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 acaprino/daodan --skill opentelemetrygit clone --depth 1 https://github.com/acaprino/daodanWrote 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/acaprino/daodan/opentelemetry)<a href="https://agentmods.dev/skills/acaprino/daodan/opentelemetry"><img src="https://agentmods.dev/badge/skills/acaprino/daodan/opentelemetry.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.01566 |
| Opus 5 | $0.00028 | $0.00783 |
| Sonnet 5 | $0.00011 | $0.00313 |
| Haiku 4.5 | $0.00006 | $0.00157 |
Grade A, and why
opentelemetry 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenTelemetry Python
Index for OTel Python -- traces, metrics, log-trace correlation, distributed propagation. References hold the gotchas; canonical reference lives at https://opentelemetry-python.readthedocs.io and https://opentelemetry.io/docs/.
When to use
- New Python service that needs distributed tracing
- Adding OTel to FastAPI / Celery / async Python
- Custom transports (AMQP, ZMQ, Kafka) needing propagator wiring
- OTLP exporter / Collector / AWS ADOT configuration
- Auditing existing instrumentation for gaps or anti-patterns
- Log-trace correlation
- Sampling strategy choice for production
Quick-start production recipe
For most Python services, start with this and iterate:
- Init:
opentelemetry-bootstrap -a install+opentelemetry-instrumentwrapper - Resource: set
service.name,service.version,deployment.environment - Sampler:
ParentBased(TraceIdRatioBased(0.1))-- 10% head sampling - Exporter: OTLP gRPC to a local Collector at
localhost:4317 - Processor:
BatchSpanProcessorwith default tuning (raiseOTEL_BSP_MAX_QUEUE_SIZE=8192if bursty) - Shutdown: register
provider.shutdown()in lifespan /atexit/SIGTERM
Then escalate based on what you actually need:
- Custom business spans → manual
tracer.start_as_current_span() - Non-HTTP transport → custom propagator (skeleton in
exporters-and-backends.md) - AWS deployment → ADOT distro + X-Ray ID generator (
aws-deployment.md) - High throughput → tune BSP queue size + export timeout
- Error-only retention → tail sampling at the Collector
Auto vs manual instrumentation (the matrix)
| Layer | Approach | Examples |
|---|---|---|
| HTTP frameworks | Auto | FastAPI, Django, Flask |
| Database clients | Auto | SQLAlchemy, psycopg2, asyncpg |
| HTTP clients | Auto | httpx, requests, aiohttp |
| Message queues | Auto | Celery, Kafka |
| Cache | Auto | redis, memcached |
| Business logic | Manual | Order processing, payment flows |
| Custom transport | Manual | AMQP payload, ZMQ events |
What ships with it
5 files 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.
- 2d ago First seen · 121 lines · 55 tokens per session scan A 3efa60b134a8
opentelemetry is a skill published in the GitHub repository acaprino/daodan (8 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 1,566 once invoked, about $0.0003 per session on Opus 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 2026-09-05.
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