Agent skill

Langfuse Prod Checklist

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Langfuse production readiness checklist and verification. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedAI & LLM Engineering

Install Langfuse Prod Checklist

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-prod-checklist -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace langfuse-prod-checklist --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/langfuse-prod-checklist .claude/skills/langfuse-prod-checklist && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
langfuse-prod-checklist
GitHub stars
2.8k
Token cost
~2k tokens
SKILL.md length
359 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Langfuse production readiness checklist and verification. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Preparing to deploy Langfuse to production
  • SKILL.md covers Overview, Prerequisites, Production Configuration and Pre-Deployment Verification…, plus 6 more sections
  • Needs LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY
  • Validating production configuration

What it does

Langfuse Prod Checklist is an agent skill from jeremylongshore/tons-of-skills-marketplace. Langfuse production readiness checklist and verification. Use when preparing to deploy Langfuse to production, validating production configuration, or auditing existing setup. Trigger with phrases like "langfuse production", "langfuse prod ready", "deploy langfuse", "langfuse checklist", "langfuse go live".

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/implementation.md`). Compatibility notes: Designed for Claude Code

It sits in AI & LLM Engineering, covering LLM observability. It works with Langfuse. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Preparing to deploy Langfuse to production
  • Validating production configuration
  • Auditing existing setup
  • With phrases like langfuse production

Example prompts

  • “langfuse production”
  • “langfuse prod ready”
  • “deploy langfuse”
  • “/langfuse-prod-checklist”

Requirements

  • A credential in LANGFUSE_PUBLIC_KEY
  • A credential in LANGFUSE_SECRET_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Grep

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(npm:*)
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • langfuse.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LANGFUSE_PUBLIC_KEY
    • LANGFUSE_SECRET_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Langfuse Prod Checklist loads about 2k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 83 tokens; SKILL.md has 359 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 359 words, ~1,980 tokens.

Download SKILL.mdSave it as .claude/skills/langfuse-prod-checklist/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
langfuse-prod-checklist
description
Langfuse production readiness checklist and verification. Use when preparing to deploy Langfuse to production, validating production configuration, or auditing existing setup. Trigger with phrases like "langfuse production", "langfuse prod ready", "deploy langfuse", "langfuse checklist", "langfuse go live".
allowed-tools
Read, Write, Edit, Bash(npm:*), Grep
compatibility
Designed for Claude Code
version
1.17.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, langfuse, deployment, audit

Langfuse Production Checklist

Overview

Comprehensive checklist for deploying Langfuse observability to production with verified configuration, error handling, graceful shutdown, monitoring, and a pre-deployment verification script.

Prerequisites

  • Development and staging testing completed
  • Production Langfuse project created with separate API keys
  • Secret management solution in place

Production Configuration

typescript
// v4+ Production Config
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

const processor = new LangfuseSpanProcessor({
  exportIntervalMillis: 5000,  // Flush every 5s
  maxExportBatchSize: 50,      // Batch size
  maxQueueSize: 2048,          // Buffer limit
});

const sdk = new NodeSDK({ spanProcessors: [processor] });
sdk.start();

// Graceful shutdown on all signals
for (const signal of ["SIGTERM", "SIGINT", "SIGUSR2"]) {
  process.on(signal, async () => {
    await sdk.shutdown();
    process.exit(0);
  });
}
typescript
// v3 Legacy Production Config
import { Langfuse } from "langfuse";

const langfuse = new Langfuse({
  flushAt: 25,            // Balance between latency and efficiency
  flushInterval: 5000,    // 5 second flush interval
  requestTimeout: 15000,  // 15s timeout
  enabled: true,          // Explicitly enable
});

process.on("beforeExit", () => langfuse.shutdownAsync());
process.on("SIGTERM", () => langfuse.shutdownAsync().then(() => process.exit(0)));
Production Error Handling
typescript
import { observe, updateActiveObservation, startActiveObservation } from "@langfuse/tracing";

