Gdd Debug

Community

hegemonart

gdd-debug

Symptom-driven design debugger with persistent state. Phase 1 builds a feedback loop; Phase 2 hypothesizes. Writes findings to .design/DEBUG.md. Use when a symptom needs systematic, one-variable-at-a-time tracking.

Overview

Publisher	hegemonart
Repository	`get-design-done`
Skill name	`gdd-debug`
Stars	3
Forks	1
Bundled files	1
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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by hegemonart on GitHub. Read the source before you install it.

Installation

Install the Gdd 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.
https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/debug
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/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/debug .claude/skills/gdd-debug

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

Use it in TypingMind

Enable Gdd 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 Gdd 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 Gdd 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.

{{command_prefix}}debug

Systematic, checkpoint-driven design debugger. Loads framing from ./../reference/debugger-philosophy.md (five principles) and the feedback-loop construction catalog from ./debug-feedback-loops.md (10 priority-ordered loop paths). Phase 1 builds the loop; Phase 2 generates hypotheses. Writes every step to .design/DEBUG.md so killed sessions can resume.

Steps

Load philosophy + feedback-loop catalog: Read reference/debugger-philosophy.md (five principles) and ./debug-feedback-loops.md (10 construction paths; iterate-on-loop discipline). Keep both in mind for the entire session.
Symptom: If no symptom argument was passed, ask (AskUserQuestion): "What design symptom are you investigating? (observable only - 'cards look crowded', not 'padding is wrong')"
Resume check: Read .design/DEBUG.md if it exists. If there is an open session with no ### Fix Proposal block, ask: "Resume existing session '' or start a new one?"
Ground truth load: Read .design/DESIGN-PLAN.md (goals), .design/STATE.md <decisions> block (D-XX items), and any source files pointed at by the symptom.
Phase 1 - Build a feedback loop: Before ANY hypothesizing, build a deterministic, fast, agent-runnable pass/fail signal that reproduces the symptom. See ./debug-feedback-loops.md for the 10 construction paths in priority order (failing test > curl > CLI fixture > headless browser > trace replay > throwaway harness > fuzz > bisect > differential > HITL bash). Iterate on the loop itself (cache setup, narrow scope, pin time, seed RNG, isolate filesystem, freeze network) before iterating on the bug. For non-deterministic bugs: raise reproduction rate to at least 30%, not clean repro. Do not proceed to Phase 2 (hypothesis generation) until you have a loop you believe in.
Optional rendered-output check: Use ToolSearch to see if Playwright/Preview MCP tools are available. If yes, capture rendered state. If no, fall back to code-only analysis.
Phase 2 - Investigation loop (one hypothesis at a time) - for each step:
- Form one hypothesis (one variable).
- Investigate (read files, grep, measure). Re-run the Phase 1 feedback loop to confirm the hypothesis pinpoints the symptom.
- Append to .design/DEBUG.md:
```
markdown
## <symptom> — <date>
### Hypothesis <N>
### Investigation
### Finding
```
- Ask (AskUserQuestion): "Continue investigating? (yes / found it / dead end)"
When found: Write ### Fix Proposal block with a concrete patch description. Re-run the Phase 1 loop to confirm the fix flips it from fail to pass. Ask: "Create a todo with {{command_prefix}}todo add, or execute the fix now?"

Do Not

Do not change multiple variables at once.
Do not modify global tokens to fix a single component without explicit user approval.
Do not close the DEBUG.md session without a finding (mark as "dead end" if abandoned).

DEBUG COMPLETE

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

debug-feedback-loops.md9 KB

Frequently asked questions

What does the Gdd Debug AI skill do?

Why use Gdd Debug on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/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 Gdd 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 Gdd Debug?

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

Is the Gdd Debug AI skill free?

Yes. It is published on GitHub by hegemonart 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

{{command_prefix}}debug

Steps

Do Not

DEBUG COMPLETE

Bundled files

Frequently asked questions

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