Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/raphaelmansuy/edgequake/langfusenpx skills add raphaelmansuy/edgequake --skill langfusegit clone --depth 1 https://github.com/raphaelmansuy/edgequakeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/raphaelmansuy/edgequake/langfuse)<a href="https://agentmods.dev/skills/raphaelmansuy/edgequake/langfuse"><img src="https://agentmods.dev/badge/skills/raphaelmansuy/edgequake/langfuse.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00100 | $0.01881 |
| Opus 5 | $0.00050 | $0.00941 |
| Sonnet 5 | $0.00020 | $0.00376 |
| Haiku 4.5 | $0.00010 | $0.00188 |
Grade A, and why
langfuse scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash(curl *langfuse.com/*) How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Langfuse
This skill helps you use Langfuse effectively across all common workflows: instrumenting applications, migrating prompts, debugging traces, and accessing data programmatically.
Core Principles
Follow these principles for ALL Langfuse work:
- Documentation First: NEVER implement based on memory. Always fetch current docs before writing code (Langfuse updates frequently) See the section below on how to access documentation.
- CLI for Data Access: Use
langfuse-cliwhen querying/modifying Langfuse data. See the section below on how to use the CLI. - Best Practices by Use Case: Check the relevant reference file below for use-case-specific guidelines before implementing
- Use latest Langfuse versions: Unless the user specified otherwise or there's a good reason, always use the latest version of Langfuse SDKs/APIs. Even if you're only creating a plan for another agent to execute, be explicit about the exact version to use.
- If you guide the user through UI and are unsure about a label or location, inspect the user’s screenshots or ask to see the relevant screen. Do not assume UI labels have the exact same names as API, SDK, or CLI fields.
Use case specific references
- instrumenting an existing function/application: references/instrumentation.md
- migrating prompts from a codebase into Langfuse: references/prompt-migration.md
- creating a prompt or changing any part of an existing prompt, including small edits and debugging/tuning: references/prompt-engineering.md
- capturing user feedback (thumbs, ratings, implicit signals) as scores on traces: references/user-feedback.md
- further tips on using the Langfuse CLI: references/cli.md
- upgrading legacy trace-level or dataset-item evaluators to observation-level or experiment evaluators: references/trace-evaluator-upgrade.md. Use the evaluator migration guide as the primary reference.
- preparing an application and Langfuse project for the v4 platform migration: references/v4-project-migration.md
- judge calibration (LLM-as-a-Judge reliability, simple accuracy checks, advanced split-based validation, confusion matrices, and metric ingestion): references/judge-calibration.md
- systematic error analysis — reading traces, building failure taxonomy, deciding what to fix: references/error-analysis.md
- setting up CI/CD experiment gates with
langfuse/experiment-action: references/ci-cd.md - submitting feedback about this skill: references/skill-feedback.md
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 782 B
- references/ci-cd.md 3.0 KB
- references/cli.md 2.1 KB
- references/error-analysis.md 4.7 KB
- references/instrumentation.md 9.9 KB
- references/judge-calibration.md 11 KB
- references/prompt-engineering.md 2.8 KB
- references/prompt-migration.md 5.1 KB
- references/skill-feedback.md 2.2 KB
- references/trace-evaluator-upgrade.md 5.2 KB
- references/user-feedback.md 4.0 KB
- references/v4-project-migration.md 6.7 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 147 lines · 100 tokens per session scan A 58a3d325b475
langfuse is a skill published in the GitHub repository raphaelmansuy/edgequake (2,080 stars, last pushed 3d ago), licensed Apache-2.0. It adds 100 tokens to every session and 1,881 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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