Langfuse Agent Skills
35 skills published by langfuse across 3 repositories. Together they weigh 234 438 tokens — that is what loading all of them at once would cost you in context.
35 skills 234 438 tokens total vendor account
Use when editing worker/src/constants/default-model-prices.json, packages/shared/src/server/llm/types.ts, pricing tiers, tokenizer IDs, or matchPattern regexes for OpenAI, Anthropic, Bedrock, Vertex, Azure, or Gemini model pricing.
| Shared workflow for editing Langfuse's repo-owned agent setup under `.agents/`. Use when changing AGENTS files, shared skills, `.agents/config.json`, generated shim behavior, provider discovery paths, or install-time agent sync.
| Analyze Langfuse Cloud infrastructure cost structure using Metabase cost marts. Use when asked about cloud spend, AWS versus ClickHouse cost splits, cost drivers by provider/service/usage type/account, daily cost per tracing event, infra cost dashboards, or cost regressions visible in Metabase.
Build or review Langfuse backend code. Use for tRPC routers, public REST APIs, BullMQ processors, services, middleware, Prisma or ClickHouse access, OpenTelemetry, Zod, environment configuration, or backend tests.
| Shared workflow for writing Langfuse changelog entries after a feature is complete. Use when a branch is ready for merge and a changelog entry or changelog draft is needed.
MUST USE when reviewing ClickHouse schemas, queries, or configurations. Contains 28 rules that MUST be checked before providing recommendations. Always read relevant rule files and cite specific rules in responses.
Review Langfuse code changes for correctness, regressions, and best practices.
Design, implement, review, or harden Langfuse repo-owned autonomous agents. Use for LLM-powered GitHub Actions, scheduled or dispatched agents, agent-created PRs, prompts, allowlists, tokens, untrusted content, or self-updating instructions.
| Research Langfuse production telemetry with reusable Datadog queries. Use for tenant or project activity, API usage, queue behavior, spans, logs, metrics, or ad hoc measurements across production regions; pair with debug-issue-with-datadog for root-cause analysis.
| Establish root cause by combining Datadog telemetry with the Langfuse repo. Use when investigating or triaging a user report, Linear or GitHub issue, incident, or pasted production error.
| Shared workflow for browser-based review of user-visible frontend changes in Langfuse. Use when a change affects UI behavior, layout, styling, navigation, or browser-visible regressions and should be checked with the Playwright MCP server before signoff.
| Architect large or state-heavy Langfuse frontend features. Use for virtualized lists, large tables, controllers, Zustand stores, row selection, high-frequency state, rendering performance, or before adding useEffect, useMemo, useCallback, or form defaults derived from loaded data.
| Langfuse repo Git, GitHub, commit, branch, pull request, issue search, release, and production-promotion workflow. Use when staging, committing, pushing, opening PRs, searching GitHub issues, or changing release/promotion behavior.
Triage an engineer's work queue across Linear, Pylon, and GitHub. Use for assigned, urgent, waiting, or stale issues, support follow-ups, and PR reviews.
| Read and, after human approval, update the Linear `incident-alert` knowledge base. Use before and after investigating a named Datadog monitor, incident.io alert or incident, or on-call page to find or record root causes.
Tune and review Langfuse autoscaling for web, web-iso, and web-ingestion. Use for Terraform scale settings, RPM targets, scaling bounds, task counts, cost/performance tradeoffs, or Datadog evidence in the infrastructure repo.
Navigate Langfuse repositories, code areas, and agent skills. Use to locate code, choose the right repo or skill, search across the Langfuse organization, or orient before implementation, debugging, documentation, support, or operations.
> Use Langfuse's disposable per-PR previews at pr-N.preview.langfuse.com (synthetic data only). Use for preview access, failed deployments, test-data seeding, kubectl debugging, or waking sleeping previews.
| Use this skill after a bug or regression candidate has measured evidence to document them within linear.
>- direct/transitive bumps, release-age checks, temporary overrides, minimumReleaseAgeExclude, lockfile/dedupe verification.
| Product analytics with posthog. Use when adding a meaningful user action or feature in `web/**`, touching PostHog capture code, or answering product-usage questions.
Use this skill to clean up a React component. Only use this skill when instructed to do so by the user.
Guidelines for writing React components. Use this when creating a new react component.
| Refactor avoidable React useEffect usage in Langfuse frontend code. Use when adding, reviewing, or removing effects; initializing forms or local UI state from query data; synchronizing client and server state; moving mutations or async workflows out of components; cleaning every effect from a frontend submodule; or reviewing whether an effect has a valid external-system owner.
Review Langfuse changes for SSRF, tenant isolation, secret handling, unsafe redirects or uploads, and RBAC drift. Use when a design or change accepts URLs or host fields, handles secrets or cross-tenant data, makes outbound requests, adds an integration, follows redirects, or widens permissions.
| Seed reproducible local Langfuse data in ClickHouse and Postgres. Use for complex traces, long sessions, v3/v4 events, bulk list data, or frontend rendering and performance tests; never use ad hoc scripts or raw inserts.
| Decide whether and how errors report to Sentry. Use when touching capture or error-handling paths in `web/**`, triaging Sentry noise, or changing Sentry settings.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
Use when writing or reviewing Storybook stories (`.stories.tsx`) for React components.
| Configure and troubleshoot Turborepo monorepos. Use for turbo.json, task pipelines, dependsOn, caching, filters, affected packages, CI optimization, environment variables, package boundaries, or shared internal packages.
| Prepare Langfuse weekly production reviews covering failures, fixes, open issues, and tracking gaps. Use for "what broke last week," production bugs, Datadog alerts or error patterns, incident.io activity, or pager load.
Add a new team member to Langfuse's canonical team data and shared team table. Use when onboarding a new employee who needs to appear on the website team, press, about, careers, or handbook pages.
>- Converts draft customer-story Markdown into Langfuse website MDX (Fumadocs), collects missing metadata and assets, wires meta.json and authors. Use when adding or converting a customer case study, user story, or /users page, or when the user mentions customer story setup, cresta/canva-style posts, or CustomerStoryCTA/BlogHeader for customers.
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
Create a new Langfuse integration page in the langfuse-docs repo. Use this skill whenever the user wants to add, create, draft, or scaffold an integration page, cookbook, or docs page for a new tool/framework/model-provider/gateway in Langfuse — triggers include "new integration", "integration page", "docs page for <X>", "cookbook for <X>", "add <X> to langfuse docs", or any request that results in a new `cookbook/integration_*.ipynb`. Also use when the user pastes working integration code, a link to a partner's docs, or rough notes and wants them turned into the standard Langfuse integration notebook. The skill produces a correctly formatted Jupyter notebook, updates `cookbook/_routes.json`, and tries to fetch the partner logo into `public/images/integrations/`.