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Agent skill

Langfuse

by HybridAIOne in HybridAIOne/hybridclaw

Use Langfuse for LLM observability and evaluation and look up Langfuse documentation.

MITAuto-check passedAI & LLM Engineering

Install Langfuse

skills CLI
$ npx skills add HybridAIOne/hybridclaw --skill langfuse -a claude-code

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

GitHub CLI
$ gh skill install HybridAIOne/hybridclaw langfuse --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/HybridAIOne/hybridclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/langfuse .claude/skills/langfuse && 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
GitHub stars
159
Token cost
~3.1k tokens
SKILL.md length
858 words
Files
14 (incl. references)
Skills in repo
72
Repo updated
First seen
Licence
MIT

At a glance

Use Langfuse for LLM observability and evaluation and look up Langfuse documentation.

  • Works in 2 steps: Langfuse data access (HybridClaw gateway… → Langfuse documentation
  • LLM observability and evaluation and look up Langfuse documentation
  • SKILL.md covers HybridClaw setup, Core Principles, Use-case references and 1. Langfuse data access…, plus 5 more sections
  • Runs JavaScript scripts from its folder; calls node, curl and python3; reaches langfuse.com and cloud.langfuse.com; needs LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY

What it does

Langfuse is an agent skill from HybridAIOne/hybridclaw. Use Langfuse for LLM observability and evaluation and look up Langfuse documentation. In HybridClaw, data access (traces, observations, sessions, scores, prompts, datasets, metrics) goes through the gateway-proxied langfuse.cjs helper with SecretRef auth — reads are green, writes are grant-gated. Documentation retrieval uses langfuse.com llms.txt, markdown pages, and search-docs. Covers instrumentation, prompt migration, error analysis, and LLM-as-a-judge calibration.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including reference files (for example `NOTICE.md`, `evals/scenarios.json` and `references/ci-cd.md`).

It sits in AI & LLM Engineering, covering LLM observability. It works with Langfuse. The repository describes itself as: Enterprise-ready self-hosted AI assistant runtime with sandboxed execution, secure credentials, approvals, and memory. The licence is MIT.

When your agent uses it

  • LLM observability and evaluation and look up Langfuse documentation
  • Tasks that involve LLM observability

Example prompts

  • “/langfuse”

Requirements

  • Node.js
  • A credential in LANGFUSE_PUBLIC_KEY
  • A credential in LANGFUSE_SECRET_KEY

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Langfuse data access (HybridClaw gateway helper)
  2. Langfuse documentation

What it can do on your machine

Read from SKILL.md and the folder at commit 8162701. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • node
    • curl
    • python3
    • npx

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • langfuse.com
    • cloud.langfuse.com
    • us.cloud.langfuse.com
    • jp.cloud.langfuse.com

    Also links to:

    • github.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.

Context cost

Langfuse loads about 3.1k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 120 tokens; SKILL.md has 858 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~120
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~17k

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 HybridAIOne/hybridclaw at commit 8162701, republished under its MIT licence (© HybridAIOne). 858 words, ~3,077 tokens.

Download SKILL.mdSave it as .claude/skills/langfuse/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
langfuse
description
Use Langfuse for LLM observability and evaluation and look up Langfuse documentation. In HybridClaw, data access (traces, observations, sessions, scores, prompts, datasets, metrics) goes through the gateway-proxied langfuse.cjs helper with SecretRef auth — reads are green, writes are grant-gated. Documentation retrieval uses langfuse.com llms.txt, markdown pages, and search-docs. Covers instrumentation, prompt migration, error analysis, and LLM-as-a-judge calibration.
user-invocable
true
requires.bins
node

Langfuse

This skill helps you use Langfuse effectively across all common workflows: instrumenting applications, migrating prompts, debugging traces, accessing data, and evaluating outputs.

HybridClaw integration. This is the official Langfuse skill (github.com/langfuse/skills, MIT) adapted for HybridClaw. Two things differ from the upstream skill:

  1. Credentials never leave the gateway. Do not export LANGFUSE_PUBLIC_KEY / LANGFUSE_SECRET_KEY, run npx langfuse-cli, or paste keys anywhere. Store them once in the runtime stores (below); the gateway injects them server-side.
  2. Data access goes through langfuse.cjs, not the Langfuse CLI. The helper builds each REST request and sends it through the HybridClaw gateway, which resolves Authorization: Basic <secret:LANGFUSE_BASIC_AUTH> and the <env:LANGFUSE_HOST> base URL. Wherever a reference says to run langfuse-cli or curl -H "Authorization: Basic $AUTH", use the helper instead. Documentation retrieval (section 2) is unchanged.

