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Systematic Debugging

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GanyuanRan
systematic-debugging

Use when encountering a bug, test failure, or unexpected behavior, before proposing fixes

Overview

PublisherGanyuanRan
RepositoryAegis
Skill namesystematic-debugging
Stars
1.2K
Forks
52
Bundled files
13
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.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GanyuanRan 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.
  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/GanyuanRan/Aegis.git /tmp/Aegis
mkdir -p .claude/skills
cp -r /tmp/Aegis/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.

Execute

Bug, failure, or unexpected behavior:

  1. Isolate — read error, reproduce, inspect the diff, and drill upward through diagnostic layers: L1 symptom → L2 logic → L3 system → L4 architecture → L5 cross-system contract → L6 platform → L7 spec gap. Layers are observation altitudes, not one causal chain; the causal shape at the stop altitude is classified explicitly before any root claim. Stop only when causal proof accounts for the recurrence generator or reaches a T-class boundary.
  2. Identify owner — compare working behavior, trace the bad value, locate the canonical owner, and treat duplicate owners as a finding.
  3. Decide before editing — Before fixing, run Patch-Shape Triage and Ripple Signal Triage when shared logic, contracts, fallbacks, adapters, producer/consumer seams, or source-of-truth boundaries are involved. Surface Change Necessity for any new source-code path or non-trivial source edit. Run Minimality Check for a new branch, fallback, adapter, owner, or compatibility path, and Pre-Edit Complexity Check for an overloaded owner or complexity growth. After Change Necessity selects code-change and before the first repair edit, own the TDD Route for the repair slice per test-driven-development (off default; strict on behavior/bugfix/shared/contract/persistence/permission/migration risk).
  4. Prove — test one hypothesis with the smallest reproduction or verification. A failing test first is required only by a recorded TDD Route: strict; with TDD Mode: off, do not require a failing test or RED/GREEN cycle. Three failed fixes means stop and question architecture.
  5. Repair and close — fix minimally at the canonical owner, verify in proportion to risk, review architecture, and close both repair and retirement tracks. If any symptom remains, stop and diagnose it separately.

Done: confidence ≥ B, causal status matches recurrence evidence or an external terminal, tracks explicit, no H signal, and required D evidence passes.

Core invariant

Find root cause and fix the bug class at its canonical owner. A minimal fix is not the smallest textual diff; it is the smallest sufficient owner-level repair.

Quick bug lane

For a low-risk, reproducible, single-owner bug with no patch-shape signal, keep the readback compact: Symptom, Reproduction, Root Cause, Change Necessity, Fix Boundary, and Verification. Skip the causal card only when the causal-proof owner's Quick Exit Proof passes. Quick bug lane must surface Change Necessity before source edits. One sentence may cover the user-visible need, no-change/non-code option, why code must change, minimum boundary, and an explicit decision token such as Decision: code-change. If shared logic, a contract, fallback, duplicate owner, consumer patch, or cross-module behavior appears, leave this lane.

Aegis Visibility names the evidence/owner/patch-shape/verification effect. Pass root cause, avoided misfix, boundary, evidence, complexity, and risk to verification-before-completion; no separate receipt.

Diagnose before repair

  1. Read the complete error/stack and record inputs, environment, versions, and success criteria.
  2. Reproduce consistently. If unstable, read feedback-loop-construction.md only when evidence shows intermittent or timing-dependent reproduction and build a bounded loop. Shrink the repro to load-bearing elements as the test input, never the fix scope: still drill upward; test at the correct seam.
  3. Inspect recent changes and compare a working example. Code is evidence; if authority, glossary, code, and tests disagree, compose establishing-project-context rather than silently redefining a term.
  4. Instrument component boundaries, then trace the bad value toward its source. Read root-cause-tracing.md only when the observed bad value is several calls or components downstream from its origin.
  5. State one hypothesis and falsify it with one-variable evidence. Do not stack speculative fixes. End each loop with Goal | DeeperCause | Evidence | Risk/Unknown | Decision.

Canonical-owner and patch-shape gate

Before editing, continue upward unless evidence proves the local site is the canonical owner when the candidate is any of these signals:

  • keyword, phrase, regex, negation-word list, or sample-text exception;
  • local guard, extra conditional, try/catch, early return, or one-off branch;
  • fallback, adapter, compatibility branch, prompt branch, or legacy path expansion;
  • consumer/caller/readiness/presentation-layer patch;
  • downstream logic re-parses raw text or re-infers action/state while typed intent, normalized state, contract, or another source-of-truth exists;
  • artifact/download/export/readback/cache patch without producer/owner proof.
text
PatchShape:
CanonicalOwner:
UpwardDrillSignal:
Decision: fix owner | continue investigation | escalate

A locally green test does not erase triage; a renamed carrier is not a new direction.

