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Systematic Debugging

Community
nahid-sparktales
systematic-debugging

Find the cause of a failure by evidence — reproduce it, read what the system actually did, form hypotheses that predict something you have not looked at yet, bisect to the smallest failing case, fix where all callers route through, then re-run the original reproduction. Use when something is broken and the cause is not yet known, when a previous fix did not hold, or when an edit is about to be made because the code "looks wrong". Not for an outage still burning (mitigate first), not for a cause already established that only needs implementing, and not a licence to skip reading the stack trace.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill namesystematic-debugging
Stars
58
Forks
5
Bundled files
1
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.

  • 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 nahid-sparktales 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/nahid-sparktales/agent-dispatcher/tree/main/skills/quality/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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/quality/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

The symptom is where the failure surfaced, not where it came from. Editing the line the report names is how a bug gets moved rather than fixed.

When this fires

  • A defect, crash, wrong output, corrupted state or failing test whose cause is not yet known.
  • A second attempt at a fix, after the first one did not hold or held for reasons unknown.
  • Before any edit whose only justification is that the code looks suspicious.

It does not fire during a live outage — mitigation comes first and the explanation comes after — and it does not fire when the mechanism is already established and only the patch remains.

Procedure

  1. State the failure precisely. Expected versus actual, the exact input, the version and environment, and when it started. "Login is broken" is not a failure statement. "POST /login returns 500 on staging for accounts created before the column was added" is one.
  2. Reproduce it before touching anything. Record the exact command or step list and its output. If it will not reproduce, that is the finding — report how you tried and how often it fires. Do not proceed to a fix whose effect you have no way to observe.
  3. Capture evidence before theorising. The full stack trace, not the last frame. The log lines either side of the failure. The failing input. What changed recently in the code and in its dependencies. Read all of it. A large share of bugs are already named in evidence nobody read.
  4. Bound it. Which inputs fail and which succeed, which callers, which versions, which environments. A failure confined to one input class is already half diagnosed.
  5. Write hypotheses down, ranked. Each one must predict something you have not yet looked at — "if it is the stale cache, this key holds the old value" — and then you go and look. A hypothesis that only explains what you have already seen cannot be tested.
  6. Test one at a time, cheapest first. Change one variable per experiment. A log line, a breakpoint or a query beats a speculative edit: reading adds no new variables, editing does.
  7. Isolate to the smallest failing case. Bisect whichever axis you can halve — the history, the input, the configuration, the code path. Stop when removing one more thing makes it pass.
  8. Name the causal mechanism in one sentence before you edit. If the best you have is "something about async timing", you are not ready to fix; you are ready to keep isolating.
  9. Fix the cause where all callers route through it. Grep every caller of the function you are about to change. Patching only the path the ticket named leaves the siblings broken; one guard in the shared function is both the smaller diff and the actual fix.
  10. Prove it. Re-run the reproduction from step 2. Add a regression test that fails on the old code (regression-testing). Run the surrounding suite to see the fix broke nothing adjacent.
  11. Remove your instrumentation — debug prints, extra logging, loosened timeouts. Anything worth keeping is kept deliberately and said out loud, not left in the diff by accident.

Anti-patterns — the random-patch loop

The loop this skill exists to break: change something plausible, see whether the symptom moves, repeat. Every round adds a variable, and the run that finally passes is indistinguishable from coincidence.

  • Fix by disappearance. The symptom stopped after five edits, so one of them worked. You do not know which, or whether it merely became less frequent.
  • A delay is not a fix. A sleep or a retry makes a race rarer. Report it as a mitigation with the race still unexplained, never as a root cause.
  • Suppression is not a fix. A broad catch, a disabled assertion, a loosened check — the failure is now invisible rather than absent.
  • Anchoring. The first plausible story gets all the evidence read into it. Ask what the evidence would look like if that story were wrong, then check for that.
  • Rewriting the region. A rewrite that happens to pass has not been diagnosed; it has replaced a known bug with unknown ones.

Checklist

  • Reproduction captured as an exact command or step list, and run before any edit
  • Full stack trace and surrounding logs read
  • Hypotheses written down, each predicting something unobserved
  • Smallest failing case in hand
  • Causal mechanism stated in one sentence
  • Every caller of the changed code checked, not only the reported path
  • Original reproduction re-run after the fix
  • Regression test confirmed to fail against the old code
  • Instrumentation removed, or deliberately kept and said so

Failure handling

  • Will not reproduce — report it as not reproduced, with the conditions tried. If a low-risk speculative fix ships anyway, label it unverified in plain words. An intermittent failure announced as fixed is worse than one announced as intermittent.
  • Reproduction is slow or manual — build a cheap one first, a script or a failing test. You are going to run it dozens of times.
  • No logs, no traces, nothing to read — adding instrumentation is a step, not a fix. Record the observability gap as a finding in its own right.
  • Every hypothesis falls — widen the frame to what you assumed and never checked: the environment, a dependency version, the data, the clock, another process, the build itself.
  • Testing it needs something destructive or outward-facing — deleting data, writing to a shared environment, calling a live third party, deploying. Stop and ask. Reproduce against a local or throwaway copy instead.

Evidence to report

The reproduction command and its output before the fix; the evidence that ruled each hypothesis in or out; the causal mechanism in one sentence; the diff; the regression test with proof it fails without the fix; and the reproduction re-run and now passing. Name what remains untested and any hypothesis you could not eliminate. A fix that was written and a fix that was proven are different claims — "fixed" without the re-run reproduction is the first one wearing the second one's clothes.

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 Systematic Debugging AI skill do?

Find the cause of a failure by evidence — reproduce it, read what the system actually did, form hypotheses that predict something you have not looked at yet, bisect to the smallest failing case, fix where all callers route through, then re-run the original reproduction. Use when something is broken and the cause is not yet known, when a previous fix did not hold, or when an edit is about to be made because the code "looks wrong". Not for an outage still burning (mitigate first), not for a cause already established that only needs implementing, and not a licence to skip reading the stack trace.

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/nahid-sparktales/agent-dispatcher/tree/main/skills/quality/systematic-debugging. 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 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 nahid-sparktales 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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