ranbot-ai/bugs-are-annoying

Bugs Are Annoying

An adversarial QA pass for any codebase, in any language. AI IDEs are optimized to produce code that looks finished — they are not optimized to produce code that is correct. This skill exists to close that gap by actively trying to break the code instead of confirming it works.

Core Mindset

Treat all code as guilty until proven innocent. The default question when reading a builder agent's output is not "does this look right?" — it's "how would this break, and what did the author not think of?"

This is an adversarial pass, not a confirmatory one. Do not skim and approve. Do not skip a category because it "seems fine." Every category in the taxonomy below must be actively checked against the actual code, not assumed clean.

When To Use

Trigger on: "find bugs," "audit this code/codebase," "run bug hunter," "check for errors," "find flaws," "review this for bugs," "is this code solid," or any request for a deep correctness pass rather than a style/readability review.

Process — Run These Phases In Order

Do not skip phases or collapse them into a single skim. Each phase catches things the others miss.

0. Determine scope — If the user named a specific file or folder, scope to that. Otherwise, ask before starting: confirm whether to audit the whole codebase, just files changed vs. the main branch (git diff), or a specific area. Never silently guess the scope on a codebase of unknown size — an unscoped "exhaustive" pass on a large repo can blow context mid-audit. Within scope, always exclude generated and dependency directories (node_modules, vendor, dist, build, .git) and minified/bundled files — this isn't the user's authored code and auditing it wastes the pass. Lockfiles are excluded by default, but must be inspected when checking for Dependency Issues.
1. Map the codebase — Identify entry points, the overall data flow, and what calls what before hunting for anything. You can't find a cross-file bug without first knowing the file relationships.
2. Static line-by-line pass — Read every relevant/changed file fully, not a skim. Check each line against the taxonomy below.
3. Trace critical data paths — Follow data from input to output across file/function boundaries. Most real bugs live at the seams between functions and files, not inside a single function.
4. Adversarial simulation — Mentally execute the code against hostile/edge inputs: null, undefined, empty string, empty array, zero, negative numbers, max-length input, duplicate calls, concurrent calls, malformed input, missing fields.
5. Cross-reference pass — When a bug is found, actively check if the same mistake was repeated elsewhere. AI IDEs frequently copy-paste the same flawed pattern into multiple files.
6. Severity triage — Classify every finding using the definitions below. Do not invent new severity labels.
7. Write/update bugs.md — Use the exact format below. This is the only output of a hunt — do not also narrate a long summary in chat; point the user to the file.

Bug Taxonomy

Language-agnostic. Check every category — these are patterns, not syntax, so they apply regardless of stack.

• Logic errors — off-by-one errors, inverted conditionals, wrong operator precedence, incorrect boolean logic
• Null/type safety — unhandled null/undefined, unsafe casts, missing optional-chaining, wrong assumed type
• Edge cases — empty input, zero, negative numbers, single-item vs multi-item collections, first/last iteration of a loop
• Error handling — swallowed exceptions, missing try/catch around fallible calls, errors caught but not logged or surfaced, wrong error propagated up the stack
• Concurrency/async — race conditions, unawaited promises, stale closures, state updated after a component/process has already torn down
• Security — injection points, hardcoded secrets/keys, auth or permission bypass, unsafe deserialization
• Resource leaks — unclosed file handles/streams/connections, listeners or subscriptions never removed
• Cross-file consistency — a function/type/field changed in one file but call sites elsewhere not updated (the single most common AI-IDE failure mode, since builder agents tend to edit one file at a time)
• API/contract mismatches — caller and callee disagree on a field name, type, or required parameter
• State management — mutation of state that should be immutable, derived state that goes stale, double-updates
• Dead/unreachable code — leftovers from an earlier AI attempt that never got cleaned up, code paths that can never execute
• Performance — N+1 queries, avoidable O(n²) where O(n) was available, unnecessary re-computation or re-renders
• Dependency issues — deprecated or vulnerable package versions, conflicting version requirements, use of a deprecated API that still works today but is slated for removal
• Documentation/comment mismatches — a comment or docstring that no longer matches