When a repair may reinterpret or retire existing semantics, responsibility, contract, or relationship, name the behavior to preserve, highest-risk counterexample, and material unknown. For each known explicit anchor or upstream/downstream reference, state its role and disposition: preserve, rebind to the canonical owner, retire with reason, or reject because of conflict. Leave unresolved relationships unknown; do not re-infer them downstream. Bind role before value and retire invalid responsibility, not evidenced carrier capability. This bounded reminder is not a behavior matrix, relationship graph, referential-integrity proof, or exhaustive discovery claim. It adds no artifact, TDD risk signal, or regression scope; the existing TDD route owner and configured/default mode still apply.

If the diagnosis crosses L3, a patch-shape signal fires, a user disputes the root claim, a prior fix leaves a symptom, compound/root topology is plausible, two or more anchored manifestations of one incident exist, reproduction conditions diverge across occurrences, or an upstream producer/config/default/contract/spec remains unexcluded, read root-cause-claim-contract.md before claiming a root cause. It is the sole owner of the Pre-Claim Gate, causal-closure/falsifier proof, layer-ceiling proof, and Causal Topology Gate.

Change Necessity

This decision is behavior-triggered, not prompt-triggered. It applies to any new source-code path. Before that path or a non-trivial source edit, expose the Change Necessity decision (no-change | docs/config-only | code-change | needs-clarification); field detail lives in advanced-debugging-governance.md.

Minimality and owner fit

For any proposed branch, fallback, adapter, compatibility path, or new owner, run Minimality Check (fields in advanced-debugging-governance.md) with verdict sufficient repair | local patch | needs first-principles review, and retire invalid responsibility: a local patch needs a retention reason and retirement trigger. For a new non-ordinary repair surface, run the Existence Check in docs/current/AEGIS_MINIMALITY_REFERENCE.md. If retirement involves old code, external compatibility, or persistent-state risk, compose anti-entropy-governance; it chooses the retirement path but never grants destructive authority.

Before editing an overloaded or mixed-purpose owner, complete Pre-Edit Complexity Check and Pre-Edit Owner-Fit Decision (templates in advanced-debugging-governance.md).

Use using-aegis/references/complexity-governance.md for pressure signals. Do not add new-responsibility in place by default. If the safer boundary changes the approved shape, update the plan/spec first.

Repair and proportional verification

Implement one owner fix; no bundled “while here” work. Under strict TDD, create the smallest failing test first. With TDD off, a reproduction is diagnostic evidence, not a RED gate or a prerequisite for production edits.

Verification must match the risk:

  • local single-owner repair: original reproduction plus focused regression;
  • shared/contract/cross-module repair: canonical owner plus affected consumers and compatibility boundary;
  • fallback/owner retirement: main-path, lingering-reference, negative, and boundary checks;
  • timing/concurrency repair: read condition-based-waiting.md only when evidence identifies polling, sleeps, or race timing as part of the cause;
  • invalid state crossing several trusted boundaries: read defense-in-depth.md only after the root repair is known and evidence shows a second independent validation boundary is required.

Read advanced-debugging-governance.md before another fix for failed/ persistent / divergent repair or three failures; for unclear/disputed stop / Layer Stop Card / intervention; or plausible compound root. Closeout triggers: repair-added patch-shape; multi-site/one-regression; remaining pattern/anomaly/duplicate/wrong-owner/downstream repair; uninspected same-symptom fix; open recurrence/unsupported root status; missing compound topology-specific member/anti-disguise proof; outside-repo authority; unmigrated published-contract break; undefined spec; missing permission/info. They route H/T/D; detail is not causal proof.

For non-trivial debugging with configured workspace support:

bash
python <aegis-workspace-helper> init --root <target-project-root>
python <aegis-workspace-helper> new-work --root <target-project-root> ...
python <aegis-workspace-helper> add-evidence --root <target-project-root> --work <YYYY-MM-DD-slug> ...
python <aegis-workspace-helper> check --root <target-project-root>

Failed attempts use <aegis-workspace-helper> add-attempt; add-evidence is terminal-only.

Fast bug fix or quick bug fix pressure does not skip this: if Ripple Signal Triage fires, record it before editing and verify the canonical owner plus affected downstream path. Records are advisory, not completion authority.

Closure

Always report:

  • Repair — cause, owner, smallest change, compatibility, verification.
  • Retirement — invalid responsibility status, carrier/capability disposition, retention reason/trigger, removal check.

Confirm the reproduction, same-pattern handling, authority, complexity, and retirement. Prefix debug logs (e.g. [DEBUG-a4f2]); confirm one-grep removal before close. Confidence: A = direct regression evidence; B = strong evidence with bounded unknowns; C = partial and not resolved.

Trace Digest may summarize audit evidence; never expose chain-of-thought or replace root-cause, rule-effect, and verification evidence.

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?

Use when encountering a bug, test failure, or unexpected behavior, before proposing fixes

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/GanyuanRan/Aegis/tree/main/skills/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 GanyuanRan 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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