// Wrap all traced operations with error safety
const tracedEndpoint = observe({ name: "api-endpoint" }, async (req: Request) => {
  try {
    updateActiveObservation({
      input: { path: req.url, method: req.method },
      metadata: { userId: req.userId },
    });

    const result = await processRequest(req);

    updateActiveObservation({ output: { status: 200 } });
    return result;
  } catch (error) {
    // Log error to trace -- don't let tracing error mask app error
    try {
      updateActiveObservation({
        output: { error: String(error) },
        metadata: { level: "ERROR" },
      });
    } catch {
      // Tracing failure must never break the app
    }
    throw error;
  }
});

Pre-Deployment Verification Script

typescript
// scripts/verify-langfuse-prod.ts
import { LangfuseClient } from "@langfuse/client";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";

async function verify() {
  const checks: Array<{ name: string; pass: boolean; detail: string }> = [];

  // 1. Environment variables
  const requiredVars = ["LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY"];
  for (const v of requiredVars) {
    checks.push({
      name: `Env: ${v}`,
      pass: !!process.env[v],
      detail: process.env[v] ? `SET (${process.env[v]!.slice(0, 10)}...)` : "MISSING",
    });
  }

  // 2. Key validation
  const pk = process.env.LANGFUSE_PUBLIC_KEY || "";
  const sk = process.env.LANGFUSE_SECRET_KEY || "";
  checks.push({
    name: "Key format",
    pass: pk.startsWith("pk-lf-") && sk.startsWith("sk-lf-"),
    detail: `Public: ${pk.startsWith("pk-lf-")}, Secret: ${sk.startsWith("sk-lf-")}`,
  });

  // 3. API connectivity
  try {
    const langfuse = new LangfuseClient();
    // Try fetching prompts as a connectivity test
    await langfuse.prompt.get("__health-check__").catch(() => {});
    checks.push({ name: "API connectivity", pass: true, detail: "Connected" });
  } catch (error) {
    checks.push({ name: "API connectivity", pass: false, detail: String(error) });
  }

  // 4. Trace creation
  try {
    await startActiveObservation("prod-verify", async () => {
      updateActiveObservation({
        input: { test: true },
        output: { verified: true },
        metadata: { verification: "pre-deploy" },
      });
    });
    checks.push({ name: "Trace creation", pass: true, detail: "Trace created" });
  } catch (error) {
    checks.push({ name: "Trace creation", pass: false, detail: String(error) });
  }

  // Report
  console.log("\n=== Langfuse Production Verification ===\n");
  let allPassed = true;
  for (const check of checks) {
    const icon = check.pass ? "PASS" : "FAIL";
    console.log(`  [${icon}] ${check.name}: ${check.detail}`);
    if (!check.pass) allPassed = false;
  }

  console.log(`\n${allPassed ? "All checks passed." : "SOME CHECKS FAILED."}\n`);
  if (!allPassed) process.exit(1);
}

verify();

Production Checklist

Authentication & Security
  • Production API keys created (separate from dev/staging)
  • Keys stored in secret manager (not env files or code)
  • Key prefix validated at startup (pk-lf- / sk-lf-)
  • PII scrubbing enabled on trace inputs/outputs
  • Secret scanning in CI/CD pipeline
SDK Configuration
  • Singleton client pattern (no per-request instantiation)
  • Batch size tuned (flushAt: 25-50)
  • Flush interval set (flushInterval: 5000)
  • Request timeout configured (requestTimeout: 15000)
Reliability
  • Graceful shutdown on SIGTERM/SIGINT
  • All spans end in try/finally (v3) or use observe/startActiveObservation (v4+)
  • Tracing errors caught -- never crash the app
  • Circuit breaker for sustained failures
Monitoring
  • Trace creation success/failure logged
  • Flush latency tracked
  • Rate limit errors monitored
  • Dashboard alerts for quality score regression
Operations
  • Runbook documented for Langfuse outages
  • Fallback behavior defined (app works without Langfuse)
  • Data retention policy configured
  • Log rotation includes redaction of API keys

Instructions

Run the checklist against one named environment and attach the verification script output to the deployment record. Resolve every failed required item or record an explicit time-bounded exception with an owner and rollback plan. Run the check again after changing SDK configuration, secrets, or deployment topology.

