naming-analyzer

Overview of naming-analyzer skill

The naming-analyzer skill is a focused code review helper for improving identifier quality: variables, functions, classes, files, database fields, and API names. It is best for developers, reviewers, and maintainers who already have code and want sharper, more consistent naming without manually applying style rules item by item.

What naming-analyzer actually helps you do

The real job-to-be-done is not “generate names” in isolation. naming-analyzer helps you review existing code, spot unclear or misleading identifiers, and produce better alternatives that fit the language, framework, and local naming patterns already in use.

Best-fit users and projects

This skill is most useful when you are:

• reviewing pull requests for readability
• cleaning up legacy code with inconsistent names
• standardizing a mixed codebase
• preparing a refactor where naming debt is slowing comprehension
• enforcing conventions in JavaScript/TypeScript or Python code

It is especially relevant as a naming-analyzer for Code Review workflow because it narrows attention to naming quality instead of giving broad, unfocused feedback.

What makes this skill different from a generic prompt

A normal “suggest better names” prompt often returns opinionated but shallow replacements. naming-analyzer is structured around a repeatable checklist:

• analyze existing identifiers across multiple code surfaces
• flag vague, inconsistent, misleading, or convention-breaking names
• check language-specific naming conventions
• explain why a suggested name is better

That structure matters when you want review output you can trust, not just creative renaming.

What it covers well

Based on the skill instructions, naming-analyzer looks at:

• variables and constants
• functions and methods
• classes, interfaces, and types
• files and directories
• database tables and columns
• API endpoints

It also checks for issues such as unclear abbreviations, single-letter names outside obvious loop contexts, naming that mismatches behavior, and boolean prefixes like is, has, can, or should.

Important limitations before you install

This skill is lightweight and instruction-driven. It does not ship with parsers, repository-specific rules, or automation scripts in the skill folder. That makes naming-analyzer install easy, but it also means output quality depends heavily on the code context you provide and how clearly you define the rename scope.

If you need guaranteed safe bulk renames or AST-backed refactors, this skill should complement your IDE and linters, not replace them.

How to Use naming-analyzer skill

naming-analyzer install steps

Install from the toolkit repository:

npx skills add softaworks/agent-toolkit --skill naming-analyzer

If your environment uses a different skill manager flow, add the skill from:

https://github.com/softaworks/agent-toolkit/tree/main/skills/naming-analyzer

What to read first in the repository

You do not need a long repository tour. Start here:

1. skills/naming-analyzer/SKILL.md
2. skills/naming-analyzer/README.md

SKILL.md gives the operational checklist. README.md is useful for trigger phrases, intended use cases, and examples of when the skill should be invoked.

What input the naming-analyzer skill needs

naming-analyzer usage is strongest when you provide more than raw identifiers. Include:

• the code snippet or file
• language and framework
• what the code is supposed to do
• whether names should be conservative or more descriptive
• local project conventions
• any names that must stay stable for API, DB, or compatibility reasons

Without that context, the skill can still improve style, but it may miss semantic intent.

Turn a vague request into a strong prompt

Weak prompt:

“Suggest better names for these variables.”

Better prompt:

“Use naming-analyzer on this TypeScript service file. Review function, variable, and class names. Keep React and project conventions intact, prefer camelCase for functions and variables, PascalCase for types and components, and do not rename public API fields. For each issue, show current name, suggested replacement, and one-line reasoning.”

That added scope reduces noisy suggestions and protects externally visible names.

A practical naming-analyzer workflow

A good naming-analyzer guide for real work looks like this:

1. start with one file or one PR, not the whole codebase
2. ask for issues grouped by identifier type
3. request suggestions with reasoning
4. review semantic accuracy before style consistency
5. apply safe renames in code tools, then rerun the skill on the updated file

This sequence avoids attractive but incorrect names.

Best prompts for code review

For naming-analyzer for Code Review, ask the skill to separate findings into:

• clear wins to rename now
• convention mismatches
• ambiguous names needing author confirmation
• names that are technically acceptable but worth standardizing later

That triage is more actionable than a flat list of rename ideas.

Language conventions it already knows

The source documents explicitly cover:

• JavaScript/TypeScript:
  • camelCase for variables and functions
  • PascalCase for classes and interfaces
  • UPPER_SNAKE_CASE for constants
  • boolean prefixes like is, has, can, should
• Python:
  • snake_case for variables and functions
  • PascalCase for classes
  • UPPER_SNAKE_CASE for constants

If your project intentionally differs, say so up front or the skill will optimize toward these defaults.

What the skill can review beyond code symbols

One useful detail many users miss: naming-analyzer is not limited to variables and methods. It can also review:

• file and directory names
• database table and column names
• API endpoint naming

That makes it useful when the naming problem spans application code and system boundaries.

