ad-spend-allocation

Split a fixed total paid-media budget across campaigns.

What this skill does for you

Split a fixed total paid-media budget across campaigns, platforms, funnel stages, and audiences based on expected marginal return - the monthly or quarterly reallocation decision, for B2B and B2C. Use whenever the user asks how to split an ad budget, which channel or campaign should get more money, how to reallocate spend, how much to put behind prospecting vs retargeting, or mentions a portfolio split, marginal return, or diminishing returns - even if they never say 'allocation'. Do NOT use to raise the total budget on a proven campaign (mbfinotti/advertising-skills@paid-media-scaling), to track daily spend against a set budget (mbfinotti/advertising-skills@ad-budget-pacing), or to pick channels at setup (mbfinotti/advertising-skills@ad-platform-selection).

Quoted from the skill description in the pinned source

Who made it

Publishermbfinotti

Repositoryadvertising-skills

Installs820as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the ad-spend-allocation skill from mbfinotti/advertising-skills.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

Scanner	Date	Result
Edge static checks	2026-09-27	Pass
Cisco skill-scanner	2026-09-27	Pass
Semgrep Edge rules	2026-09-27	Pass

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: 7ede9c641c67d03fed5df54613f14e3d999a8716. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: 7ede9c641c67d03fed5df54613f14e3d999a8716. Raw findings: . Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: 7ede9c641c67d03fed5df54613f14e3d999a8716. Raw findings: . Counted findings: .

Source and licence

Repository: https://github.com/mbfinotti/advertising-skills
Pinned skill file: View source at revision
Revision: 7ede9c641c67d03fed5df54613f14e3d999a8716
Package hash: sha256:3d4848a91e0809f21d4b4da73b562b7a018548f9013f4c9b51031b00032303af
Licence: MIT