Setup

Organization
pydantic
logfire-setup

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.

Overview

Publisherpydantic
Repositoryskills
Skill namelogfire-setup
Stars
147
Forks
7
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by pydantic on GitHub. Read the source before you install it.

Installation

Install the Logfire Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pydantic/skills/tree/main/plugins/logfire/skills/logfire-setup
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pydantic/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/logfire/skills/logfire-setup .claude/skills/logfire-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logfire Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Logfire Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Logfire Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 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:

SurfaceCoversSkill
App instrumentationTraces, logs, metrics, and AI/agent spans from application code — Python, JavaScript/TypeScript, Rust, or any OpenTelemetry languagelogfire-instrumentation
Frontend observabilityBrowser errors, RUM, Web Vitals, and session replayslogfire-frontend
Infrastructure monitoringHosts, Docker, Kubernetes, database/queue/cache servers, cloud-provider metrics — no application codelogfire-infrastructure
EvalsSet up and run AI/agent evaluations against test-case datasets in Python or Node.jslogfire-evals
Querying telemetrySearch traces/logs/spans/metrics, summarize errors, find root causelogfire-query
Live UIOpen project pages, the live view, trace links, or the Explore page in a browserlogfire-ui
Feature flagsRuntime-managed variables (logfire.var(), logfire.template_var())no dedicated skill yet — see the product's own docs
Application GatewayRoute an application's model calls through Pydantic AI Gatewaylogfire-gateway
Coding agentsRecord Claude/Codex sessions or configure coding-agent Gateway routinglogfire-coding-agents
  • 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.

Frequently asked questions

What does the Logfire Setup AI skill do?

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.

Why use Logfire Setup on TypingMind?

Because you install it once and use it with any model. Logfire Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Logfire Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pydantic/skills/tree/main/plugins/logfire/skills/logfire-setup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Logfire Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Logfire Setup?

As many as you like. As long as a model supports skills, you can use Logfire Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Logfire Setup AI skill free?

Yes. It is published on GitHub by pydantic under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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