Hai Debug

Community

hylarucoder

hai-debug

Diagnoses an unexplained software failure through reproduction, competing hypotheses, discriminating checks, and a causal explanation. Use for 排障、根因分析、偶发失败、环境差异、重复执行, or a bug whose cause is unclear. Return observed evidence, ruled-out hypotheses, cause/confidence, and a regression target; fix and verify only when the request includes fixing. Use hai-tdd when the cause and desired behavior are already known, and hai-architecture for change complexity without a malfunction.

Overview

Publisher	hylarucoder
Repository	`hai-stack`
Skill name	`hai-debug`
Stars	284
Forks	15
Bundled files	3
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.
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 hylarucoder on GitHub. Read the source before you install it.

Installation

Install the Hai Debug 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.

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/hylarucoder/hai-stack.git /tmp/hai-stack
mkdir -p .claude/skills
cp -r /tmp/hai-stack/skills/hai-debug .claude/skills/hai-debug

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

Use it in TypingMind

Enable Hai Debug 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 Hai Debug 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 Hai Debug 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.

Hai Debug

For Chinese readers, see SKILL.zh_CN.md. English is the execution source of truth. Status: trial. The method has not yet demonstrated improvement across representative real tasks.

Principle

An explanation must account for the observed failure and predict a check that could disprove it. A plausible patch is not causal evidence. Begin with the cheapest check that separates likely causes.

Workflow

Establish expected versus actual behavior, scope, timing, environment/version, and requested action (diagnose or fix). Read available logs, errors, code, recent changes, and existing tests before asking for information that is already available.
Reproduce on an isolated local fixture when possible. Record inputs, preconditions, command, and observed result. For intermittent failures, capture frequency/order/concurrency and a bounded repeat strategy; do not loop indefinitely or hammer live services.
Trace the symptom backward through callers, state/config, persistence, and external boundaries. Compare working and failing cases. Distinguish observations, assumptions, and historical claims.
Form a small set of plausible competing hypotheses. For each, record supporting/conflicting evidence and one cheap discriminating check. A trivial obvious cause needs no ceremonial table.
Run the highest-information safe check, update confidence, and eliminate contradicted hypotheses. Instrument narrowly if authorized by the fix request; diagnose-only work uses read-only checks or separate disposable reproductions, not edits to user code.
Explain the causal chain: precondition → faulty decision/state → observable symptom. Cite actual file:line/log/test evidence. A failed reproduction leaves the cause unconfirmed; report the best next check and missing evidence rather than guessing.
If only diagnosis was requested, stop with the cause and a proposed repair/regression target. If fixing was requested, continue through the smallest complete repair and verification: use hai-tdd when a meaningful behavioral RED exists; otherwise use honest targeted integration/runtime checks. Reproduce the original case again and check nearby contracts.
Deliver using references/output-template.md. Separate symptom mitigation from root-cause repair and executed checks from proposed checks.

Constraints and stop rules

Respect the user's environment and authority. Reproduction does not authorize destructive production operations, external messages, paid calls, or traffic load. Prefer isolated fixtures. After repeated checks yield no new evidence, change the hypothesis or evidence source; identify the exact blocker if progress needs unavailable data. Elapsed time is not proof.

Do not broadly refactor, suppress errors, add blind retries, or change expected behavior merely to make the symptom disappear. A mitigation may be useful but must be labeled and bounded. Preserve unrelated work and remove only temporary instrumentation created for this task when its diagnostic purpose is complete.

Handoffs

Cause known, implement a behavior change → hai-tdd when suitable, otherwise normal execution.
System hard to change without a specific failure → hai-architecture.
Review a completed change → code-review-and-quality.
Prove the full change against requirements → write-technical-acceptance-report.
Multi-phase repair with unresolved execution dependencies → hai-goal.

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 Hai Debug AI skill do?

Why use Hai Debug on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hylarucoder/hai-stack/tree/main/skills/hai-debug. 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 Hai Debug?

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 Hai Debug?

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

Is the Hai Debug AI skill free?

It is published on GitHub by hylarucoder. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Hai Debug

Principle

Workflow

Constraints and stop rules

Handoffs

Bundled files

Frequently asked questions

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