pre-mortem

name	pre-mortem
description	Imagine future bug post-mortems for the codebase. Identifies fragile code, implicit assumptions, and likely failure modes by writing realistic incident reports for bugs that haven't happened yet.
argument-hint	[file, directory, or description of what to focus on]
disable-model-invocation	true

Pre-Mortem: Future Bug Post-Mortems

You are now in pre-mortem mode. Your job is to read production code, identify areas of fragility and implicit assumptions, and then write realistic post-mortem reports for bugs that haven't happened yet — but plausibly could, given the kind of changes a future developer might reasonably make.

This is not a bug hunt. The code may be perfectly correct today. You're looking for places where the code is fragile against future edits: places where a developer who doesn't have full context could make a seemingly reasonable change that breaks something in a non-obvious way.

Scope

$ARGUMENTS

• If the user names specific files or directories, scope your analysis to those.
• If no argument is given, look at the project's source layout and use AskUserQuestion to agree on a starting scope. Pick a module or package with meaningful logic — don't try to cover everything at once.
• Focus on production code. Config files, migrations, and boilerplate are out of scope unless they contain logic that other code depends on.

Workflow

1. Read deeply. Read the files in scope carefully. Don't skim — you need to understand data flow, state management, implicit invariants, and the relationships between components. Read callers and callees, not just the file in isolation.

2. Identify fragility. Look for the patterns described in the catalogue below. For each one you find, ask: "What change would a reasonable developer make here that would break this?" If you can't imagine a plausible edit that causes a problem, move on — not everything is fragile.

3. Write post-mortems. For each fragility you identify, write a fictional post-mortem in the format described below. Write them as if the bug has already happened, in past tense, from the perspective of the team investigating the incident after the fact. Make the scenarios concrete and specific — name the functions, the variables, the values.

4. Produce the report. Write all post-mortems to a single file. Use AskUserQuestion to confirm the output path with the user, defaulting to PRE-MORTEM.md in the project root.

Use TaskCreate to track progress across files when there are more than a handful.

Fragility Catalogue

Look for these patterns when reading code. This is not exhaustive — use your judgement to spot anything that feels like it would surprise a future editor.

1. Implicit ordering dependencies

Code that must run in a specific order but doesn't enforce it. Setup methods that must be called before other methods. List processing that assumes elements arrive sorted. Initialization sequences where step 3 silently depends on step 1 having run.

Future edit: Someone reorders the calls, adds a new step between existing ones, or calls a method before the object is fully initialized.

2. Semantic coupling through shared mutable state

Two components that communicate through a shared object (a dict, a list, a module-level variable, an attribute on a passed-in object) rather than through explicit arguments and return values. The reader of component A might not realise that component B is reading or writing the same state.

Future edit: Someone modifies one component's use of the shared state without realising the other depends on it. Or someone adds caching/memoization that prevents the shared state from updating.

3. Stringly-typed contracts

Logic that depends on the exact value of strings — dict keys, status fields, format strings, column names, error messages. These create invisible contracts between producers and consumers that aren't enforced by any type checker or test.

Future edit: Someone renames a status string, adds a new enum variant that existing match/if-elif chains don't handle, or changes a dict key in one place but not another.

4. Assumptions baked into data transformations

A function that processes data assuming a particular shape, range, or distribution — e.g. assuming a list is non-empty, a value is positive, a string matches a pattern, or a column contains no nulls. These assumptions might be true today because of how the data is produced upstream, but nothing enforces them.

Future edit: Someone changes the upstream data source, adds a new code path that feeds different data into the function, or relaxes validation at the system boundary.

5. Coincidental correctness

Code that produces the right result for the wrong reason. A condition that happens to work because two variables are always equal today. A loop that doesn't handle the empty case but is never called with an empty input. An exception handler that catches too broadly but currently only encounters one exception type.

Future edit: The coincidence stops holding. The input space widens, a new exception type appears, or the previously-equal variables diverge.

6. Non-atomic compound operations

A sequence of operations that should be atomic but isn't — e.g. "check then act" patterns, multi-step state updates with no rollback, or file operations that assume no concurrent access. Includes anything where a failure or interruption between steps leaves the system in an inconsistent state.

Future edit: Someone adds concurrency, moves the code to a context where interruption is possible, or adds an early return between the steps.

7. Invisible invariants

Relationships between pieces of data that must be maintained but are enforced only by convention — e.g. "this list and that dict always have the same keys", "this counter equals len(that list)", "this field is non-None whenever that flag is True". No assertion, type, or test enforces the invariant.

Future edit: Someone updates one side of the invariant but not the other, especially when the two sides are in different functions or files.

8. Load-bearing defaults

Default values (function parameters, config settings, class attributes, environment variables) that the code subtly depends on. The default doesn't just provide convenience — the code would behave incorrectly or dangerously with a different value, and nothing documents this constraint.

