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Opentelemetry Codex

Organization
SpecterOps
opentelemetry-codex

Install and configure OpenTelemetry for Codex-oriented workflows, including OTLP exporter setup, collector endpoint wiring, and validation that traces/logs/metrics are emitted and stored under PROJECT_PATH/OpenTelemetry.

Overview

PublisherSpecterOps
Repositoryskills
Skill nameopentelemetry-codex
Stars
706
Forks
80
Bundled files
3
LicenseApache-2.0
Links
Skill sourceRepository
  • 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 SpecterOps on GitHub. Read the source before you install it.

Installation

Install the Opentelemetry Codex 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/SpecterOps/skills/tree/main/plugins/codex-observability/skills/opentelemetry-codex
  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/SpecterOps/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/codex-observability/skills/opentelemetry-codex .claude/skills/opentelemetry-codex
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Opentelemetry Codex 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 Opentelemetry Codex 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 Opentelemetry Codex 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.

OpenTelemetry Codex

Use this skill when the operator asks to add OpenTelemetry (OTEL) instrumentation or telemetry export for Codex workflows in a repository. Direct trigger phrase: Install OpenTelemetry.

Input Contract

Accept input as: PROJECT_PATH OTLP_ENDPOINT MODE

Mode:

  • plan: produce an implementation plan only
  • execute (default): apply configuration and validate telemetry emission

Examples:

  • $opentelemetry-codex /path/to/repo http://127.0.0.1:4318 execute
  • $opentelemetry-codex /path/to/repo https://otel-collector.internal:4318 plan

Preconditions

  1. Confirm the target project is in scope for modification.
  2. Detect project runtime/language before choosing instrumentation package(s).
  3. Confirm an OTLP endpoint exists (collector or vendor gateway).
  4. Configure Codex OTEL via config.toml blocks, not shell env vars.
  5. Default local telemetry storage path is PROJECT_PATH/OpenTelemetry.

Execution Workflow

  1. Baseline inventory:
  • detect primary runtime and dependency manager,
  • identify existing telemetry/logging framework.
  • if PROJECT_PATH/OpenTelemetry/install_opentelemetry.sh exists, prefer it as the default implementation path for consistency across clones.
  1. Configure Codex OTEL in config.toml:
  • [otel]
  • service_name = "codex-agent" (or project-specific service name)
  • log_user_prompt = true (when operator wants prompt logging)
  • exporter = "otlp-http"
  • [otel.exporter."otlp-http"]
  • endpoint = "<OTLP_ENDPOINT>"
  • protocol = "json" (use binary when compact transport is preferred)
  • Use supported exporter variants (otlp-http, otlp-grpc, statsig, none) and valid protocol values when applicable.
  • Do not rely on OTEL_* shell environment variables for primary wiring.
  1. Install/Bootstrap (preferred path):
  • run "$PROJECT_PATH/OpenTelemetry/install_opentelemetry.sh" when available.
  • this should prepare the local runtime and ensure telemetry output files exist.
  1. Add startup wiring so telemetry initializes at process start.
  • default runner should be "$PROJECT_PATH/OpenTelemetry/run_codex_with_otel.sh" for user-friendly operation.
  • when using an OpenTelemetry Collector, configure a file exporter writing to:
    • "$PROJECT_PATH/OpenTelemetry/otel-logs.jsonl"
    • "$PROJECT_PATH/OpenTelemetry/otel-traces.jsonl" (if traces are persisted locally)

  1. Add a minimal smoke operation that should emit telemetry (single request/task run).

  2. Validate:

  • process starts successfully,
  • OTEL exporter attempts/acknowledges delivery,
  • collector/backend receives signal data (trace/log/metric as available).
  • local files under "$PROJECT_PATH/OpenTelemetry" are created and non-empty when file export is enabled.
  1. Document exact run commands and outputs for reproducibility.

Codex Data Capture Guidance

When instrumenting Codex workflows, capture at least:

  • command execution lifecycle (start/end/error),
  • task identifiers and branch/repo context,
  • high-level agent role labels (planner, researcher, etc.),
  • latency and status outcomes.

Do not export secrets, raw credentials, or full sensitive payloads in spans/logs. Use redaction or attribute allow-lists for safety.

Reporting Requirements

For each implementation, include:

  • runtime and OTEL package(s) installed,
  • exact config changes (file paths and config.toml OTEL blocks),
  • local storage path used: PROJECT_PATH/OpenTelemetry,
  • verification commands and resulting output,
  • timestamp (UTC ISO 8601),
  • confirmed telemetry signals observed,
  • follow-up hardening recommendations.

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 Opentelemetry Codex AI skill do?

Install and configure OpenTelemetry for Codex-oriented workflows, including OTLP exporter setup, collector endpoint wiring, and validation that traces/logs/metrics are emitted and stored under PROJECT_PATH/OpenTelemetry.

Why use Opentelemetry Codex on TypingMind?

Because you install it once and use it with any model. Opentelemetry Codex 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 Opentelemetry Codex in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SpecterOps/skills/tree/main/plugins/codex-observability/skills/opentelemetry-codex. 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 Opentelemetry Codex?

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 Opentelemetry Codex?

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

Is the Opentelemetry Codex AI skill free?

Yes. It is published on GitHub by SpecterOps 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.

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