Diagnosing Bugs logo

Diagnosing Bugs

Organization
OyadotAI
diagnosing-bugs

This skill should be used when diagnosing a reported bug, intermittent failure, stuck operation, or performance regression and isolating its cause with reproducible evidence.

Overview

PublisherOyadotAI
Repositoryoya-browser
Skill namediagnosing-bugs
Stars
351
Forks
281
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Diagnosing Bugs 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/OyadotAI/oya-browser/tree/main/.agents/skills/diagnosing-bugs
  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/OyadotAI/oya-browser.git /tmp/oya-browser
mkdir -p .claude/skills
cp -r /tmp/oya-browser/.agents/skills/diagnosing-bugs .claude/skills/diagnosing-bugs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Diagnosing Bugs 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 Diagnosing Bugs 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 Diagnosing Bugs 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.

Diagnose the reported symptom

Preserve the assigned role and the user's authorization. QA and reliability assignments remain investigation or verification unless repair is assigned. Write or run tests, add instrumentation, or alter product code only when authorized. Reading this workflow grants no additional tools or permissions. Read relevant repository instructions, glossary, and architectural decisions.

Establish an exact signal

Record the user's symptom, expected result, environment, version, input, and relevant state. Choose the smallest permitted observation that exercises the actual failing path: an existing targeted test, a CLI fixture, an HTTP request, trace replay, or the product journey. Use Oya Browser exclusively for browser reproduction, screenshots, and automation. Read the Oya skill first. Use available native tools for native UI. Preserve the person's live sessions and tabs.

Use scratch data and separate resources. Capture only the lines needed to show the symptom; redact credentials, auth headers, personal data, and sensitive payloads before sharing or saving. Keep secrets in existing protected environment/configuration rather than command text.

Name the exact command or action sequence and the observed outcome. Make the signal specific to the reported symptom; successful startup alone cannot prove a failed interaction is fixed. For intermittent failures, report attempts, failures, timing, and controlled conditions rather than claiming determinism. Bound repetition, resource use, and runtime.

If reproduction is blocked, state what was attempted and the missing environment or evidence. Continue bounded static investigation where useful, label hypotheses as unverified, and request only the information needed to unblock. Never claim reproduction or verification from code alone.

Minimize and compare hypotheses

Reduce inputs, dependencies, and steps one at a time while preserving the exact symptom. Keep a copy of the original case. For a hard bug, list a few ranked, falsifiable causes; for a simple defect, use the smallest useful set. State what observation would support or reject each cause. Share the leading hypotheses concisely, then continue permitted investigation without requiring an approval checkpoint for routine diagnostic choices.

Choose each probe to distinguish hypotheses. Change one variable at a time. Prefer focused inspection or a debugger to broad logging. Tag authorized temporary instrumentation with a unique prefix and record its paths for cleanup. For performance, collect a baseline and compare the same workload and conditions; account for warmup and variance before attributing a cause.

Verify or hand off

For a diagnosis-only assignment, return the cause supported by evidence, rejected alternatives, the smallest proposed repair, and an exact verification recipe. Keep implementation unchanged.

When repair and testing are authorized, exercise the real failure through the caller-facing contract. Establish the regression signal before the fix, then rerun it and the original case afterward. If forcing failure with a deliberate mutation, inspect the diff to confirm it landed before trusting the result. Explain any missing test seam or limits of an isolated fake.

Remove owned temporary instrumentation and fixtures; preserve existing user changes and running services. Report the cause, exact observations, executed checks, remaining uncertainty, and cleanup. Use assets/investigation.md when a repeatable investigation record is useful; omit fields that do not apply. Complete only with an evidenced diagnosis or a clear blocker and next actionable step, without presenting an unrun repair as verified.

Adapted from Matt Pocock's engineering skills; see SOURCE.md and LICENSE.

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 Diagnosing Bugs AI skill do?

This skill should be used when diagnosing a reported bug, intermittent failure, stuck operation, or performance regression and isolating its cause with reproducible evidence.

Why use Diagnosing Bugs on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OyadotAI/oya-browser/tree/main/.agents/skills/diagnosing-bugs. 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 Diagnosing Bugs?

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 Diagnosing Bugs?

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

Is the Diagnosing Bugs AI skill free?

It is published on GitHub by OyadotAI. Check the repository for licensing terms. 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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