Skill: cc-data-organization

Skill: cc-data-organization

STOP - Priority 1: Never Skip

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

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

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Chain