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.
npx skills add swan-gtm/gtm-skills --skill google-ads-budget-portfolio-reallocationgit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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.
[](https://agentmods.dev/skills/swan-gtm/gtm-skills/google-ads-budget-portfolio-reallocation)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/google-ads-budget-portfolio-reallocation"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/google-ads-budget-portfolio-reallocation/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.
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/google-ads-budget-portfolio-reallocation"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/google-ads-budget-portfolio-reallocation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.00795 |
| Opus 5 | $0.00046 | $0.00398 |
| Sonnet 5 | $0.00018 | $0.00159 |
| Haiku 4.5 | $0.00009 | $0.00080 |
Grade A, and why
google-ads-budget-portfolio-reallocation 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio-Style Budget Reallocation Across Campaigns
Applies to an account with enough live history to compare campaigns against each other, not to a brand-new build with no data yet.
The play
- Model the account as one budget envelope, not a set of independent silos. The question isn't "does this campaign deserve more budget" in isolation — it's "given a fixed total, which campaign returns more for the next dollar."
- Read impression share lost to budget as the primary signal for underfunded campaigns — it directly shows demand the account is missing because the budget cap, not the auction, is the limit. Pair it with each campaign's marginal cost-per-conversion at current spend, not just its average, since averages hide campaigns that are efficient at low spend and expensive at the margin.
- Cap any single reallocation at roughly 20-30% of a campaign's budget per move. Bigger single jumps outrun what Smart Bidding can absorb cleanly and create a fresh learning period right when you need stable data to judge the change.
- Keep protected minimums outside the optimization — brand campaigns and any geography or line of business with a standing floor get funded first, regardless of where they'd rank on pure marginal return.
- For a known short-term demand spike (a launch, a flash sale, a 1-7 day event), separate two different levers and don't conflate them: raising the budget cap can start several days early so there's headroom once demand shows up, but a conversion-rate seasonality adjustment should start exactly when the shift begins — never days in advance "to be safe" — and always carry a hard end date matched to when the event ends.
- Consolidate campaigns running too little volume for reliable signal before optimizing across them; a marginal-return comparison built on thin data is a guess wearing a spreadsheet.
What good looks like
- The best reallocation plans move budget toward impression-share-limited campaigns before they move budget away from anything — funding real missed demand beats cutting a campaign that merely looks less efficient on average.
- The common mistake is starting a seasonality bid adjustment early "just to be safe." That pays inflated costs for normal-intent traffic during the run-up and is functionally the same error as forgetting to set an end date — both quietly burn budget outside the window that actually mattered.
- A good plan states the size of each shift as a percentage, the signal that justified it, and what happens to protected minimums — a plan that just says "move budget to the winners" hasn't done the portfolio work.
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.
- 9d ago First seen · 66 lines · 91 tokens per session scan A 4e908970dcf1
google-ads-budget-portfolio-reallocation is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 91 tokens to every session and 795 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
ad-campaign-analyzer
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
google-search-ads-builder
End-to-end Google Search Ads campaign builder. Performs deep keyword research (competitor SEO, review language mining, Reddit/HN community terminology, site audit), builds keyword architecture with funnel mapping and intent classification, creates ad group structure, generates headline/description variants, builds…
meta-ads-analyzer
Diagnose Meta Ads campaign performance and account gaps using Meta's actual system mechanics — including customer-journey coverage, Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue. Use for performance diagnosis, account audits, full-funnel or TOF/MOF/BOF gap analysis, deciding what to…
ad-campaign-analyzer
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
competitor-ad-intelligence
Scrape competitor ads from Meta, TikTok, Google, and LinkedIn ad libraries, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad…
ad-angle-miner
Mine the highest-converting ad angles from customer reviews, Reddit complaints, support tickets, and competitor ads. Extracts actual pain language, competitor weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank with proof quotes and recommended ad formats per angle.