Babysitter is a workflow engine for AI coding agents that enforces predefined steps, quality checks, human approvals, and decision records. It is used to coordinate complex, repeatable agent workflows across supported coding tools. The catalogue contains skills, agents, instructions, settings, a plugin, and an MCP integration for its workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add a5c-ai/babysitter --skill opentelemetry-llmgit clone --depth 1 https://github.com/a5c-ai/babysitterWrote 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/a5c-ai/babysitter/opentelemetry-llm)<a href="https://agentmods.dev/skills/a5c-ai/babysitter/opentelemetry-llm"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/opentelemetry-llm/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/a5c-ai/babysitter/opentelemetry-llm"><img src="https://agentmods.dev/badge/skills/a5c-ai/babysitter/opentelemetry-llm.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 | $0.00018 | $0.00393 |
| Opus 5.5 | $0.00007 | $0.00157 |
| Sonnet 5.5 | $0.00004 | $0.00079 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
opentelemetry-llm 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 29d 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
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)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 29d ago First seen · 73 lines · 18 tokens per session scan A 59fda80b8116
opentelemetry-llm is a skill published in the GitHub repository a5c-ai/babysitter (1,847 stars, last pushed 29d ago), licensed MIT. It adds 18 tokens to every session and 393 once invoked, about $0.0001 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 2026-09-17.
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