Show full SKILL.md (126 more words)Show less

Output

Produce a production-readiness receipt with environment, deployment revision, check timestamp, pass/fail results, exception owners, and one trace-health observation. Reference secret names only; never print API keys or trace payloads.

Examples

Before a production release, run the verifier with production secret references available, confirm graceful shutdown in a disposable rollout, and submit one synthetic trace. If the trace is delayed, record telemetry as degraded while keeping the application's health result separate.

Error Handling

IssueCauseSolution
Missing traces in prodNo flush on exitAdd shutdown handler for SIGTERM
Memory growthClient created per requestUse singleton pattern
High latencySmall batchesIncrease flushAt to 25-50
Lost traces on deployNo graceful shutdownAdd SIGTERM handler with sdk.shutdown()

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/langfuse-prod-checklist of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Langfuse Prod Checklist next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Langfuse Prod Checklist compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Langfuse Prod Checklist this skilljeremylongshore/tons-of-skills-marketplace2.8k—~2kAutomated safety check: PassMIT
Langfuselangfuse/skills301—~2.1kAutomated safety check: NotesMIT
AI Observabilityomer-metin/skills-for-antigravity163—~578Automated safety check: PassApache-2.0
Agent Setup Maintenancelangfuse/langfuse36k—~799Automated safety check: PassCustom licence
Langfuse Integration Pagelangfuse/langfuse-docs246—~3.7kAutomated safety check: PassMIT
Infra Scalinglangfuse/langfuse36k—~4.4kAutomated safety check: PassCustom licence

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Works with

Questions about Langfuse Prod Checklist

What does Langfuse Prod Checklist do?

Langfuse production readiness checklist and verification. An agent skill from jeremylongshore/tons-of-skills-marketplace. Langfuse Prod Checklist is an agent skill from jeremylongshore/tons-of-skills-marketplace. Langfuse production readiness checklist and verification.

When should I use Langfuse Prod Checklist?

Langfuse Prod Checklist fits situations like: preparing to deploy Langfuse to production; validating production configuration; auditing existing setup; with phrases like langfuse production.

How do I install Langfuse Prod Checklist in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-prod-checklist -a claude-code`. Or copy the skill folder (skills/.curated/langfuse-prod-checklist in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/langfuse-prod-checklist in your project. Claude Code loads it when a task matches its description.

How do I install Langfuse Prod Checklist in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-prod-checklist -a codex`. Or copy the skill folder (skills/.curated/langfuse-prod-checklist in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/langfuse-prod-checklist in your project. Codex loads it when a task matches its description.

Can I use Langfuse Prod Checklist in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-prod-checklist -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langfuse-prod-checklist, .gemini/skills/langfuse-prod-checklist, .github/skills/langfuse-prod-checklist and .opencode/skills/langfuse-prod-checklist in your project.

What does Langfuse Prod Checklist need to run?

Going by SKILL.md and its folder, Langfuse Prod Checklist needs credentials named LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY. Our summary lists: A credential in LANGFUSE_PUBLIC_KEY; A credential in LANGFUSE_SECRET_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*), Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Langfuse Prod Checklist access the network?

SKILL.md names 1 domain. As links in the text: langfuse.com. This is read from the text; nothing was executed.

Is Langfuse Prod Checklist safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Langfuse Prod Checklist use?

Langfuse Prod Checklist is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langfuse Prod Checklist use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Langfuse Prod Checklist?

Skills that share tags, products or a category with Langfuse Prod Checklist: Langfuse (langfuse/skills, 301 stars), AI Observability (omer-metin/skills-for-antigravity, 163 stars), Agent Setup Maintenance (langfuse/langfuse, 36k stars) and Langfuse Integration Page (langfuse/langfuse-docs, 246 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langfuse Prod Checklist?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.