adspirer-optimize

adspirer-optimize is a skill for Claude Code from amekala/ads-mcp. It costs 61 tokens per session (1,250 once invoked), scanned A, original, MIT.

A skill for finding wasted advertising spend and proposing campaign changes through Adspirer, an advertising-management service. It checks several ad platforms for spending that is not producing enough conversions.

In plain words
What is it for?
Use it to analyze waste on Google, Meta, TikTok, LinkedIn, or Amazon campaigns, add exclusions such as negative keywords, and reallocate budgets after approval.
Why use it?
It helps identify money going to non-converting campaigns, irrelevant search terms, audiences, or placements, as well as budgets that are pacing badly.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the adspirer-advertising-agent plugin — 19 skills, 5 commands, 1 agent, 2 MCP servers shipped together

Good fit Use it to analyze waste on Google, Meta, TikTok, LinkedIn, or Amazon campaigns, add exclusions such as negative keywords, and reallocate budgets after approval.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/amekala/ads-mcp/adspirer-optimize
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 amekala/ads-mcp --skill adspirer-optimize
Clone the repo
git clone --depth 1 https://github.com/amekala/ads-mcp

Made for: Claude Code.

Or install adspirer-advertising-agent, the plugin that ships this one along with the rest of its 19 skills, 5 commands, 1 agent, 2 MCP servers.

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 adspirer-optimize

README.md
[![agentmods](https://agentmods.dev/badge/skills/amekala/ads-mcp/adspirer-optimize/github.svg)](https://agentmods.dev/skills/amekala/ads-mcp/adspirer-optimize)
Your own site
<a href="https://agentmods.dev/skills/amekala/ads-mcp/adspirer-optimize"><img src="https://agentmods.dev/badge/skills/amekala/ads-mcp/adspirer-optimize/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 adspirer-optimize

Your own site · 80×15
<a href="https://agentmods.dev/skills/amekala/ads-mcp/adspirer-optimize"><img src="https://agentmods.dev/badge/skills/amekala/ads-mcp/adspirer-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,250 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00061 $0.01250
Opus 5 $0.00030 $0.00625
Sonnet 5 $0.00012 $0.00250
Haiku 4.5 $0.00006 $0.00125

Measured 10d ago against content hash ab7e22a15cf4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

adspirer-optimize 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/chatgpt/adspirer/skills/adspirer-optimize/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cutting waste and pacing budget

Follow adspirer-mcp before any tool call. Load the platform skill for field rules. This skill proposes changes and applies them only after the user agrees to that specific change.

Find the waste

Platform Tool (through its router unless noted)
Google analyze_wasted_spend, then analyze_search_terms
Meta analyze_meta_wasted_spend
TikTok analyze_tiktok_wasted_spend
LinkedIn analyze_linkedin_wasted_spend
Amazon analyze_amazon_wasted_spend, then get_amazon_search_terms

Waste has a few shapes and they need different fixes:

  • Spend with zero conversions. The obvious one. But check the volume first — an ad group with $40 spend and no conversions may simply not have had a chance yet at a $60 CPA.
  • Spend converting far above target. Worse than zero, because it looks like it's working.
  • Search terms you never intended to buy. On Google and Amazon this is where the money leaks: broad match matching "free", "jobs", "diy", a competitor's brand, or a wildly adjacent product.
  • Placements and audiences that never convert. Display and TikTok especially.

Before you cut anything

Check the tracking first. Run audit_conversion_tracking. A campaign that looks like it converts nothing very often converts fine and reports nothing. Pausing it would destroy a working campaign — this mistake is common and expensive.

Check the volume. Ten clicks and no conversions is not evidence. Roughly, you want at least 3× the target CPA in spend, or ~100 clicks, before you call something a loser.

Check the assist. The campaign with no last-click conversions may be the one introducing people to the brand. Look at view-through and assisted conversions before killing an upper-funnel campaign.

Fix it, in this order

  1. Negatives before pauses. Adding negative keywords is precise, reversible, and keeps the campaign learning. add_negative_keywords (Google), the equivalent on Amazon. Pausing a whole ad group to stop one bad search term is a blunt instrument.
  2. Exclude placements and audiences that spend without converting.
  3. Pause the ad, ad group, or campaign — the narrowest thing that solves the problem.
  4. Reallocate what you freed up.

Read the full file on GitHub · 109 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 · 109 lines · 61 tokens per session scan A ab7e22a15cf4

Subscribe to this mod's changes

adspirer-optimize is a skill published in the GitHub repository amekala/ads-mcp (89 stars, last pushed 3d ago), licensed MIT. It adds 61 tokens to every session and 1,250 once invoked, about $0.0003 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 2026-08-30.

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