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 adrielp/ai-engineering-harness --skill otel-instrumentgit clone --depth 1 https://github.com/adrielp/ai-engineering-harnessWrote 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/adrielp/ai-engineering-harness/otel-instrument)<a href="https://agentmods.dev/skills/adrielp/ai-engineering-harness/otel-instrument"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/otel-instrument/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/adrielp/ai-engineering-harness/otel-instrument"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/otel-instrument.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 | $0.00042 | $0.00828 |
| Opus 5 | $0.00021 | $0.00414 |
| Sonnet 5 | $0.00008 | $0.00166 |
| Haiku 4.5 | $0.00004 | $0.00083 |
Grade A, and why
otel-instrument 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 6d 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.
This is a copy
92% identical to otel_instrument — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenTelemetry Orchestrator
You are the routing layer for all OpenTelemetry work. Detect intent, gather minimal context, then delegate to the right sub-skill. Never produce OTel guidance directly — always route.
Activation Triggers
Auto-activate when the user mentions: observability, telemetry, instrument, OpenTelemetry, OTel, tracing, spans, metrics, counters, histograms, structured logging, trace correlation, Collector configuration, OTTL, transforms, redaction, semantic conventions, attribute naming, sampling, exporters, receivers, processors.
Step 1: Gather Context
Detect the stack and existing OTel footprint before routing:
ls package.json go.mod requirements.txt pyproject.toml pom.xml build.gradle *.csproj Gemfile 2>/dev/null
grep -rl "opentelemetry\|otel" --include="*.json" --include="*.toml" --include="*.xml" --include="*.gradle" --include="*.csproj" --include="*.mod" . 2>/dev/null | head -20
find . -name "otel-collector*" -o -name "collector-config*" -o -name "otelcol*" 2>/dev/null | head -10
Step 2: Route
Match the user's intent to exactly one sub-skill. Use the first match:
| If the request involves… | Route to |
|---|---|
| Local instrumentation feedback loop, "drive with observability", trace-as-design-artifact, narrative-first, ODD | observability-driven-development |
| Collector YAML, receivers, processors, exporters, pipelines, deployment, sampling policies | otel_collector |
| OTTL expressions, transform processor, filter expressions, Collector-side redaction/normalization | otel_ottl |
| Attribute naming, placement rules, legacy→current migration, semantic convention lookup | otel_semantic_conventions |
| SDK setup, adding traces/metrics/logs, language-specific instrumentation, span design, validation | otel_instrumentation |
If ambiguous, ask:
Which area? (1) Application instrumentation (2) Collector configuration (3) Semantic conventions (4) OTTL transforms
Step 3: Multi-Skill Sequencing
Some tasks span skills. Execute in this order:
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.
- 6d ago First seen · 60 lines · 42 tokens per session scan A 3d3fa361edc4
otel-instrument is a skill published in the GitHub repository adrielp/ai-engineering-harness (20 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 828 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to otel_instrument, differing in 7 lines, and is treated as a copy.
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