pydantic/logfire/logfire-setup
- community
Checking that the SKILL.md file is still in the repository.
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.
Install
npx vskill@latest install pydantic/logfire/logfire-setupSKILL.md
The same SKILL.md was published earlier as pydantic/skills/logfire-setup.
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:
| 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` | |
| Frontend observability | Browser errors, RUM, Web Vitals, and session replays | `logfire-frontend` | |
| Infrastructure monitoring | Hosts, Docker, Kubernetes, database/queue/cache servers, cloud-provider metrics — no application code | `logfire-infrastructure` | |
| Evals | Set up and run AI/agent evaluations against test-case datasets in Python or Node.js | `logfire-evals` | |
| Querying telemetry | Search traces/logs/spans/metrics, summarize errors, find root cause | `logfire-query` | |
| Live UI | Open project pages, the live view, trace links, or the Explore page in a browser | `logfire-ui` | |
| 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` | |
| Coding agents | Record Claude/Codex sessions or configure coding-agent Gateway routing | `logfire-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.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckinglogfire-sdk/logfire/.agents/skills/logfire-setup/SKILL.md
Popularity
- Repository stars
- 4,512
- Repository forks
- 315
- 7-day trend score
- 0
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
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