Skill: cc-data-organization

STOP - Priority 1: Never Skip

Item	Why Critical
No magic numbers in business logic	Source of silent bugs
Currency uses integer cents, never float	Financial bugs are lawsuits
No float == comparisons	Non-deterministic failures
Variables initialized before use	Undefined behavior
Boolean naming is unambiguous	Logic inversion bugs

Skipping Priority 1 items is NEVER acceptable. They represent latent defects that will manifest later.

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Modes

CHECKER

Purpose: Execute data organization checklists against code Triggers:

• "review my variable declarations"
• "check for magic numbers"
• "review data type usage"
• "check my variable names" Non-Triggers:
• "what type should I use for X" -> APPLIER
• "how should I name this variable" -> APPLIER
• "fix these magic numbers" -> TRANSFORMER Checklist: See checklists.md Metrics: See hard-data.md for Span/Live Time measures (goal: minimize both) Output Format: | Item | Status | Evidence | Location | |------|--------|----------|----------| Severity:
• VIOLATION: Fails checklist item
• WARNING: Partial compliance
• PASS: Meets requirement

APPLIER

Purpose: Guide data type selection, variable naming, and structure design Triggers:

• "what data type should I use for..."
• "how should I name this variable"
• "best practice for enums/constants"
• "how should I organize this data" Non-Triggers:
• "review my types" -> CHECKER
• "fix this" -> TRANSFORMER
• "audit my code" -> CHECKER Produces: Type recommendations, naming conventions, enum patterns, constant definitions, structure designs Constraints:
• [p.308] Eliminate semantic literals - Replace business values (

  86400

  ,

  12

  ,

  0.07

  ) with named constants. Loop bounds

  0

  ,

  1

  and array indices are typically fine.
• [p.295] For currency: integer cents or BCD, never float
• [p.306] Enums (language-dependent):
  • C/C++: Reserve 0 for invalid, define First/Last bounds
  • TypeScript string enums: No zero-reservation needed (no uninitialized risk)
  • Rust/Kotlin: Leverage exhaustive matching instead of bounds checks
• [p.259] Minimize scope: Declare variables in innermost block where all usages occur. Balance with testability—sometimes slightly wider scope enables testing.
• [p.263] Names describe the entity clearly: Reader should understand purpose without searching for definition. Examples:

  d

  (bad) →

  data

  (vague) →

  userData

  (better) →

  validatedUserSubmission

  (good for complex entity)
• [p.279] Problem Orientation: names refer to problem domain (employeeData, printerReady), not computing (inputRec, bitFlag)
• [p.263] Name length heuristic: 2-4 words, long enough to describe purpose, short enough to scan. Research shows 10-16 chars minimizes debugging effort [Gorla et al. 1990], but this is guidance, not a hard rule.

TRANSFORMER

Purpose: Fix data organization violations Triggers:

• CHECKER findings with VIOLATION status
• "replace magic numbers with constants"
• "fix float comparison"
• "refactor these globals" Non-Triggers:
• Large refactorings beyond data organization -> cc-refactoring-guidance
• Control flow restructuring -> cc-control-flow-quality Input -> Output:
• Magic

  86400

  ->

  SECONDS_PER_DAY = 86400

• if (a == b)

  floats ->

  if (Math.abs(a-b) < EPSILON)

• true, false, true

  params -> enum values
• Unstructured variables -> grouped structure
• Direct global access -> access routines Preserves: Behavior, unrelated code Verification: Re-run CHECKER; VIOLATION count = 0

Rationalization Counters

Excuse	Reality
"Everyone knows what 12 means"	Named constants aid maintenance [Glass 1991]
"Floats are close enough for =="	0.1 added 10 times rarely equals 1.0
"Magic numbers are faster to type"	Debugging hard-coded literals takes far longer
"I don't need custom types"	One typedef change vs hundreds of declarations
"Short names are faster to type"	Code read far more than written; favor read-time convenience
"Global variables are more convenient"	Convenience writing trades against difficulty reading, debugging, modifying

Sunk Cost Counters

For resisting changes to "working" code:

Excuse	Reality
"It works, why change it?"	Violations are latent defects; "works" means "hasn't failed yet"
"I already invested time in this"	Time invested in bad code is lost regardless; fix now or pay more later
"Refactoring will break things"	Violations already broken; you just haven't discovered how yet
"Currency has always used floats here"	Every penny calculation is a potential lawsuit
"We've had no bugs from these magic numbers"	You've had bugs—you attributed them to other causes
"The code passed review before"	Past reviews missed issues; evidence now shows violations

Success-Bias Warning

Past success does NOT predict future safety.

Violations that "worked for years" fail when:

• Edge cases finally occur (currency rounding in new scenarios)
• Scale changes (global variable contention under load)
• Maintenance happens (magic numbers misunderstood by new developers)
• Requirements shift (hard-coded values need changing)

Every checklist item applies regardless of past success. "Worked until it didn't" examples fill bug databases.

Modern Data Types Coverage

Beyond Code Complete's C-era focus:

Concurrent Access

When data may be accessed from multiple threads/async contexts:

• Identify shared state - Mark variables accessed across thread boundaries
• Access routines are mandatory - Never expose shared data directly
• Consider immutability - Immutable data eliminates race conditions by design
• Document thread safety - Comment whether type/routine is thread-safe
• Violations: Data races, torn reads, lost updates

Nullable/Optional Types

Modern languages use

Option<T>

,

Maybe

,

T?

instead of null pointers:

• Prefer non-nullable by default - Make nullability explicit and intentional
• Handle all cases - Exhaustive matching on Option/Maybe types
• Avoid null as "not found" - Use Option types or result types instead
• Document null semantics - When null is valid, document what it means
• C-style pointer guidance still applies to unsafe code

Temporal Data

Dates and times are a common bug source:

• Store timestamps in UTC - Convert to local only for display
• Use timezone-aware types - Never use naive datetime for user-facing data
• Be explicit about precision - Seconds, milliseconds, nanoseconds?
• Name with time unit -

  timeoutMs

  ,

  durationSeconds

  , not just

  timeout

• Avoid magic time values -

  86400

  →

  SECONDS_PER_DAY

Security-Sensitive Data

Secrets, tokens, API keys require special handling:

• Clear from memory after use - Don't leave secrets in variables longer than needed
• Never log sensitive data - Redact in all log statements
• Use dedicated types -

  SecureString

  ,

  SensitiveData

  wrappers
• Limit scope aggressively - Shortest possible lifetime

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Chain

After	Next
Data organization verified	cc-control-flow-quality (CHECKER)