Otel Component Telemetry logo

Otel Component Telemetry

Organization
ollygarden
otel-component-telemetry

Scan one OpenTelemetry component at pinned upstream versions and record the telemetry it emits (spans, metrics, logs) as change-point files under skills/otel-telemetry-emissions/. Invoked headless by scripts/scan.sh, one agent per component from config.json. Authoring-time only — not shipped.

Overview

Publisherollygarden
Repositoryopentelemetry-agent-skills
Skill nameotel-component-telemetry
Stars
119
Forks
13
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ollygarden on GitHub. Read the source before you install it.

Installation

Install the Otel Component Telemetry AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ollygarden/opentelemetry-agent-skills/tree/main/tools/otel-telemetry-emission-scan
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ollygarden/opentelemetry-agent-skills.git /tmp/opentelemetry-agent-skills
mkdir -p .claude/skills
cp -r /tmp/opentelemetry-agent-skills/tools/otel-telemetry-emission-scan .claude/skills/otel-component-telemetry
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Otel Component Telemetry in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Otel Component Telemetry on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Otel Component Telemetry is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

You scan one component from the OpenTelemetry ecosystem and write its telemetry emission inventory.

Repo: ${Repo} Path: ${Path} Versions to scan: ${Version} Tag prefix: ${TagPrefix} Force: ${Force} Output root: ${OutDir}

Procedure

  1. Resolve versions. The repo is already cloned locally at /tmp/otel-component-telemetry/<repo basename> — do not clone it yourself. Versions to scan is either a count of recent releases or one exact tag. For a count, list that many released versions from git tag. For an exact tag, scan only that tag. When Tag prefix is non-empty, consider only tags beginning with that literal prefix, check out the full tag, and strip the prefix to obtain the version used in frontmatter and the output filename. For independently versioned packages, confirm the package version in its manifest or version.py, even when a repository release tag exists (e.g. Python repo tag v1.16.0 contains instrumentation 0.37b0). Use that package version for the filename and version, and git rev-parse '<tag>^{commit}' for the full commit_sha.

    For Python count requests, select recent instrumentation beta tags (v<major>.<minor>b<revision>); the historical SDK tag v1.16.0 is supported only as an exact-tag request. Normalize the filename and version to exactly one leading v.

  2. Get source. Check out each resolved tag into its own temp directory: git worktree add "$(mktemp -d)" <tag>. Never scan a branch — only pinned tags.

  3. Ground truth in the code. Follow instrument/span/log construction through shared helpers and generated code. Record literal names after prefixes and namespaces are applied, exact units, and attributes from recording sites; semantic-convention definitions alone do not prove emission. Distinguish generated pipeline telemetry from the component's internal telemetry. Read the actual meter/tracer scope creation; when rows use different scopes, name them in Notes rather than assigning the component's path to all rows.

  4. Record conditionality. If a signal, metric, or attribute is gated (feature gate, opt-in config, experimental semconv env var, framework/runtime version), it still gets a row — with the gate and default named in Notes. Check both branches and client/server recording sites separately. If emission is delegated to a runtime/library outside the pinned repository, identify that boundary and source instead of claiming its telemetry was verified here.

  5. Recheck before writing. Compare each row to its construction and recording sites, specifically checking prefixes/separators, scopes, units, attribute sets, and disabled/default branches. List exact source paths present at the pinned commit; do not cite guessed paths or abbreviate them with .... Reconcile an existing component/version file rather than creating another path. Set last_verified to today's date only after this source check, never copy a date from another version. Run python3 tools/otel-telemetry-emission-scan/scripts/validate.py --component '${Repo}/${Path}' from the repository root. This checks provenance and source paths, not semantic completeness; fix failures before accepting output.

Updates

  • A version whose file already exists with a non-null verification date is skipped. A last_verified: null file needs a source recheck and reconciliation even without force.
  • If your prompt says force: true, scan it anyway and reconcile: compare our findings with the existing file and edit only rows that are wrong or missing. Do not rewrite the whole file.

Never modify files of other components.

Output

Output one file per scanned version under the output root given above, mirroring repo + path:

${OutDir}/<repo basename>/<path>/v<version>.md

Example: ${OutDir}/opentelemetry-collector-contrib/receiver/kafkareceiver/v0.158.0.md

Follow the below format strictly:

md
---
repo: $repo
path: $path
version: v$version
scope_name: $scope_name
commit_sha: $full_commit_sha
last_verified: $YYYY-MM-DD
---

# $component

## Traces

| Span name | Kind | Attributes | Notes |
|---|---|---|---|

## Metrics

| Metric name | Type | Unit | Attributes | Notes |
|---|---|---|---|---|

## Logs

| Log/event name | Attributes | Notes |
|---|---|---|

## Sources

- $files_consulted

Omit any signal section the component does not emit — never write empty tables. Notes names the gate when emission is conditional, - otherwise.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Otel Component Telemetry AI skill do?

Scan one OpenTelemetry component at pinned upstream versions and record the telemetry it emits (spans, metrics, logs) as change-point files under skills/otel-telemetry-emissions/. Invoked headless by scripts/scan.sh, one agent per component from config.json. Authoring-time only — not shipped.

Why use Otel Component Telemetry on TypingMind?

Because you install it once and use it with any model. Otel Component Telemetry is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Otel Component Telemetry in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ollygarden/opentelemetry-agent-skills/tree/main/tools/otel-telemetry-emission-scan. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Otel Component Telemetry?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Otel Component Telemetry?

As many as you like. As long as a model supports skills, you can use Otel Component Telemetry with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Otel Component Telemetry AI skill free?

Yes. It is published on GitHub by ollygarden under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