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Gitnexus Debugging

Community
JackReis
gitnexus-debugging

Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"

Overview

PublisherJackReis
Repositoryneurodivergent-visual-org
Skill namegitnexus-debugging
Stars
73
Forks
8
Bundled files
Instructions only
LicenseMIT
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 JackReis on GitHub. Read the source before you install it.

Installation

Install the Gitnexus Debugging 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/JackReis/neurodivergent-visual-org/tree/main/.claude/skills/gitnexus/gitnexus-debugging
  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/JackReis/neurodivergent-visual-org.git /tmp/neurodivergent-visual-org
mkdir -p .claude/skills
cp -r /tmp/neurodivergent-visual-org/.claude/skills/gitnexus/gitnexus-debugging .claude/skills/gitnexus-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gitnexus Debugging 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 Gitnexus Debugging 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 Gitnexus Debugging 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.

Debugging with GitNexus

When to Use

  • "Why is this function failing?"
  • "Trace where this error comes from"
  • "Who calls this method?"
  • "This endpoint returns 500"
  • Investigating bugs, errors, or unexpected behavior

Workflow

1. gitnexus_query({query: "<error or symptom>"})            → Find related execution flows
2. gitnexus_context({name: "<suspect>"})                    → See callers/callees/processes
3. READ gitnexus://repo/{name}/process/{name}                → Trace execution flow
4. gitnexus_cypher({query: "MATCH path..."})                 → Custom traces if needed

If "Index is stale" → run npx gitnexus analyze in terminal.

Checklist

- [ ] Understand the symptom (error message, unexpected behavior)
- [ ] gitnexus_query for error text or related code
- [ ] Identify the suspect function from returned processes
- [ ] gitnexus_context to see callers and callees
- [ ] Trace execution flow via process resource if applicable
- [ ] gitnexus_cypher for custom call chain traces if needed
- [ ] Read source files to confirm root cause

Debugging Patterns

SymptomGitNexus Approach
Error messagegitnexus_query for error text → context on throw sites
Wrong return valuecontext on the function → trace callees for data flow
Intermittent failurecontext → look for external calls, async deps
Performance issuecontext → find symbols with many callers (hot paths)
Recent regressiondetect_changes to see what your changes affect

Tools

gitnexus_query — find code related to error:

gitnexus_query({query: "payment validation error"})
→ Processes: CheckoutFlow, ErrorHandling
→ Symbols: validatePayment, handlePaymentError, PaymentException

gitnexus_context — full context for a suspect:

gitnexus_context({name: "validatePayment"})
→ Incoming calls: processCheckout, webhookHandler
→ Outgoing calls: verifyCard, fetchRates (external API!)
→ Processes: CheckoutFlow (step 3/7)

gitnexus_cypher — custom call chain traces:

cypher
MATCH path = (a)-[:CodeRelation {type: 'CALLS'}*1..2]->(b:Function {name: "validatePayment"})
RETURN [n IN nodes(path) | n.name] AS chain

Example: "Payment endpoint returns 500 intermittently"

1. gitnexus_query({query: "payment error handling"})
   → Processes: CheckoutFlow, ErrorHandling
   → Symbols: validatePayment, handlePaymentError

2. gitnexus_context({name: "validatePayment"})
   → Outgoing calls: verifyCard, fetchRates (external API!)

3. READ gitnexus://repo/my-app/process/CheckoutFlow
   → Step 3: validatePayment → calls fetchRates (external)

4. Root cause: fetchRates calls external API without proper timeout

Frequently asked questions

What does the Gitnexus Debugging AI skill do?

Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"

Why use Gitnexus Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JackReis/neurodivergent-visual-org/tree/main/.claude/skills/gitnexus/gitnexus-debugging. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gitnexus Debugging?

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 Gitnexus Debugging?

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

Is the Gitnexus Debugging AI skill free?

Yes. It is published on GitHub by JackReis 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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