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 varunk130/ai-gtm-skill-library --skill budget-allocatorgit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/budget-allocator)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/budget-allocator"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/budget-allocator/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/varunk130/ai-gtm-skill-library/budget-allocator"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/budget-allocator.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 | $0.00040 | $0.02458 |
| Opus 5.5 | $0.00016 | $0.00983 |
| Sonnet 5.5 | $0.00008 | $0.00492 |
| Haiku 4.5 | $0.00004 | $0.00246 |
Grade A, and why
budget-allocator 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.
How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Budget Allocator (APEX Allocation Model)
A rigorous budget optimization engine that applies portfolio theory principles to marketing spend allocation, producing scenario-modeled investment plans with built-in experimentation reserves and continuous rebalancing triggers. APEX ensures every dollar is allocated to its highest-impact use while maintaining optionality for emerging opportunities.
When to Use
- Planning marketing budget for a product launch
- Allocating spend across channels and timeframes
- Building ROI projections for budget approval
- Designing structured marketing experiments with kill criteria
- Stress-testing budget assumptions through sensitivity analysis
- Rebalancing mid-campaign when channels over- or underperform
- Justifying budget requests to finance or leadership
What You'll Need
Critical inputs (ask if not provided):
- Total available budget and time horizon
- Target metrics (pipeline, revenue, CAC targets, ROI floor)
- Channel performance data or benchmarks (from demand-engine WAVE scores)
- Product and launch context (launch type, audience, market)
- Financial constraints or guardrails (max spend per channel, minimum ROI)
Nice-to-have:
- Historical channel performance data (CAC, conversion rates, LTV by channel)
- Competitive spend intelligence (from battle-scanner)
- Seasonal or market timing data (from signal-radar)
- Customer journey stage mapping (from journey-architect)
- Previous launch budgets and actuals for calibration
Process
Step 1: Allocate -- Define the Five Spend Buckets
Every launch budget is divided into five strategic buckets. The percentages flex based on launch type and maturity.
| Bucket | Range | Purpose | Examples |
|---|---|---|---|
| Foundation | 15-20% | Infrastructure that enables all other spend | Website, landing pages, tracking, tooling, creative assets |
| Awareness | 25-30% | Top-of-funnel reach and brand visibility | Content marketing, PR, social media, display, sponsorships |
| Acquisition | 30-35% | Direct pipeline and demand generation | Paid search, paid social, email campaigns, events, webinars |
| Enablement | 10-15% | Sales and partner activation | Sales tools, partner co-marketing, demo environments, training |
| Experiment Reserve | 10-15% | Structured tests on unproven channels | New channels, messaging tests, audience tests, creative tests |
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.
- 1mo ago First seen · 219 lines · 40 tokens per session scan A cdada86d61ab
budget-allocator is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 40 tokens to every session and 2,458 once invoked, about $0.0002 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 2026-08-31.
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