Aspire Diagnostics logo

Aspire Diagnostics

OrganizationPopular
exceptionless
aspire-diagnostics

Inspect local Aspire resource health, logs, traces, and browser telemetry.

Overview

Publisherexceptionless
RepositoryExceptionless
Skill nameaspire-diagnostics
Stars
2.5K
Forks
508
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Aspire Diagnostics 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/exceptionless/Exceptionless/tree/main/.agents/skills/aspire-diagnostics
  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/exceptionless/Exceptionless.git /tmp/Exceptionless
mkdir -p .claude/skills
cp -r /tmp/Exceptionless/.agents/skills/aspire-diagnostics .claude/skills/aspire-diagnostics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aspire Diagnostics 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 Aspire Diagnostics 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 Aspire Diagnostics 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.

Aspire Diagnostics

Use runtime evidence to diagnose a local failure or measure an affected operation. Follow root AGENTS.md for runtime startup and endpoint scope.

From the repository root, select this AppHost explicitly:

sh
aspire describe --apphost src/Exceptionless.AppHost --format Json --non-interactive

Use the reported resource names and endpoints. Add --include-hidden when a resource is not listed.

EvidenceCommand
Structured logsaspire otel logs <resource> --limit 100 --format Json --non-interactive
Process outputaspire logs <resource> --tail 100 --timestamps --non-interactive
Recent tracesaspire otel traces --limit 20 --format Json --non-interactive
Trace spansaspire otel spans <resource> --trace-id <traceId> --format Json --non-interactive
Exportaspire export --output ./dogfood-output/aspire-telemetry.zip --non-interactive

Diagnose the affected operation

Start with resource health and the reported failure. Prefer structured logs for application errors and contextual fields; use console logs for startup failures, crashes, and other process output. Use traces when the failure or latency crosses API, queue, job, or storage boundaries, then inspect the relevant spans.

Correlate evidence with the user action, timestamp, resource, and trace ID. For a known trace, inspect related logs with aspire otel logs --trace-id <traceId> --format Json --non-interactive. A healthy resource alone does not establish that the operation succeeded.

Performance investigation

  1. Record the app URL, resource names, action, and timestamp; perform the action once.
  2. Locate its trace and inspect spans for repeated API requests, Elasticsearch searches, Redis operations, queue publishes, and job processing.
  3. Count calls by dependency and operation, identify slow spans, and correlate errors with structured logs. Distinguish retries from duplicate work.
  4. Use dashboard metrics for aggregate request counts, error rates, and latency trends. A single trace describes that request, not the workload's overall performance.
  5. Export telemetry when a deeper comparison or report needs the evidence preserved.

Report the exact action, resource names, trace IDs, repeated-call counts, durations, and relevant errors. State any missing telemetry that limits the conclusion.

Browser and environment boundaries

The AppHost enables WithBrowserLogs() for the Svelte App resource, which serves the application at /. Discover that resource's current endpoint. Use its dashboard for browser console/network evidence or screenshots when they explain a UI failure, and correlate browser activity with backend traces where available.

Avoid restarting the whole AppHost as a diagnostic loop. For deployed or external environments, follow the user's explicit scope and the platform's diagnostics; do not assume local Aspire commands or endpoints apply.

Frequently asked questions

What does the Aspire Diagnostics AI skill do?

Inspect local Aspire resource health, logs, traces, and browser telemetry.

Why use Aspire Diagnostics on TypingMind?

Because you install it once and use it with any model. Aspire Diagnostics 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 Aspire Diagnostics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/exceptionless/Exceptionless/tree/main/.agents/skills/aspire-diagnostics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aspire Diagnostics?

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 Aspire Diagnostics?

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

Is the Aspire Diagnostics AI skill free?

Yes. It is published on GitHub by exceptionless 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 👇