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/langfuse/langfuse-docs/langfuse-integration-pagenpx skills add langfuse/langfuse-docs --skill langfuse-integration-pagegit clone --depth 1 https://github.com/langfuse/langfuse-docsWrote 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/langfuse/langfuse-docs/langfuse-integration-page)<a href="https://agentmods.dev/skills/langfuse/langfuse-docs/langfuse-integration-page"><img src="https://agentmods.dev/badge/skills/langfuse/langfuse-docs/langfuse-integration-page.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 5.1 | $0.00186 | $0.03738 |
| Opus 5 | $0.00093 | $0.01869 |
| Sonnet 5 | $0.00037 | $0.00748 |
| Haiku 4.5 | $0.00019 | $0.00374 |
Grade A, and why
langfuse-integration-page 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 6d 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.
Use `curl -sSfL -o <dest> <url>` in bash. Check the result is non-empty and looks like a valid image before using it — if `curl` returns an HTML error page saved as `.svg`, that's worse than a missing file. How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Langfuse integration page creator
This skill scaffolds a new integration page for the langfuse-docs site. Integration pages live as Jupyter notebooks in cookbook/integration_<slug>.ipynb and are converted to MDX by scripts/update_cookbook_docs.sh using the mapping in cookbook/_routes.json. Getting the notebook metadata block, the STEPS_START/STEPS_END wrapper, and the routes entry right is the whole job — once those are correct, the build does the rest.
What to produce
Three things, always, in the user's langfuse-docs checkout:
- A new notebook at
cookbook/integration_<slug>.ipynbthat matches the house template (see "Notebook structure" below). - A new entry appended to
cookbook/_routes.jsonpointing at the notebook and the targetdocsPath. - A best-effort logo download into
public/images/integrations/<slug>_icon.<ext>. If fetching fails, leave a TODO for the user.
Do not run scripts/update_cookbook_docs.sh yourself — that regenerates many files and is slow (~10 min build). The user runs it when they're ready.
Step 1 — Gather what you need, up front
Before writing anything, collect the following. Ask the user for what's missing using a single AskUserQuestion batch where possible. Some answers are mutually exclusive (pick-one); some can be inferred from context.
Always ask (these determine the template and the routes entry):
- Integration name — the human-readable name (e.g., "Pydantic AI", "Fireworks AI", "Temporal"). Used in the title and intro.
- Slug — kebab-case, used in the filename, logo path, and
docsPath. Default to the name lowercased with spaces → hyphens, but confirm. Example: "Pydantic AI" →pydantic-ai; "Fireworks AI" →fireworks-ai. - Category — one of:
model-providers,frameworks,gateways,other. This is the<category>segment indocsPath: "integrations/<category>/<slug>". Guidance:model-providers: inference APIs (OpenAI-compatible or otherwise) — Anthropic, Cohere, Fireworks, Groq, Bedrock, Vertex, Gemini, etc.frameworks: agent/app frameworks — LangChain, CrewAI, Pydantic AI, Google ADK, Temporal, Semantic Kernel, etc.gateways: LLM proxies/routers — Portkey, LiteLLM proxy, TrueFoundry, OpenRouter, Kong AI, etc.other: anything else — scraping (Firecrawl, Exa), UIs (Gradio, LibreChat), dev tools, etc.
- Language —
python(default) orjs. JS integrations use the filename prefixjs_integration_<slug>.ipynband commonly get a-jssuffix in the slug when both exist (e.g.,anthropic-js,claude-agent-sdk-js). - Instrumentation pattern — pick one (this determines the template body). See
references/patterns.mdfor full details and match it to the integration:openinference— OpenInference instrumentor library (e.g.,openinference-instrumentation-google-adk). Most common for agent frameworks.openai-drop-in— The partner is OpenAI-compatible; usefrom langfuse.openai import openai. Common for inference providers (Fireworks, Groq, DeepSeek, etc.).framework-native— Framework has built-in instrumentation hook (e.g.,Agent.instrument_all()for Pydantic AI).otel-direct— Partner emits OTel natively; configure an OTLP exporter pointing at Langfuse. Less common; used for things like Temporal, some MLflow setups.
What ships with it
6 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.
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.
- 6d ago First seen · 239 lines · 186 tokens per session scan A abe92fc0a754
langfuse-integration-page is a skill published in the GitHub repository langfuse/langfuse-docs (240 stars, last pushed today), licensed MIT. It adds 186 tokens to every session and 3,738 once invoked, about $0.0009 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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