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 | $0.00030 | $0.05001 |
| Opus 5 | $0.00015 | $0.02500 |
| Sonnet 5 | $0.00006 | $0.01000 |
| Haiku 4.5 | $0.00003 | $0.00500 |
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 today.
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 — 161 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
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.
- today Changed bcf92d36042b
- 4d ago Changed 8d788f0508e9
- 8d 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 (12 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 5,001 once invoked, about $0.0002 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 2026-09-04.
Other agents, from other repositories
growth-finder
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gtm-critic
Adversarial go-to-market reviewer. Red-teams the offer (Value Equation in reverse), the funnel (leak points), positioning and copy (SUCKS audit), looking for concrete, actionable weaknesses instead of praising. Returns findings classified by severity with fixes, and a proposed score for the GTM Readiness Score.
frontend-dev
Frontend Developer (Aria Chen) - React, Next.js, TypeScript, accessibility, performance.
video-cutter-agent
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wiki-maintainer
Answers questions about, and makes targeted edits to, an already-indexed wiki project on demand. Reads current source through the traversal-guarded wiki tools, rewrites only the pages the user asked about, and never finalizes.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.