Night Market Skill — ported from claude-night-market/imbue. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

The simplest change that fixes the problem is the safest change to merge. Adding code is easy. Removing the need for code is engineering.

Justify#

The Additive Bias Problem#

AI models are trained to be helpful, which creates a systematic bias toward adding code rather than fixing root causes:

AI Default BehaviorCorrect BehaviorAdd a workaroundFix the root causeModify test expectationsFix the implementationCreate a new helperUse an existing oneAdd error handlingPrevent the errorAdd a compatibility shimRemove the old codeWrap in try/catchFix the exception source

This skill audits changes for these patterns and requires explicit justification for each.

When to Use#

• After completing implementation work
• Before committing or creating PRs
• When reviewing your own changes for quality
• When scope-guard flags RED/YELLOW zone

Audit Protocol#

Step 1: Gather the Delta#

bash

# Determine base branch
base=$(git merge-base master HEAD 2>/dev/null \
  || git merge-base main HEAD 2>/dev/null)

# Get change statistics
git diff "$base" --stat
git diff "$base" --shortstat
git diff "$base" --diff-filter=A --name-only  # new files
git diff "$base" --diff-filter=M --name-only  # modified files
git diff "$base" --diff-filter=D --name-only  # deleted files

Step 2: Compute Additive Bias Score#

Score each dimension 0-3 (0 = clean, 3 = high bias):

SignalWeightHow to MeasureLine ratio2xadditions / max(deletions, 1)New files2xCount of --diff-filter=ATest logic changes3xTest assertion/expectation diffsNew abstractions1xNew classes, functions, modulesWorkaround patterns2xTry/catch, if/else guards added

Line Ratio Scoring:

RatioScoreInterpretation< 2:10Balanced change2:1 to 5:11Mildly additive5:1 to 10:12Additive bias likely> 10:13Strong additive bias

Aggregate Score:

text

bias_score = sum(signal_score * weight) / sum(weights)

AggregateZoneAction0.0 - 0.5GREENProceed0.5 - 1.5YELLOWJustify each signal1.5 - 2.5REDRethink approach2.5+STOPLikely wrong approach

Step 3: Iron Law Compliance Check#

The Iron Law states: tests drive implementation, not the other way around. Check for violations:

bash

# Find test files that were modified
git diff "$base" --name-only | rg "test_|_test\.|spec\." \
  || git diff "$base" --name-only | grep -E "test_|_test\.|spec\."

# For each modified test file, check what changed
git diff "$base" -- <test_file> | rg "^[-+].*assert|^[-+].*expect|^[-+].*should"

Violation patterns (test logic was tampered):

• Assertion values changed (expected output modified)
• Test cases removed or commented out
• @skip or @pytest.mark.skip added
• Error expectations weakened (broad exception types)
• Mock return values changed to match new behavior
• Test renamed to no longer describe original behavior

Each violation requires explicit justification:

"I changed this test assertion because the requirement changed, not because my implementation couldn't meet the original requirement."

If the requirement didn't change, the test should not change. Fix the implementation instead.

Step 4: Minimal Intervention Analysis#

For each changed file, answer:

1. Was this change necessary? Could the goal be achieved without touching this file?

2. Was this the minimal change? Could fewer lines achieve the same result?

3. Did this change add or remove complexity? New functions, classes, or control flow = added complexity that needs justification.

4. Is there a subtraction-first alternative? Could removing code fix the problem instead of adding code?

Step 4.5: Invariant Impact Analysis#

Changes can be minimal and still catastrophically wrong if they silently revise a load-bearing design decision. For each changed file, check whether it touches a design invariant:

What counts as an invariant:

• Architectural patterns (module boundaries, layer separation, data flow direction)
• Data structure choices (why a map vs list, why normalized vs denormalized)
• API contracts (public interfaces, protocol formats)
• Error handling strategies (fail-fast vs recovery)
• Concurrency models (single-threaded assumption, actor model, shared-nothing)

Detection heuristic:

bash

# Check for structural changes (new modules, moved
# boundaries, changed interfaces)
git diff "$base" --name-only | rg "(interface|abstract|base|core|types|schema|model)" \
  || git diff "$base" --name-only | grep -E "(interface|abstract|base|core|types|schema|model)"

# Check for pattern-breaking changes
git diff "$base" -U5 | rg "(TODO.*refactor|HACK|WORKAROUND|XXX)" \
  || git diff "$base" -U5 | grep -E "(TODO.*refactor|HACK|WORKAROUND|XXX)"

When an invariant conflict is detected:

Do NOT silently pick a resolution. Present the three options to the human:

OptionDescriptionWhen RightPreserveDon't add the feature; the invariant pays dividendsInvariant simplifies many things; feature is marginalLayerAdd feature inelegantly on topFeature is needed; invariant is still valuable; imperfection is acceptableReviseChange the invariant itselfGenuine new learning invalidates the original decision

Add to Justification Report:

markdown

### Invariant Impact: NONE / DETECTED

[If DETECTED:]
- **Invariant**: [name the design decision]
- **Conflict**: [what change clashes with it]
- **Option chosen**: Preserve / Layer / Revise
- **Justification**: [why this option, not the others]
- **Human reviewed**: YES / NO — if NO, flag as
  requiring review before merge

Compounding risk warning: Bad invariant decisions accumulate. If this branch has multiple invariant revisions, flag the entire branch for architectural review. Each silent invariant change multiplies the probability of an unsalvageable codebase.