HybridClaw setup

The helper never sees credentials. Store two values once. For secrets, use this order:

  1. Browser admin: open the active HybridClaw admin URL ending in /admin/secrets.

  2. Browser /chat or TUI fallback.

  3. Local console fallback.

  4. LANGFUSE_BASIC_AUTH — base64 of public-key:secret-key: /secret set LANGFUSE_BASIC_AUTH "<base64-public-colon-secret>"

  5. LANGFUSE_HOST — your Langfuse base URL: /env set LANGFUSE_HOST https://cloud.langfuse.com (use https://us.cloud.langfuse.com for US, https://jp.cloud.langfuse.com for JP, or your self-hosted origin).

Local console fallback: hybridclaw secret set LANGFUSE_BASIC_AUTH "<...>" and hybridclaw env set LANGFUSE_HOST https://cloud.langfuse.com.

See references/operator-setup.md for key scope, host selection, autonomy defaults, and network-policy notes.

Core Principles

Follow these principles for ALL Langfuse work:

  1. Documentation first: never implement from memory. Langfuse updates frequently — fetch current docs (section 2) before writing instrumentation or SDK code.
  2. Helper for data access: use langfuse.cjs (gateway + SecretRef) when querying or modifying Langfuse data. It owns endpoints, methods, bodies, stakes tiers, host, and the Basic auth placeholder.
  3. Best practices by use case: check the relevant reference below before implementing.
  4. Use latest Langfuse versions: unless the user says otherwise, target the latest Langfuse SDKs/APIs.

Use-case references

1. Langfuse data access (HybridClaw gateway helper)

langfuse.cjs is the API wrapper. Do not handcraft Langfuse API URLs, JSON bodies, tiers, host, or the Basic auth header from memory.

bash
node skills/langfuse/langfuse.cjs --help
  • plan classifies a natural-language request into an operation + tier: node skills/langfuse/langfuse.cjs --format json plan "average eval score this week"
  • run executes a live request through the gateway (the gateway injects the Basic auth header): node skills/langfuse/langfuse.cjs --format json run list-traces --user-id alice --limit 50
  • http-request emits the gateway-ready payload without calling Langfuse — use it for dry-run inspection or runtimes without helper gateway access.

Read examples (green):

bash
node skills/langfuse/langfuse.cjs --format json run get-trace --trace-id abc123
node skills/langfuse/langfuse.cjs --format json run list-observations --type GENERATION --trace-id abc123
node skills/langfuse/langfuse.cjs --format json run list-scores --name quality
node skills/langfuse/langfuse.cjs --format json run get-prompt --prompt-name support-reply --label production
node skills/langfuse/langfuse.cjs --format json run metrics --query '{"view":"traces","metrics":[{"measure":"count","aggregation":"count"}]}'

Guarded write examples (amber — only after an explicit operator grant):

bash
node skills/langfuse/langfuse.cjs --format json run create-score \
  --trace-id abc123 --name quality --value 0.8 --data-type NUMERIC --comment "reviewed" --operator-grant
node skills/langfuse/langfuse.cjs --format json run create-prompt \
  --name summarizer --type text --prompt "Summarize: {{input}}" --label production --operator-grant

Select region or self-hosted host explicitly (otherwise <env:LANGFUSE_HOST>):

bash
node skills/langfuse/langfuse.cjs --format json run list-traces --host https://us.cloud.langfuse.com
Show full SKILL.md (350 more words)Show less
Working rules
  • Reads are green. Writes (create-score, create-comment, create-dataset, create-dataset-item, create-prompt) require --operator-grant: produce a plan, wait for the operator's grant, then run the exact approved command.
  • Deletions and project / API-key / organization / SCIM administration are out of scope. Use the Langfuse UI for those.
  • Page size is capped at 100; use --page (legacy) or --cursor (modern endpoints) to paginate. The helper rejects --limit above 100.
  • Trace reads use Langfuse's v2 Observations API. list-traces returns logical root observation rows (one application root per trace); get-trace returns every observation row sharing the requested trace ID. Follow meta.cursor with --cursor when more rows are available.
  • Langfuse v4 has no separate trace-level input/output. Reconstruct them from the root observation; prompts and outputs may instead live on a child GENERATION observation.
  • Before creating a score config, list existing ones (list-score-configs); configs cannot be deleted.
  • Never print, inspect, or ask for LANGFUSE_BASIC_AUTH; the gateway injects it as Authorization: Basic <secret:LANGFUSE_BASIC_AUTH>.
  • Cost per assistant run is recorded by HybridClaw UsageTotals; helper output includes costMeasurement.system = "UsageTotals" for eval verification.

2. Langfuse documentation

Prefer your application's native web fetch/search tools (e.g. web_fetch, web_search) over curl. The URLs work with any fetching method.

2a. Documentation index (llms.txt)

Fetch the full index of doc pages, then fetch the right one:

bash
curl -s https://langfuse.com/llms.txt
2b. Fetch individual pages as markdown

Append .md to any doc path (or send Accept: text/markdown):

bash
curl -s "https://langfuse.com/docs/observability/overview.md"
2c. Search documentation

When you don't know the page (also indexes GitHub issues/discussions):

bash
curl -s "https://langfuse.com/api/search-docs?query=How+do+I+trace+LangGraph+agents"

Workflow: start with llms.txt to orient → fetch the specific page → fall back to search when the topic is unclear.

