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Debugging

Community
neuromechanist
debugging

Use this skill when the user asks to "debug", "find the root cause", "figure out why X fails", "investigate this bug", "this test is flaky", "the build broke", "production incident", "why is the output wrong", or when any observed behavior contradicts expected behavior. Covers the reproduce-isolate-prove-fix-verify loop, anti-shortcut gates, and numerical/scientific debugging.

Overview

Publisherneuromechanist
Repositoryresearch-skills
Skill namedebugging
Stars
51
Forks
9
Bundled files
1
LicenseBSD-3-Clause
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 neuromechanist on GitHub. Read the source before you install it.

Installation

Install the 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/neuromechanist/research-skills/tree/main/plugins/project/skills/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/neuromechanist/research-skills.git /tmp/research-skills
mkdir -p .claude/skills
cp -r /tmp/research-skills/plugins/project/skills/debugging .claude/skills/debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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

A disciplined root-cause loop. The goal is a stated causal mechanism with proof, then a fix verified by the exact check that first exposed the problem. Never fix by guessing, and never make a symptom disappear without knowing why.

The loop

  1. Reproduce. Run the failing thing yourself and capture the exact error, command, and environment. If you cannot reproduce, stop and gather the reporter's exact conditions; do not fix blind. Side observations found on the way get noted and set aside, not chased.
  2. Trace the intended path. Before reading logs, read the code/docs to state what SHOULD happen, step by step. You cannot recognize the broken step without knowing the intended sequence.
  3. Check live state. What is actually on disk, in the database, running as a process, deployed? A grep that finds no trace of a migration is not proof it never ran; completed one-off scripts are routinely deleted. Absence in code is not absence in reality.
  4. Find the smoking gun. Logs, CI runs, git history. Cheapest checks first: installed version vs latest, the artifact's own declared type or config, the library's supported list. Only conclude "unsupported" or "impossible" after checking the declared capability directly.
  5. Prove the mechanism. Confirm with a live probe, not log trust (example: the blob that returned 403 in the failing run returns 200 now, so it was a permission-timing issue, not missing data). A differential test beats an assumption: when two configurations should differ, run both and compare.
  6. Only now read source for the WHY, and state the cause in one sentence with the evidence that proves it, plus what changes AND what deliberately does not change because it was not the cause.
  7. Fix, minimally. Fix the upstream cause, not the symptom (a service that dies when the machine sleeps needs the sleep addressed, not a restart loop).
  8. Verify with the original failing check, unchanged. Then run the wider gates (tests, lint, typecheck). Add a regression test that would have caught this bug. If recovery of live state was part of the fix, its success doubles as diagnosis confirmation.

Isolation techniques (when the cause is entangled)

  • Single-variable toggles: flip one factor at a time (feature flag, config bit, environment) and record the result per state. Never attempt a compound fix while two candidate causes are still entangled.
  • Necessary-but-not-sufficient check: after any partial fix, re-run the original failing check. The first improving number is not proof of a complete fix; say "necessary but not sufficient" and keep going.
  • Ranked compounding causes: many incidents have several contributing causes (stale binary + lost race + shared timeout). Enumerate and rank them; do not stop at the first plausible one.
  • Confound check: before concluding "the fix did not work" or "the test is wrong", look for an independent confound (a second process racing the test, a hook rewriting the file). Re-run clean.
  • Instrumentation-first for weird results: a flat or extreme result that fails to discriminate across conditions that should matter is an instrumentation bug until a targeted, isolated reproduction says otherwise. Do not report it as a finding, and do not let it block unrelated work while you wait to reproduce it.
  • Doc-vs-code conflicts: when documentation (or an Architecture Decision Record) and code disagree, determine the actual source of truth empirically (test the code), then fix whichever artifact is wrong, making the fix more precise about where the invariant is enforced. Never silently pick a side.

Anti-shortcut gates (hard rules)

  • Never delete, skip, or loosen a failing test to make a suite pass. If a test is genuinely wrong, prove it (git history: did the tested thing move or disappear?) and say so explicitly.
  • Never add a broad catch, silent fallback, or retry loop to make a symptom disappear. That converts a loud bug into a silent one.
  • Never weaken a designated safety mechanism (rate limit, halt, permission gate) because it fired. When a guardrail fires during a bad outcome, the default hypothesis is that the new code is incompatible with a correct guardrail. Changing the guardrail requires separate, explicit user sign-off.
  • Never read or print secrets (.env contents, tokens) while debugging credential or environment issues; test credentials differentially instead (call the API with each and compare behavior).
  • Retry a flaky operation at most once under changed conditions. If it fails again under conditions you believe clean, stop retrying and surface a structural hypothesis instead.

Verification is layered

An automated check passing covers only what that check was built to detect. Before declaring a generated or visual artifact done, do one perceptual/manual pass for defect classes the validator cannot see (label collisions, off-canvas content, semantic errors). If the ideal verification is structurally unavailable, run the nearest safe non-mutating substitute and state exactly what it does and does not cover; never report "verified" against a silently downgraded check.

Numerical and scientific code

For parity work against a reference implementation, tolerance judgment, and trajectory comparison, read references/numerical-debugging.md before starting. Summary of its hard rules: compare from identical initialization, know the float noise floor before calling a residual a bug, reserve "bit-exact" for residuals at machine precision, and test whether the metric itself is well-posed before chasing an unmet target.

When to stop

  • Root cause stated in one sentence, with cited evidence.
  • Fix verified by the original failing check plus the standard gates.
  • Regression test added (or an explicit reason why not).
  • If the investigation dead-ends: document it (goal, issue, root cause if known, lesson, alternative) in the project's scratch/history notes and the issue tracker; a documented dead end is a valid outcome, a silent one is not.
  • If you are deep in a long session and the remaining fix is risky, prefer a validated, independently re-runnable handoff artifact (script plus ordered notes, verified to run) over a rushed multi-file edit.

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 Debugging AI skill do?

Use this skill when the user asks to "debug", "find the root cause", "figure out why X fails", "investigate this bug", "this test is flaky", "the build broke", "production incident", "why is the output wrong", or when any observed behavior contradicts expected behavior. Covers the reproduce-isolate-prove-fix-verify loop, anti-shortcut gates, and numerical/scientific debugging.

Why use Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/neuromechanist/research-skills/tree/main/plugins/project/skills/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 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 Debugging?

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

Is the Debugging AI skill free?

Yes. It is published on GitHub by neuromechanist under the BSD-3-Clause 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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