Network Tracing

Organization

nexus-labs-automation

network-tracing

Instrument API requests with spans and distributed tracing. Use when tracking request latency, correlating client-backend traces, or debugging API issues.

Overview

Publisher	nexus-labs-automation
Repository	`mobile-observability`
Skill name	`network-tracing`
Stars	116
Forks	12
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by nexus-labs-automation on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/nexus-labs-automation/mobile-observability/tree/main/skills/network-tracing

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/nexus-labs-automation/mobile-observability.git /tmp/mobile-observability
mkdir -p .claude/skills
cp -r /tmp/mobile-observability/skills/network-tracing .claude/skills/network-tracing

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Network Tracing 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 Network Tracing 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 Network Tracing 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.

Network Tracing

Measure API requests and correlate with backend traces.

What to Capture (OTel-Compatible Names)

Attribute	OTel Name	Purpose
Method	`http.request.method`	GET, POST, etc.
Status	`http.response.status_code`	Success/failure
URL	`url.path`	Endpoint (sanitized)
Duration	`http.request.duration`	Request time (ms)
Size	`http.response.body.size`	Payload bytes

Using OTel naming = easier migration when OTel mobile matures. See references/otel-mobile.md for rationale.

Distributed Tracing

Propagate trace context to backend:


Client Request
    │
    ├── traceparent: 00-{trace_id}-{span_id}-01
    ├── X-Request-Id: {uuid}
    └── X-Session-Id: {session}
         │
         ▼
    Backend (correlates logs with trace_id)

Key Thresholds

Metric	Good	Acceptable	Poor
API p50	<500ms	<1s	>1s
API p95	<2s	<5s	>5s
Error rate	<1%	<3%	>3%

Integration Options

Choose based on existing vendor:

Vendor	iOS	Android	Approach
Sentry	Auto URLSession swizzling	OkHttp integration	Automatic
Datadog	URLSession delegate	OkHttp interceptor	Semi-auto
Embrace	Auto-instrumentation	Auto-instrumentation	Automatic
New Relic	Auto URLSession instrumentation	Auto OkHttp/HttpURLConnection	Automatic
Custom	Manual interceptor	Manual interceptor	Manual

Automatic (swizzling): Less code, may miss custom clients Manual (interceptors): More control, works with any HTTP client

Platform Integration Points

Platform	Manual Option	Works With
iOS	URLSession delegate	All URLSession-based clients
iOS	Alamofire EventMonitor	Alamofire
Android	OkHttp Interceptor	OkHttp, Retrofit
Android	Ktor HttpClientPlugin	Ktor
RN	fetch wrapper	Native fetch
RN	axios interceptor	axios

Implementation

See references/mobile-challenges.md (Client-Backend Correlation) for:

W3C trace header format
Platform-specific interceptor code
Backend log correlation patterns

See references/performance.md (Network section) for latency budgets.

Frequently asked questions

What does the Network Tracing AI skill do?

Instrument API requests with spans and distributed tracing. Use when tracking request latency, correlating client-backend traces, or debugging API issues.

Why use Network Tracing on TypingMind?

Because you install it once and use it with any model. Network Tracing 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 Network Tracing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nexus-labs-automation/mobile-observability/tree/main/skills/network-tracing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Network Tracing?

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 Network Tracing?

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

Is the Network Tracing AI skill free?

Yes. It is published on GitHub by nexus-labs-automation under the MIT 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Network Tracing

What to Capture (OTel-Compatible Names)

Distributed Tracing

Key Thresholds

Integration Options

Platform Integration Points

Implementation

Frequently asked questions

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