Eval suite

bash
node skills/langfuse/langfuse.cjs --format json eval-scenarios

The fixture at evals/scenarios.json contains 10 scenarios covering trace, observation, session, score, metric, prompt, and dataset reads plus guarded score, dataset, and prompt writes.

Skill feedback

If the skill gives wrong or outdated guidance, is missing something, or could be improved, offer to submit feedback to the Langfuse skill maintainers following references/skill-feedback.md. Do not trigger this for issues with Langfuse the product — only this skill's instructions.

Attribution

Adapted from the official Langfuse skill (github.com/langfuse/skills), MIT-licensed, with HybridClaw gateway/SecretRef data access in place of the upstream langfuse-cli + plaintext-key path. See NOTICE.md.

Validation

bash
python3 skills/skill-creator/scripts/quick_validate.py skills/langfuse
node skills/langfuse/langfuse.cjs --help
node skills/langfuse/langfuse.cjs --format json eval-scenarios

© HybridAIOne, 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 13 other files (references) in skills/langfuse of HybridAIOne/hybridclaw.

  • SKILL.md
  • NOTICE.md
  • evals/scenarios.json
  • langfuse.cjs
  • references/ci-cd.md
  • references/cli.md
  • references/error-analysis.md
  • references/instrumentation.md
  • references/judge-calibration.md
  • references/operator-setup.md
  • references/prompt-migration.md
  • references/sdk-upgrade.md
  • references/skill-feedback.md
  • references/user-feedback.md

Open the folder on GitHubat commit 8162701

Compare with similar skills

Langfuse 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 compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Langfuse this skillHybridAIOne/hybridclaw159—~3.1kAutomated safety check: PassMIT
Langfuse Codebase Navigatorlangfuse/langfuse36k—~1.4kAutomated safety check: PassCustom licence
Langfuse Integration Pagelangfuse/langfuse-docs246—~3.7kAutomated safety check: PassMIT
Langfuselangfuse/skills301—~2.1kAutomated safety check: NotesMIT
Add Yourself To Team Langfuselangfuse/langfuse-docs246—~548Automated safety check: PassMIT
Weekly Production Reviewlangfuse/langfuse36k—~4.1kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Langfuse

What does Langfuse do?

Use Langfuse for LLM observability and evaluation and look up Langfuse documentation. Langfuse is an agent skill from HybridAIOne/hybridclaw. Use Langfuse for LLM observability and evaluation and look up Langfuse documentation.

When should I use Langfuse?

Langfuse fits situations like: LLM observability and evaluation and look up Langfuse documentation; tasks that involve LLM observability.

How do I install Langfuse in Claude Code?

Run `npx skills add HybridAIOne/hybridclaw --skill langfuse -a claude-code`. Or copy the skill folder (skills/langfuse in HybridAIOne/hybridclaw) into .claude/skills/langfuse in your project. Claude Code loads it when a task matches its description.

How do I install Langfuse in Codex?

Run `npx skills add HybridAIOne/hybridclaw --skill langfuse -a codex`. Or copy the skill folder (skills/langfuse in HybridAIOne/hybridclaw) into .agents/skills/langfuse in your project. Codex loads it when a task matches its description.

Can I use Langfuse 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 HybridAIOne/hybridclaw --skill langfuse -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, .gemini/skills/langfuse, .github/skills/langfuse and .opencode/skills/langfuse in your project.

What does Langfuse need to run?

Going by SKILL.md and its folder, Langfuse needs JavaScript for the scripts in its folder, the command-line tools its instructions call (node, curl, python3 and npx) and credentials named LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY. Our summary lists: Node.js; A credential in LANGFUSE_PUBLIC_KEY; A credential in LANGFUSE_SECRET_KEY.

Does Langfuse access the network?

SKILL.md names 5 domains. In commands or code: langfuse.com, cloud.langfuse.com, us.cloud.langfuse.com and jp.cloud.langfuse.com; the agent is likely to contact these when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Langfuse 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 use?

Langfuse is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langfuse use?

About 3.1k tokens (SKILL.md is roughly 12k 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 14k tokens, read only when the agent opens those files.

What are the alternatives to Langfuse?

Skills that share tags, products or a category with Langfuse: Langfuse Codebase Navigator (langfuse/langfuse, 36k stars), Langfuse Integration Page (langfuse/langfuse-docs, 246 stars), Langfuse (langfuse/skills, 301 stars) and Add Yourself To Team Langfuse (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?

HybridAIOne (a GitHub organization) maintains it in HybridAIOne/hybridclaw, which has 159 GitHub stars. The repository holds 72 skills in this directory. The repository was last updated on October 9, 2026.

Source: HybridAIOne/hybridclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.