langfuse
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Use when debugging AI pipelines, investigating errors, analyzing latency, managing prompt versions, or setting up Langfuse. Triggers on "langfuse", "traces", "debug AI", "find exceptions", "what went wrong", "why is it slow", "datasets", "evaluation sets".
npx skills add majiayu000/claude-skill-registry --skill langfuse-avivsinai-langfuse-mcp-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Langfuse Skill Debug your AI systems through Langfuse observability. **Triggers:** langfuse, traces, debug AI, find exceptions, set up langfuse, what went wrong, why is it slow, datasets, evaluation sets ## Setup **Step 1:** Get credentials from https://cloud.langfuse.com → Settings → API Keys If self-hosted, use your instance URL for `LANGFUSE_HOST` and create keys there. **Step 2:** Install MCP (pick one): ```bash # Claude Code (project-scoped, shared via .mcp.json) claude mcp add \ --scope project \ --env LANGFUSE_PUBLIC_KEY=pk-... \ --env LANGFUSE_SECRET_KEY=sk-... \ --env LANGFUSE_HOST=https://cloud.langfuse.com \ langfuse -- uvx --python 3.11 langfuse-mcp # Codex CLI (user-scoped, stored in ~/.codex/config.toml) codex mcp add langfuse \ --env LANGFUSE_PUBLIC_KEY=pk-... \ --env LANGFUSE_SECRET_KEY=sk-... \ --env LANGFUSE_HOST=https://cloud.langfuse.com \ -- uvx --python 3.11 langfuse-mcp ``` **Step 3:** Restart CLI, verify with `/mcp` (Claude) or `codex mcp list` (Codex) **Step 4:** Test: `fetch_traces(age=60)` ### Read-Only Mode For safer observability without risk of modifying prompts or datasets, enable read-only mode: ```bash # CLI flag langfuse-mcp --read-only # Or envi
- Setup
- Read-Only Mode
- Playbooks
- "Where are the errors?"
- "What happened in this interaction?"
- "Why is it slow?"
- "What's this user experiencing?"
- "Manage datasets"
- "Manage prompts"
- Quick Reference
- Troubleshooting
- MCP connection fails
- No traces found
- Permission denied
Claude Code (project-scoped, shared via .mcp.json) claude mcp add \ langfuse -- uvx --python 3.11 langfuse-mcp Codex CLI (user-scoped, stored in ~/.codex/config.toml) codex mcp add langfuse \ CLI flag langfuse-mcp --read-only Or environment variable
What does the langfuse skill do?
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Use when debugging AI pipelines, investigating errors, analyzing latency, managing prompt versions, or setting up Langfuse. Triggers on "langfuse", "traces", "debug AI", "find exceptions", "what went wrong", "why is it slow", "datasets", "evaluation sets".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langfuse-avivsinai-langfuse-mcp-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.