Borrowing it
Nothing to install: this file belongs to arunanksharan/claude-forge. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/arunanksharan/claude-forge/main/.claude/skills/wire-langfuse/SKILL.mdgit clone --depth 1 https://github.com/arunanksharan/claude-forgeWrote 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/arunanksharan/claude-forge/wire-langfuse)<a href="https://agentmods.dev/skills/arunanksharan/claude-forge/wire-langfuse"><img src="https://agentmods.dev/badge/skills/arunanksharan/claude-forge/wire-langfuse/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arunanksharan/claude-forge/wire-langfuse"><img src="https://agentmods.dev/badge/skills/arunanksharan/claude-forge/wire-langfuse.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00087 | $0.00630 |
| Opus 5.5 | $0.00035 | $0.00252 |
| Sonnet 5.5 | $0.00017 | $0.00126 |
| Haiku 4.5 | $0.00009 | $0.00063 |
Grade A, and why
wire-langfuse scanned grade A with 0 findings 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 2mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Wire Up Langfuse for LLM Observability (claudeforge)
Follow observability/04-langfuse.md. Steps:
- Confirm with user:
- Stack: Python or Node/TS?
- Hosting: Langfuse Cloud (default) or self-hosted?
- Existing LLM framework: raw OpenAI / Anthropic SDK, LangChain, LlamaIndex, or Vercel AI SDK?
- Have keys (
LANGFUSE_PUBLIC_KEY,LANGFUSE_SECRET_KEY,LANGFUSE_HOST)?
- Install + configure the SDK:
- Python:
uv add langfuse+ createlangfuse_client.pywith the Langfuse instance - Node:
pnpm add langfuse+ createlangfuse.tsexporting the client
- Python:
- Instrument LLM calls:
- Use
@observe()decorator (Python) or manualtrace.generation()(Python/TS) for each LLM call - Pass
model,input,output,usage(input/output tokens) - Add
user_id,session_id,metadata(prompt version, feature flag) for filtering
- Use
- Set up integrations if relevant:
- LangChain:
CallbackHandlerfrom langfuse — pass tochain.invoke({}, config={'callbacks': [handler]}) - LlamaIndex: similar callback
- Vercel AI SDK:
LangfuseExportervia@vercel/otel
- LangChain:
- Add scoring: capture user feedback (thumbs up/down) and send via
langfuse.score(...). Inline scores for hallucination/quality if you have heuristics. - Set up prompt management (optional but high-leverage): move static prompts into Langfuse, fetch via
langfuse.get_prompt(name, label='production', cache_ttl_seconds=300). Lets non-engineers iterate. - Build a baseline eval dataset: create dataset from production traces (
langfuse.create_dataset_item(...)); use to compare prompt versions. - Configure flushing: call
langfuse.flush()in shutdown handler / FastAPI lifespan /process.on('SIGTERM'). - Scrub PII in inputs before passing to Langfuse if needed. Set sample rate for high-traffic apps.
Verify in the Langfuse UI: traces, completions, costs visible. Set up alerts on cost / failure rate if available.
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.
- 2mo ago First seen · 33 lines · 87 tokens per session scan A f7cde51a6f82
wire-langfuse is a skill published in the GitHub repository arunanksharan/claude-forge (2 stars, last pushed 6mo ago), licensed Unlicense. It adds 87 tokens to every session and 630 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
wire-langfuse compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| wire-langfusearunanksharan/claude-forge | Skill | 2 | 6mo ago | Unlicense | A | 87 | Claude Code |
| agent-platform-model-registrygoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 60 | Claude Code, Codex |
| foundry-config-setupmicrosoft/agent-framework | Skill | 15k | yesterday | MIT | A | 65 | Claude Code, Codex |
| google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 132 | Claude Code, Codex |
| training-checkwanshuiyin/Auto-claude-code-research-in-sleep | Skill | 19k | 3d ago | MIT | A | 35 | Claude Code |
| nemo-automodel-launcher-configNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 30 | Claude Code |
| finetuningmicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 132 | Claude Code |