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LangFuse Integration Skill — measured AI agent skill | DecimalAI

a5c-ai/langfuse-integration

SkillScore48Maintained

LangFuse LLM observability integration for tracing, analytics, and cost tracking

by a5c-ai

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

AI Engineer pack

decimalai skills pull a5c-ai-langfuse-integration

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/a5c-ai-langfuse-integration/SKILL.md

Raw file: /s/a5c-ai-langfuse-integration/SKILL.md ↗ · machine-readable summary at /s/a5c-ai-langfuse-integration.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

23 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 9d ago

Yes — +30% lift-12% tokens0% turns48% of whole cases pass with skill (11/23)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	-35%	0%
case-04	✗→✓	▲ Improved	-13%	0%
case-05	✗→✓	▲ Improved	-13%	0%
case-06	✗→✓	▲ Improved	5%	0%
case-09	✗→✓	▲ Improved	-11%	0%

Showing 5 of 23 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/31/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

a5c-ai / babysitter

library/specializations/ai-agents-conversational/skills/langfuse-integration/SKILL.md

View on GitHub ↗

License MITImported 7/31/2026Version v1

Skill contents

SKILL.md

LangFuse Integration Skill

Capabilities

Set up LangFuse tracing for LLM calls
Configure cost tracking and analytics
Implement prompt management
Set up evaluation datasets
Design custom trace metadata
Create dashboards and alerts

Target Processes

llm-observability-monitoring
cost-optimization-llm

Implementation Details

Core Features

Tracing: Track LLM calls, chains, and agents
Prompts: Version and manage prompts
Analytics: Usage, latency, cost metrics
Datasets: Evaluation and testing data
Scores: Track output quality

Integration Methods

LangChain callback handler
Direct SDK integration
OpenAI drop-in replacement
Decorator-based tracing

Configuration Options

Public/secret keys
Host URL (cloud or self-hosted)
Sampling rate
Metadata configuration
User tracking

Best Practices

Consistent trace naming
Meaningful metadata
Regular prompt versioning
Set up alerting

Dependencies

langfuse
langchain (for callback integration)

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Other measured skills in the registry, with their headline benchmark lift.