Future edit: Someone changes the default to something that seems equally reasonable, or a caller starts passing an explicit value that nobody anticipated.

9. Implicit resource lifecycle

Resources (connections, file handles, locks, temporary files, background threads) that are created but whose cleanup depends on a particular control flow. No context manager or finalizer guarantees cleanup.

Future edit: Someone adds an early return, raises an exception, or refactors the function into smaller pieces, and the cleanup code is no longer reached.

10. Version-coupled assumptions

Code that depends on the behaviour of a specific version of a dependency, runtime, or protocol — e.g. relying on dict ordering (pre-3.7), assuming a library function's undocumented side effect, or depending on the exact format of an error message from a third-party library.

Future edit: The dependency is upgraded, the runtime version changes, or the API's undocumented behaviour shifts.

Post-Mortem Format

Write each post-mortem as a self-contained section. Use this structure:

### <Short incident title>

**Severity:** Critical | High | Medium | Low
**Component:** <file(s) and function(s) involved>
**Fragility type:** <category from the catalogue>

#### What happened

<2-4 sentences describing the bug as if it already occurred. What did users or
the team observe? Be specific — name the symptom.>

#### The change that caused it

<Describe the edit a future developer made. Make it sound reasonable — this
should be a change that would pass code review. Include a plausible motivation
for the change (new feature, refactoring, performance improvement, dependency
upgrade, etc.)>

#### Why it broke

<Explain the hidden assumption or fragility that the change violated. Point to
the specific lines or patterns in the current code that create this fragility.
Reference actual function names, variable names, and file paths.>

#### How it was caught

<How would this bug surface? Would tests catch it? Would it fail silently?
Would it corrupt data? Would it only manifest under specific conditions or
at scale? Be honest — if no test would catch it, say so.>

#### Hardening suggestions

<1-3 concrete, actionable suggestions for making the code more resilient
against this kind of change. These might include: adding assertions or
validation, introducing types that enforce invariants, writing a specific test,
adding a comment that explains the non-obvious constraint, refactoring to make
the dependency explicit. Don't suggest vague improvements — be specific enough
that someone could implement your suggestion directly.>

Calibration

Quality over quantity. Aim for 3-7 post-mortems per module, depending on its complexity. Each one should describe a genuinely plausible scenario — a bug that a competent developer could introduce during a reasonable edit.

Avoid:

• Current bugs. You're not looking for things that are broken today. If you find an actual bug, mention it to the user separately — don't write a fictional post-mortem about it.
• Adversarial scenarios. Don't imagine a developer deliberately sabotaging the code. The imagined changes should be things a well-intentioned developer would do.
• Extremely unlikely changes. "If someone rewrote the function in a completely different way, it might break" is not useful. The imagined change should be a small, local edit — a refactoring, a feature addition, a performance tweak.
• Generic advice. "This function has no tests" is an observation, not a post-mortem. Every post-mortem must describe a specific future change and a specific resulting failure.
• Excessive severity. Not everything is Critical. Use severity levels honestly. A bug that silently corrupts data is Critical. A bug that causes a clear error in an uncommon code path is Low.

Aim for:

• Scenarios where the cause and effect are non-obvious — the change is in one place and the breakage manifests somewhere else, or the breakage only appears under specific conditions.
• Fragilities that are endemic to the design, not surface-level issues. A missing null check is less interesting than an architectural assumption that permeates multiple files.
• Post-mortems that would make a reader say "oh, I wouldn't have thought of that" — not "well, obviously."

Output File Structure

# Pre-Mortem Report

**Scope:** <files/modules analysed>
**Date:** <today's date>

## Summary

<A short paragraph summarizing the overall fragility posture. How many
post-mortems? What are the dominant themes? Are there systemic patterns, or are
the fragilities mostly independent?>

## Post-Mortems

### 1. <title>
...

### 2. <title>
...

## Themes and Recommendations

<After all post-mortems, step back and identify cross-cutting themes. If
several post-mortems point to the same underlying architectural issue, call it
out here. Suggest structural changes that would address multiple fragilities at
once, not just point fixes.>

Critical Rules

• Read before writing. Never write post-mortems for code you haven't read thoroughly. You must understand how the code actually works, not just what it looks like.
• Be specific. Every post-mortem must reference actual functions, variables, and file paths in the current codebase. No hand-waving.
• Be plausible. The imagined changes must be things a reasonable developer might do. If you can't articulate a plausible motivation for the change, the scenario isn't realistic enough.
• Don't fix the code. Your job is to write the report, not to refactor the codebase. The hardening suggestions should describe what to do, but you shouldn't implement them unless the user asks.
• Separate actual bugs. If you discover a real, current bug while reading, flag it to the user immediately via text output — don't bury it in a fictional post-mortem.
• Ask when uncertain. If you're unsure whether a pattern is truly fragile or just unfamiliar to you, use AskUserQuestion to discuss it with the user before including it in the report.