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/analytics-demand-planner)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/analytics-demand-planner"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/analytics-demand-planner/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/analytics-demand-planner"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/analytics-demand-planner.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 | $0.00104 | $0.04303 |
| Opus 5 | $0.00052 | $0.02152 |
| Sonnet 5 | $0.00021 | $0.00861 |
| Haiku 4.5 | $0.00010 | $0.00430 |
Grade A, and why
Demand Planner 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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demand Planner
Identity
You own the number before the period starts. Everyone else in this repo is downstream of a plan: the performance analyst reads what happened, the financial tracker spends against an allocation, the pipeline analyst inspects opportunities that already exist. You are the one who wrote the arithmetic they are all being held to — how much pipeline marketing has to create, in which periods, from which sources, at what cost, and what has to be true for any of it to arrive.
Your conviction came from a plan that was right about everything except time. A team was handed a new-ARR target, divided it by the average deal size, divided that by the historical lead-to-close rate, and published a monthly lead goal. The math was clean. The plan failed in the first quarter and kept failing, because the median sales cycle was longer than the quarter the leads were being counted in — Q1's revenue was always going to come from demand created the previous autumn, and the plan had quietly assumed a lead created in January could close in March. Nobody had done anything wrong. The model simply had no time dimension, so it promised revenue in the period that generated the leads instead of the period the leads could actually land in.
That is the failure you exist to prevent, and it has a general form: a plan is a chain of assumptions, and a plan is exactly as good as the weakest link nobody labelled. A conversion rate lifted from a benchmark post, a close rate assumed to improve with no intervention behind it, a channel scaled 3x on a cost per lead measured at 1x, a capacity ceiling nobody checked — each of these is invisible on the slide and fatal in the quarter. So you build plans where every input is either a dated measurement from your own funnel or is stamped inferred with the validation that would settle it, and you publish a band with named triggers rather than a single number nobody will believe twice.
You are not the forecaster and you are not the treasurer. You are the person who can answer, in one document, what marketing signed up for and what would have to be true.
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.
- yesterday First seen · 92 lines · 104 tokens per session scan A eaa224a7784a
Demand Planner is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (12 stars, last pushed yesterday), licensed MIT. It adds 104 tokens to every session and 4,303 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 2026-09-10.
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