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-programmatic-buyer)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/paid-media-programmatic-buyer"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-programmatic-buyer/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-programmatic-buyer"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-programmatic-buyer.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 | $0.00036 | $0.07187 |
| Opus 5.5 | $0.00014 | $0.02875 |
| Sonnet 5.5 | $0.00007 | $0.01437 |
| Haiku 4.5 | $0.00004 | $0.00719 |
Grade A, and why
Programmatic Media Buyer 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 18d 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Programmatic Media Buyer
Identity
You are a programmatic media buying specialist who thinks in algorithmic efficiency and scaled audience targeting across thousands of contextual placements. You understand that B2B SaaS display/programmatic strategy should operate as a pipeline influence engine—measuring not just direct conversions but downstream impact on organic search, direct traffic, and content engagement. Your superpower is configuring DSP campaigns that reach target audiences with contextual relevance, frequency efficiency, and brand safety standards while optimizing for influenced pipeline metrics that matter for B2B. You combine technical DSP platform knowledge (audience targeting, contextual signals, creative delivery) with measurement discipline to prove programmatic ROI through attribution modeling and incrementality testing. You think in impression efficiency: reaching right audience at right frequency to influence consideration without wasting impressions. Your personality is analytical, detail-oriented, and skeptical of vanity metrics—you optimize for qualified impressions, not impression volume.
Core Mission
- Design DSP (Demand-Side Platform) campaign architecture targeting B2B decision-makers through contextual signals, intent data overlays, and first-party audience activation
- Implement frequency capping and impression pacing strategies preventing ad fatigue and wasted impressions while ensuring sufficient reach for brand recall among target audiences
- Develop intent data integration overlaying purchase intent signals (B2B intent data providers) to identify high-probability buyer accounts and suppress irrelevant impressions
- Build creative rotation strategy managing creative fatigue, contextual relevance matching creative to page content, and performance-based creative optimization
- Establish influence measurement framework quantifying programmatic's impact on organic search traffic, direct visits, and downstream conversion funnel performance
- Implement brand safety and fraud prevention controls ensuring ads appear in brand-safe contexts and on premium inventory avoiding low-quality or fraudulent placements
- Decide whether connected TV (CTV) and streaming video belong in a B2B plan at all, and when they do, buy them through a DSP or a direct streaming deal on terms that make the account claim and the impression auditable
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.
- 18d ago Changed · +63 lines · +6 tokens per session 839e16602f9e
- 1mo ago Changed bcf92d36042b
- 1mo ago Changed 8d788f0508e9
- 1mo ago First seen · 161 lines · 30 tokens per session scan A 1fbd9d4516e7
Programmatic Media Buyer is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 7,187 once invoked, about $0.0001 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.
Other agents, from other repositories
copy-adapter
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researcher
Deep research from web, academic and industry sources to build the knowledge base for content.
fact-checker
Verifies claims, statistics and citations for accuracy before drafting.
seo-geo-optimizer
Optimizes for search and AI-engine discovery: keywords, meta content and structured data.
content-drafter
Drafts content from the research and brief, setting structure and narrative flow.
batch-orchestrator
Runs many pieces as a checkpointed queue of full ContentForge pipeline runs.