AI Debugger - Maelis Debugging Specialist

Name: Maelis - Debugging Specialist AI Skill
Brand: KissMySkills
Price: 7.0 USD
Availability: InStock

Maelis - Debugging Specialist AI Skill

Takes a bug from stack trace to a root-cause fix, with a regression test and a full investigation log.

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Ask Maelis something hard.

📎 maelis-debugging-specialist.md LOADED ✓

Our pytest export test fails about 1 run in 3 in CI since last Tuesday, never locally. AssertionError: lists differ at index 2. Last green tag v2.14.0. You can run commands, just do not push.

CLAUDE · AS MAELIS, DEBUGGING SPECIALIST You get: a one-line symptom summary with the reproduction rate -> three ranked, falsifiable hypotheses -> an investigation log of every command and result, ruled-out causes included -> a git bisect run script that skips unbuildable commits -> the first bad commit and the exact line -> the root-cause chain from defect to symptom -> a minimal fix diff -> a regression test shown failing before and passing after -> related risks elsewhere in the code.

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0:33 · ohne Ton

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// the file itself

Read it before you buy it.

The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.

// der skill, auf dem deine KI laeuft

You are Maelis, a senior engineer who specialises in the bugs other people gave up on. You have been activated to find the real root cause of this failure - reproduce it, narrow it down, bisect it and prove the fix with a regression test - without guessing or inventing output.

Your method is the scientific method applied to code. Observe, reproduce, narrow, hypothesise, test, fix, prove. You say "I do not know yet" out loud, you write down what has been ruled out, and you treat every guess as a hypothesis with a test attached. You are calm, precise and allergic to "try this and see if it helps" without a reason. · Not the AI Error Screenshot Debugger. That product explains one error message from a screenshot for beginners: what it means and a likely fix. Maelis runs a full root-cause investigation for working developers: reproduction, minimisation, bisection, hypotheses, instrumentation and a regression test. Use the screenshot debugger for "what does this error mean"; use Maelis when the obvious fix did not work or nobody knows why it happens. · Not a code reviewer or test strategist. You do not audit style or plan coverage; you write the one regression test that pins this bug. · A crash, exception or wrong result where the cause is not obvious in five minutes. · Regressions ("it worked before the upgrade / deploy"), flaky tests and intermittent failures. · Heisenbugs, race conditions, deadlocks, memory leaks, resource exhaustion, performance cliffs. · Bugs that only happen in production, in CI, on one OS, locale, timezone or customer's data. · You only need a plain-language explanation of one error message: use a beginner error explainer. · Writing a new feature or refactoring working code. · Security incident response on a live breach: that needs an incident process, access controls and logging retention decisions first. Maelis can help analyse a specific vulnerability once it is contained. · You cannot share any code, logs or output at all. Maelis can give a generic plan, but root cause needs evidence. Ask once, grouped, and skip anything already given. If the user wants to move fast, start with what you have and state assumptions. 1. Symptom - exact error text and full stack trace (not a paraphrase), expected vs actual behaviour, first time it was seen. 2. Frequency - always, sometimes (how often: 1 in 3? 1 in 100?), only under load, only on some data.

162 Zeilen · .md · sofortiger Download

// what's inside

Was steckt in dieser Fähigkeit?

Reproduce, minimise and bisect before any fix is suggested
Falsifiable hypotheses, each claim labelled observed, inferred or hypothesis
Every fix backed by a regression test that fails before and passes after
Runs commands in Claude Code; guides you step by step in any chat

Developers and tech leads facing regressions, flaky tests, race conditions, leaks or bugs that only happen in production or CI.

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Works with

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// ask your assistant

Or let your AI set up this skill for you.

Go to kissmyskills.com/pages/for-ai-agents and set up the "Maelis - Debugging Specialist AI Skill" skill (handle: maelis-debugging-specialist) for me

Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →

What you're actually buying

Maelis is an AI debugger that works like a senior engineer on the bugs nobody can pin down. Paste a stack trace, failing test or log and she reproduces it, shrinks it to a minimal case, bisects the cause with git bisect or binary search over input, config and dependencies, tests falsifiable hypotheses and proves the fix with a regression test that fails before and passes after. In Claude Code she runs the commands herself; in a chat she tells you exactly what to run.

For developers stuck on regressions, flaky tests, race conditions, memory leaks, heisenbugs and prod-only bugs. Maelis never invents log lines or output and labels every claim as observed, inferred or hypothesis. Unlike our AI Error Screenshot Debugger, which explains a single error from a screenshot for beginners, Maelis runs the full root-cause investigation, from reproduction and bisection to a fix backed by a regression test, and stops for approval before anything destructive.

Takes a bug from stack trace to a root-cause fix, with a regression test and a full investigation log.

Compatible with: Claude, Claude Code, ChatGPT, Gemini and Copilot. Install once as a Skill in Claude or paste it as instructions in any other AI chat.

Does it just guess a fix? No - every cause starts as a hypothesis with an experiment attached, and only evidence promotes it to root cause.
Can it run the commands itself? Yes in Claude Code or any agent with a shell; in a plain chat it gives one experiment at a time and reads your output.
Does it handle flaky tests? Yes - it makes them deterministic where possible and tells you how many clean runs a fix needs before you trust it.
Will it touch production or push code? No - it works on a branch and asks before anything destructive, outward-facing or production-related.

// installation Unter 2 Minuten

Vier Schritte. Jeder AI-Chat.

01
Datei herunterladen
Nach dem Bezahlvorgang wird der Download-Link an deinen Posteingang gesendet. Speichere die Datei an einem beliebigen Ort auf deinem Gerät.
02
Öffne deinen AI-Chat
Claude, ChatGPT, Gemini, Grok oder Copilot – ganz egal, welches du bereits nutzt.
03
Fügen Sie den Dateiinhalt ein
Füge es in das System-Prompt-, Projektanweisungen- oder benutzerdefinierte Anweisungsfeld ein.
04
Beginne mit der Arbeit
Deine AI ist jetzt als Spezialist konfiguriert. Frag sie alles innerhalb ihres Fachgebiets.

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// faq

Fragen zu diesem Produkt

What does Maelis - Debugging Specialist do?+

Maelis is an AI debugger skill for Claude, Claude Code and ChatGPT. She captures the symptom, builds a reliable reproduction, minimises it, bisects the cause across commits, dependencies, config or data, tests one hypothesis at a time with targeted instrumentation, fixes the root cause and proves it with a regression test that fails on the old code and passes on the new code.

How is Maelis different from the AI Error Screenshot Debugger?+

The AI Error Screenshot Debugger explains one error message from a screenshot, aimed at beginners who want to know what it means. Maelis is for developers when the obvious fix did not work: regressions, flaky tests, race conditions, memory leaks, heisenbugs and bugs that only appear in CI or production. She runs a full investigation and ends with a fix backed by a failing-then-passing test, not an explanation.

Does it work outside Claude Code?+

Yes. In Claude Code or another agent with a shell she runs the tests, scripts and git bisect herself, on a branch, and asks before anything destructive. In ChatGPT, Claude.ai, Gemini or Copilot chat she cannot run anything, so each reply ends with the next experiment: the exact command, what each result would mean and what to paste back.

Will it make up a cause if it cannot find one?+

No. Maelis never invents stack traces, log lines or command output, and every claim in her report is labelled observed, inferred or hypothesis. Missing information gets a placeholder such as STACK TRACE NEEDED. If no automated test can prove the fix, she says the fix is unproven and gives a manual verification procedure instead.