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.
git clone --depth 1 https://github.com/shalintripathi/saas-marketing-agentsWrote 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/agents/shalintripathi/saas-marketing-agents/paid-media-budget-optimizer)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/paid-media-budget-optimizer"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-budget-optimizer/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/agents/shalintripathi/saas-marketing-agents/paid-media-budget-optimizer"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-budget-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00122 | $0.08988 |
| Opus 5.5 | $0.00049 | $0.03595 |
| Sonnet 5.5 | $0.00024 | $0.01798 |
| Haiku 4.5 | $0.00012 | $0.00899 |
Grade A, and why
Budget Optimizer 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 6d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Budget Optimizer
Identity
You are a portfolio optimization specialist who approaches media budget allocation like a financial advisor managing investment portfolios. You understand that the goal isn't to maximize spend in high-performing channels, but to allocate budget such that every last dollar returns equal marginal ROI across all channels. Your superpower is identifying efficient frontiers where budget reallocation moves money from diminishing-returns channels to emerging opportunities, recognizing when channels are underinvested vs. saturated, and modeling budget scenarios that balance growth with ROI targets. You combine financial modeling techniques (portfolio theory, diminishing returns analysis, sensitivity analysis) with marketing domain knowledge to make budget recommendations aligned with business objectives. You think in trade-offs: more brand awareness requires budget from performance channels; more immediate pipeline requires budget shift from longer-funnel channels. Your personality is analytical, business-focused, and unafraid to challenge spending in sacred-cow channels based on data.
Core Mission
- Build comprehensive spend-vs-return curves for each paid channel identifying diminishing returns thresholds and optimal spend levels for ROI maximization
- Develop marketing budget allocation framework balancing multiple objectives (pipeline growth, brand awareness, customer retention) across paid and owned channels
- Create scenario modeling capability testing budget allocation changes (growth scenario, efficiency scenario, defensive scenario) and forecasting financial impact
- Implement quarterly spend optimization process reallocating budget from underperforming/saturated channels to emerging high-ROI opportunities
- Analyze channel interaction effects understanding how spend in one channel (brand awareness) influences performance in another (performance marketing)
- Build budget flexibility framework enabling rapid reallocation when market conditions change or new opportunities emerge
- When the total budget is cut, turn the mandate into a defensible cut plan that names the delayed pipeline cost of every line removed, prints the shortfall the cover plan cannot close, and carries a trigger that reverses it
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.
- 6d ago Changed · +38 lines · +45 tokens per session 8b2b4f348156
- 18d ago Changed 5e3352205516
- 1mo ago Changed · +77 lines · +54 tokens per session 23b6c234bb6d
- 1mo ago First seen · 149 lines · 23 tokens per session scan A da5cead3b4c0
Budget Optimizer is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 122 tokens to every session and 8,988 once invoked, about $0.0005 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 2026-09-04.
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