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OpenTelemetry LLM Skill — measured AI agent skill | DecimalAI

a5c-ai/opentelemetry-llm

SkillScore14Maintained

OpenTelemetry instrumentation for LLM applications with distributed tracing

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-opentelemetry-llm

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/a5c-ai-opentelemetry-llm/SKILL.md

Raw file: /s/a5c-ai-opentelemetry-llm/SKILL.md ↗ · machine-readable summary at /s/a5c-ai-opentelemetry-llm.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

22 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 — +9% lift+12% tokens0% turns14% of whole cases pass with skill (3/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-04	✗→✓	▲ Improved	6%	0%
case-05	✗→✓	▲ Improved	29%	0%
case-18	✓→✓	＝ Same ✓	23%	0%
case-01	✗→✗	＝ Same ✗	4%	0%
case-02	✗→✗	＝ Same ✗	24%	0%

Showing 5 of 22 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/opentelemetry-llm/SKILL.md

View on GitHub ↗

License MITImported 7/31/2026Version v1

Skill contents

SKILL.md

OpenTelemetry LLM Skill

Capabilities

Configure OpenTelemetry SDK for LLM apps
Implement LLM-specific instrumentation
Set up trace exporters (Jaeger, OTLP)
Design semantic conventions for LLM
Configure span attributes for AI workloads
Implement context propagation

Target Processes

llm-observability-monitoring
agent-deployment-pipeline

Implementation Details

Core Components

TracerProvider: SDK configuration
SpanProcessor: Batch/simple processors
Exporters: Jaeger, OTLP, Console
Instrumentation: Auto and manual

LLM Semantic Conventions

gen_ai.system (OpenAI, Anthropic)
gen_ai.request.model
gen_ai.request.max_tokens
gen_ai.response.finish_reason
gen_ai.usage.prompt_tokens

Configuration Options

Exporter selection
Sampling strategies
Resource attributes
Span limits
Context propagation

Best Practices

Consistent attribute naming
Appropriate sampling
Error handling traces
Propagate context across services

Dependencies

opentelemetry-sdk
opentelemetry-exporter-
openinference (optional)

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