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Dogfood

OrganizationPopular
exceptionless
dogfood

Systematically dogfood Exceptionless locally. Use when asked to dogfood, QA, exploratory test, bug hunt, browser-test, or validate frontend/full-stack behavior in this repo. Start from the AppHost/Aspire local URLs and use the Svelte application. External Exceptionless URLs require an explicit user-provided URL and confirmation.

Overview

Publisherexceptionless
RepositoryExceptionless
Skill namedogfood
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 Dogfood 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/dogfood
  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/dogfood .claude/skills/dogfood
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dogfood 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 Dogfood 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 Dogfood 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.

Dogfood

Systematically explore Exceptionless as a local user, find issues, and produce a concise report with reproducible evidence.

Setup

Local targets:

The Svelte app at / is the only application UI. Exercise primary hash URLs when testing incoming-link compatibility, then verify they resolve to the root routes without losing the session.

  • Aspire dashboard: https://ex.dev.localhost:7101

  • Svelte app: https://web-ex.dev.localhost:7131/

  • API health check: https://api-ex.dev.localhost:7111/api/v2/about

  • API health fallback for command-line tools with local TLS issues: http://api-ex.dev.localhost:7110/api/v2/about

  • Start the app with aspire run or the AppHost before testing.

  • Probe the API health check before browser work.

  • Store evidence under ./dogfood-output/ unless the user provides another workspace-local path.

  • Use the available browser automation tool for local browser interaction when one is exposed in the session.

  • Test localhost by default. Use an external Exceptionless URL only when the user explicitly provides that URL and confirms external testing for the current task.

If the app is not running, start it or report the infrastructure blocker. Do not skip QA just because infrastructure is initially down.

Workflow

1. Health check   Confirm local API responds
2. Initialize     Create output dirs/report
3. Authenticate   Sign in if the tested flow requires it
4. Orient         Capture the starting page and navigation
5. Explore        Walk realistic workflows
6. Document       Save evidence as each issue is found
7. Wrap up        Summarize severity, repro steps, and residual risk

Evidence Rules

  • Interactive or stateful bugs need step-by-step repro notes and screenshots; video is useful when timing or multi-step interaction matters.
  • Static visible issues, such as clipped text or layout breakage on load, only need a screenshot and the viewport details.
  • Capture console/network errors when they explain the user-visible issue.
  • Write findings as you discover them so interruptions do not lose context.
  • Keep evidence files; do not delete or rewrite history mid-session.

Report Format

For each finding include:

  • Severity: P0, P1, P2, or P3
  • Area / URL
  • Environment: local URL, browser, viewport
  • Repro steps
  • Expected result
  • Actual result
  • Evidence file paths
  • Notes on console/network errors, if relevant

Prefer 5-10 well-documented issues over a long vague list.

Scope

Test the changed surface first, then adjacent critical workflows. Never use production data. Production URLs require an explicit user-provided URL and confirmation.

Frequently asked questions

What does the Dogfood AI skill do?

Systematically dogfood Exceptionless locally. Use when asked to dogfood, QA, exploratory test, bug hunt, browser-test, or validate frontend/full-stack behavior in this repo. Start from the AppHost/Aspire local URLs and use the Svelte application. External Exceptionless URLs require an explicit user-provided URL and confirmation.

Why use Dogfood on TypingMind?

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

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

Which AI models can use Dogfood?

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 Dogfood?

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

Is the Dogfood 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.

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