Step 5: Generate Justification Report#

Output a structured report:

markdown

## Justification Report

**Branch**: feature/xyz
**Base**: master
**Delta**: +N/-M lines, X files changed

### Additive Bias Score: X.X (ZONE)

| Signal | Score | Detail |
|--------|-------|--------|
| Line ratio | N | +A/-D = R:1 |
| New files | N | [list] |
| Test changes | N | [list] |
| New abstractions | N | [list] |
| Workarounds | N | [list] |

### Iron Law Compliance: PASS/FAIL

[List any test logic modifications with justification]

### Change-by-Change Justification

#### file.py (+N/-M)
- **What**: [description]
- **Why**: [root cause this addresses]
- **Alternatives considered**: [what else could work]
- **Why this is minimal**: [why fewer changes won't work]

#### test_file.py (+N/-M)
- **What**: [description]
- **Justification**: [why test logic changed, if it did]
- **Iron Law status**: PASS/VIOLATION

### Risk Assessment

| Factor | Rating |
|--------|--------|
| Lines changed | LOW/MED/HIGH |
| Files touched | LOW/MED/HIGH |
| Test modifications | NONE/JUSTIFIED/VIOLATION |
| New abstractions | NONE/JUSTIFIED/UNNECESSARY |
| Overall merge risk | LOW/MED/HIGH |

### Recommendations

[List any changes that should be reconsidered,
simpler alternatives, or unnecessary additions]

Decision Weights#

When evaluating competing approaches, weight these factors:

FactorWeightRationaleFewer lines changedHIGHLess risk, easier reviewNo new filesHIGHNo new maintenance burdenNo test logic changesHIGHIron Law complianceRoot cause fixHIGHPrevents recurrenceRemoves codeBONUSReduces maintenance surfaceAdds abstractionPENALTYOnly justified at 3rd useAdds error handlingNEUTRALOnly at system boundaries

The Subtraction Test: Before accepting any change, ask: "Could I achieve this by removing code instead of adding it?" If yes, prefer the subtractive approach.

Integration with Proof of Work#

Justify extends proof-of-work with change-level accountability:

• proof-of-work: "Did it work?" (evidence)
• justify: "Was this the right way?" (reasoning)

Both are required before claiming work is complete. Run proof-of-work first, then justify.

Anti-Patterns to Flag#

1. Test Mutation#

Changing test expectations to match broken code. Fix: Revert the test change, fix the implementation.

2. Shotgun Addition#

Adding code in many files for a single-concern fix. Fix: Find the single point of change.

3. Defensive Overengineering#

Adding try/catch, null checks, or validation for scenarios that can't happen in practice. Fix: Trust internal code. Only validate at boundaries.

4. Premature Abstraction#

Creating a helper/utility/base class for one use case. Fix: Inline the code. Abstract at the 3rd use.

5. Compatibility Shim#

Adding backward-compatibility code instead of updating callers. Fix: Update callers directly. Delete dead paths.

6. Silent Invariant Revision#

Changing an architectural pattern, data structure choice, or API contract without acknowledging that a design invariant is being revised. Fix: Name the invariant. Present the 3 options (preserve, layer, revise) to a human. Do not make the judgment call yourself — models default to the "average" of training data, and wrong invariant decisions compound into unsalvageable codebases.

Scrutiny Questions (from leyline:additive-bias-defense)#

Before justifying any change, apply these questions. If the answer to questions 4 and 5 is not concrete evidence, the change is unjustified.

1. Priority alignment: Is this a deviation from the current priority?
2. Criticality: Is it critical to implement at this juncture?
3. Simplicity: Does a simpler or more elegant solution exist?
4. Evidence: What evidence proves this is needed (not assumed)?
5. Consequence: What breaks if we do not add this?

Burden of Proof Inversion#

The default stance is: this addition should not exist. The change must prove its necessity, not the reviewer must prove it unnecessary.

When generating the Justification Report (Step 5), add a Burden of Proof section:

ChangeScrutiny Q4 AnswerScrutiny Q5 AnswerVerdictfile.py[evidence][consequence]justified/needs_evidence/unjustified

Changes with unjustified verdict MUST be removed or reworked before the report passes.

The Wise Counsel#

Is what you are doing a deviation of your priority? Is it critical to implement at this juncture? Rely less on AI and initial lines of thinking. Challenge yourself to be better, to think of a more elegant implementation or a simpler solution.

Related Skills#

• imbue:karpathy-principles - "Surgical Changes" and "Goal-Driven Execution" principles invoke this audit from a higher-level synthesis
• leyline:additive-bias-defense - the contract this audit enforces in detail
• imbue:proof-of-work - the validation layer this audit complements (proof-of-work asks "did it work?", justify asks "did it need to exist?")
• See docs/quality-gates.md#skill-level-quality-gate-composition for the full gate-skill federation graph