ads-optimization-signals

ads-optimization-signals is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 31 tokens per session (1,017 once invoked), scanned A, original, MIT.

A guide to judging advertising performance using early signals, slower business results, and testing rules. It explains metrics such as click-through rate, cost per lead, return on investment, and revenue.

In plain words
What is it for?
Use it to choose metrics for each campaign stage, calculate break-even lead costs, plan experiments, and decide when a result is reliable enough to act on.
Why use it?
It helps separate useful evidence from random fluctuations, so changes are not made too quickly or based on vanity metrics.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to choose metrics for each campaign stage, calculate break-even lead costs, plan experiments, and decide when a result is reliable enough to act on.

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Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/ads-optimization-signals
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 swan-gtm/gtm-skills --skill ads-optimization-signals
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-skills

Made for: Claude Code, Codex.

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 ads-optimization-signals

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ads-optimization-signals/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/ads-optimization-signals)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/ads-optimization-signals"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ads-optimization-signals/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 ads-optimization-signals

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/ads-optimization-signals"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/ads-optimization-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,017 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 $0.00031 $0.01017
Opus 5.5 $0.00012 $0.00407
Sonnet 5.5 $0.00006 $0.00203
Haiku 4.5 $0.00003 $0.00102

Measured 1mo ago against content hash bbe01791c67a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ads-optimization-signals 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 1mo 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.

skills/ivan-falco/ads-optimization-signals/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Optimization Signals & Testing Rules

Why Optimization Matters

  • After campaigns launch, the real work begins
  • Take time for planning new experiments and writing down learnings
  • Building on your learnings while others repeat the same mistakes

Optimization Signal Types

  • Leading = events that happen quickly (< month) - use for quick optimization
  • Lagging = events that happen slowly (> month) - use for directional truth

Signals by Stage

Stage Leading Lagging
Create CTR, Engagement Rate Blended Inbound Leads
Capture CPL, CPMQL Pipe-to-Spend, CPOPP
Accelerate/Activate Accounts Reached, CTR Avg time to close, Influenced Revenue
Revive CPL, CPMQL Pipe-to-Spend, ROI
Expand Accounts Reached, CTR Expansion Revenue

Key Insight

The key is choosing the right leading signals that actually influence your lagging events. Experimentation is key, and you'll likely change over time.

The average B2B sales cycle can range from 2-24 months - you can't afford to wait even 2 weeks without being able to optimize your campaigns.

Testing Rules

Find Your Breakeven Costs

Breakeven CPL Formula:

Breakeven CPL = Average deal size x lead to close won rate

Example: $3,000 x 10% = $300 breakeven CPL

Breakeven CPC Formula:

Breakeven CPC = CPL target x landing page conversion rate

Example: $300 x 5% = $15 breakeven CPC

Two Essential Rules

1. Non-Performer Rule

When to apply: All time

Pause ad if it's spent 2-3x your target CPL with 0 conversions.

Example: Target CPL = $300, ad spends $600-$900 = PAUSE

This helps with pausing new ads you're testing.

2. Maintenance Rule

When to apply: Past 7 to 14 days (depending on volume)

Pause ad if the CPL is 1.5-2x over your target CPL.

Example: Target CPL = $300, current ad CPL = $450-$600 = PAUSE

This helps with pausing old ads that start to underperform.

Important Notes

These aren't statistically significant but they're repeatable, easy to follow, and prevent emotional decision-making.

Read the full file on GitHub · 123 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. 1mo ago First seen · 123 lines · 31 tokens per session scan A bbe01791c67a

Subscribe to this mod's changes

ads-optimization-signals is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 8d ago), licensed MIT. It adds 31 tokens to every session and 1,017 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.

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