What good output should look like

A strong response from the naming-analyzer skill should include:

• the problematic identifier
• why it is weak or inconsistent
• one or more better alternatives
• the convention or semantic reason behind the suggestion
• any caution where renaming may affect public interfaces

If the output is only a list of replacement names with no reasoning, ask it to justify each suggestion.

Example prompt pattern that improves results

Use a structure like this:

“Run naming-analyzer on the code below. Target: Python. Goal: improve readability without changing domain meaning. Check variables, functions, classes, and boolean names. Flag vague abbreviations, misleading names, and convention mismatches. Return a table with current_name, issue, suggested_name, reason, and rename_risk.”

This format makes the first pass much easier to review and apply.

naming-analyzer skill FAQ

Is naming-analyzer worth using if I already have a linter

Yes, if your problem is semantics rather than formatting. Linters usually catch pattern violations; naming-analyzer is more useful when names are technically valid but still vague, misleading, inconsistent, or cognitively expensive.

Is the naming-analyzer skill beginner-friendly

Yes. Beginners often know a name feels weak but do not know what a better alternative should emphasize. This skill helps by connecting code behavior to naming conventions and giving reasons, not just replacements.

When is naming-analyzer a poor fit

Skip naming-analyzer when:

• you need automated mass rename execution
• you cannot share enough code context
• names are constrained by external contracts you cannot change
• the real issue is architecture, not naming

In those cases, ordinary review or refactoring tools may matter more.

Does naming-analyzer work for whole repositories

It can, but repository-wide prompts often produce shallow results. Start with one module, one directory, or one PR. The skill is much more reliable when the scope is narrow enough to preserve meaning.

How is naming-analyzer different from asking for “better names”

The main difference is review discipline. The skill explicitly checks convention, clarity, consistency, misleading semantics, abbreviation quality, and boolean prefixes. That gives you a more systematic review than a freeform brainstorm.

Can I use naming-analyzer for public APIs and databases

Yes, but carefully. The skill can review endpoint, table, and column names, yet rename suggestions in those areas may carry migration or compatibility costs. Ask it to mark high-risk names separately from low-risk internal cleanup.

How to Improve naming-analyzer skill

Give naming-analyzer the behavior, not just the symbol

The biggest upgrade in results comes from adding behavioral context. Instead of pasting:

fn process(data)

add:

“This function validates user-uploaded CSV rows, removes duplicates, and returns normalized records.”

Now the skill can suggest names tied to actual responsibility rather than generic verbs.

Include project naming patterns explicitly

If your repository uses patterns like:

• suffixing React hooks with use
• prefixing booleans with is or has
• reserving DTO or Model for certain layers
• using domain abbreviations intentionally

state that before invocation. Otherwise, naming-analyzer may suggest names that are cleaner in isolation but inconsistent with the codebase.

Ask for risk-aware suggestions

A useful improvement prompt is:

“Use naming-analyzer and classify suggestions into safe internal renames, needs team review, and public contract risk.”

This keeps the skill practical in real repositories where not every good name is worth changing.

Force the skill to explain semantic mismatches

One common failure mode is superficially nicer names that still do not match behavior. Prevent that by asking:

“Only suggest a rename if you can explain how the current name misrepresents what the code actually does.”

That filter improves trust and reduces style-only churn.

Use side-by-side alternatives for ambiguous names

When a name could reasonably emphasize more than one concept, ask for multiple candidates:

“Provide 2-3 alternatives and explain what each one foregrounds.”

This is especially useful for service methods, domain entities, and data transformation utilities.

Improve first-pass output with a structured return format

If the first response feels messy, rerun with fields such as:

• identifier
• kind
• current_problem
• suggested_name
• reason
• confidence
• rename_risk

Structured output makes it easier to accept, reject, or escalate each suggestion.

Common failure modes to watch for

Even a good naming-analyzer guide should warn about these:

• over-descriptive names that become hard to scan
• generic verbs like handle, process, manage
• names that mirror implementation details instead of business meaning
• convention-perfect names that still hide purpose
• suggestions that ignore external compatibility constraints

Review for semantic accuracy first, then for style compliance.

Iterate after the first output

The best way to improve naming-analyzer usage is a second pass with tighter scope. For example:

1. first pass: identify weak names
2. second pass: refine only high-value renames
3. third pass: check consistency after edits

This works better than asking for a perfect full-codebase rename plan in one shot.

Pair the skill with your refactor tools

Use naming-analyzer for judgment and candidate generation, then apply accepted changes with IDE rename tooling, test runs, and lint checks. That combination gives you better names without risking broken references.

What users usually care about most

In practice, the highest-value improvements are:

• names that hide side effects
• booleans without clear truth semantics
• misleading function names
• inconsistent patterns across similar modules
• abbreviations only insiders understand

If you ask naming-analyzer to prioritize those categories first, the output becomes much more actionable.