skill-003-naming-convention-consistency

skill-003-naming-convention-consistency is a skill for Claude Code from TrueClicks/claude-plugins. It costs 27 tokens per session (570 once invoked), scanned A, original, MIT.

A checker for naming patterns across advertising campaigns, ad groups, and labels. A naming taxonomy is a shared format that makes names predictable and easier to report on.

In plain words
What is it for?
Use it to audit campaign and ad-group names, detect naming patterns and outliers, and suggest a consistent format.
Why use it?
It finds inconsistent, duplicate, overly long, or unclear names that can make reports unreliable and automation rules fail.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the google-ads plugin — 52 skills, 4 commands shipped together

Good fit Use it to audit campaign and ad-group names, detect naming patterns and outliers, and suggest a consistent format.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add TrueClicks/claude-plugins --skill skill-003-naming-convention-consistency
Clone the repo
git clone --depth 1 https://github.com/TrueClicks/claude-plugins

Made for: Claude Code.

Or install google-ads, the plugin that ships this one along with the rest of its 52 skills, 4 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for skill-003-naming-convention-consistency

README.md
[![agentmods](https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency/github.svg)](https://agentmods.dev/skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency)
Your own site
<a href="https://agentmods.dev/skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for skill-003-naming-convention-consistency

Your own site · 80×15
<a href="https://agentmods.dev/skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-003-naming-convention-consistency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 570 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00027 $0.00570
Opus 5 $0.00014 $0.00285
Sonnet 5 $0.00005 $0.00114
Haiku 4.5 $0.00003 $0.00057

Measured 10d ago against content hash 0bc56722eeb2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

skill-003-naming-convention-consistency scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/google-ads/skills/skill-003-naming-convention-consistency/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill 003: Campaign Naming Convention Consistency

Purpose

Audit naming conventions across all campaigns, ad groups, and labels for a standardized taxonomy. Inconsistent naming makes reporting unreliable, cross-account management chaotic, and automation rules fragile.

Data Requirements

Data Source: Standard

Standard Data:

  • data/account/campaigns/*/campaign.md - All campaign names
  • data/account/campaigns/*/*/ad_group.md - All ad group names

Reference GAQL:

SELECT
  campaign.id,
  campaign.name,
  campaign.advertising_channel_type
FROM campaign
WHERE campaign.status != 'REMOVED'

Use /google-ads:get-custom if you need to include labels or additional metadata.

Analysis Steps

  1. Extract all entity names: List all campaign and ad group names from the data files
  2. Detect naming patterns: Identify delimiters (-, _, |, /) and common segments (Region, Product, Network, Match Type)
  3. Analyze consistency: Group by detected pattern, calculate percentage using each, flag outliers
  4. Check for problems: Very long names (>100 chars), special characters, duplicates, non-descriptive names ("Campaign 1", "Test")
  5. Recommend taxonomy: Suggest format based on majority pattern or best practice

Thresholds

Condition Severity
Consistency score < 50% Critical
Duplicate campaign/ad group names Critical
Consistency score < 80% Warning
Non-descriptive names ("Test", "Campaign 1") Warning
Name > 100 characters Info

Output

Use Short format by default. Use Detailed if user requests comprehensive analysis.

Short:

## Naming Convention Audit
**Account:** [Name] | **Analyzed:** [X] campaigns, [Y] ad groups | **Consistency:** [Z]%

### Critical ([Count])
- **Duplicate names:** "[Name]" appears [X] times → Rename to differentiate

### Warnings ([Count])
- **[Entity]**: Non-descriptive name → Rename to [Suggested]

### Recommendations
1. Adopt format: [Region]_[Brand/NB]_[Product]_[Network]
2. Rename [X] non-conforming entities

Read the full file on GitHub · 72 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 72 lines · 27 tokens per session scan A 0bc56722eeb2

Subscribe to this mod's changes

skill-003-naming-convention-consistency is a skill published in the GitHub repository TrueClicks/claude-plugins (2 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 570 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens