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Systematic Debugging

OrganizationPopular
vstorm-co
systematic-debugging

Systematic approach to diagnosing and fixing errors

Overview

Publishervstorm-co
Repositorypydantic-deepagents
Skill namesystematic-debugging
Stars
1.1K
Forks
134
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 vstorm-co on GitHub. Read the source before you install it.

Installation

Install the Systematic 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/vstorm-co/pydantic-deepagents/tree/main/apps/cli/skills/systematic-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/vstorm-co/pydantic-deepagents.git /tmp/pydantic-deepagents
mkdir -p .claude/skills
cp -r /tmp/pydantic-deepagents/apps/cli/skills/systematic-debugging .claude/skills/systematic-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Systematic 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 Systematic 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 Systematic 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.

Systematic Debugging

A structured approach to finding and fixing bugs.

The Debugging Loop

1. REPRODUCE → 2. ISOLATE → 3. DIAGNOSE → 4. FIX → 5. VERIFY

Never skip steps. Never guess-and-check repeatedly.

Step 1: Reproduce

  • Run the exact command that fails
  • Capture FULL output (stdout AND stderr)
  • Note: exit code, error message, stack trace
  • If intermittent, identify what changes between runs

Step 2: Isolate

  • What's the MINIMAL input that triggers the error?
  • Which specific line/function fails? (read the traceback bottom-up)
  • Is it a compile error, runtime error, or wrong output?
  • Does it fail on all inputs or specific ones?

Step 3: Diagnose

Read the error message carefully

Error typeWhere to look
Compile errorThe FIRST error (later ones are often cascading)
SegfaultLast function in the stack trace, check array bounds and null pointers
Python tracebackThe innermost frame (bottom), but also check the middle for context
Wrong outputDiff expected vs actual: diff <(expected) <(actual)
Timeout/hangIs it an infinite loop? Deadlock? I/O bound? Add a timer or counter

Add minimal instrumentation

  • C: fprintf(stderr, "reached checkpoint %d\n", __LINE__);
  • Python: print(f"DEBUG: {var=}", file=sys.stderr)
  • Check intermediate values, not just final output
  • Remove debug prints after fixing

Step 4: Fix

  • Change ONE thing at a time
  • If the same approach fails 3 times → completely different strategy
  • Don't add workarounds — fix the root cause
  • Common root causes:
    • Off-by-one errors (loop bounds, array indexing)
    • Type mismatches (int vs float, signed vs unsigned)
    • Encoding issues (UTF-8 vs bytes)
    • Path errors (relative vs absolute, missing trailing slash)
    • Race conditions (file not written yet, process not started)

Step 5: Verify

  • Run the same command that failed before
  • Test with multiple inputs, not just the one that was failing
  • Check edge cases: empty input, single element, very large input
  • Run any existing test suite

Common Failure Patterns

"It compiles but gives wrong output"

  1. Print all intermediate values
  2. Compare with a known-correct reference implementation
  3. Check: integer overflow, floating point precision, endianness

"It works on small input but times out on large"

  1. Check algorithm complexity — O(n²) on 1M items = timeout
  2. Profile: which loop/function takes the most time?
  3. Restructure: hash maps, sorting, streaming

"It works locally but fails in the test"

  1. Check: absolute vs relative paths
  2. Check: different working directory
  3. Check: different input format than expected
  4. Read the test script to understand what it actually checks

Frequently asked questions

What does the Systematic Debugging AI skill do?

Systematic approach to diagnosing and fixing errors

Why use Systematic Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vstorm-co/pydantic-deepagents/tree/main/apps/cli/skills/systematic-debugging. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Systematic 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 Systematic Debugging?

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

Is the Systematic Debugging AI skill free?

Yes. It is published on GitHub by vstorm-co 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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