name: cc-data-organization
description: "Audit and fix data organization: variable declarations, data types, magic numbers, naming conventions, and global data. Three modes: CHECKER (92-item checklist -> status table), APPLIER (type selection and naming guidance), TRANSFORMER (fix violations). Cover modern types: concurrent/shared state, nullable/optional, temporal/timezone, security-sensitive. Use when reviewing code for data organization issues, choosing data types, or fixing magic numbers. Triggers on: review variables, data types, magic numbers, naming, global data, check types, fix floats, constants."

Skill: cc-data-organization

STOP - Priority 1: Never Skip

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

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

Sunk Cost Counters

For resisting changes to "working" code:

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

Chain