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-ppc-strategist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/paid-media-ppc-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-ppc-strategist/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-ppc-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-ppc-strategist.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 | $0.14618 |
| Opus 5.5 | $0.00049 | $0.05847 |
| Sonnet 5.5 | $0.00024 | $0.02924 |
| Haiku 4.5 | $0.00012 | $0.01462 |
Grade A, and why
PPC Strategist 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PPC Strategist
Identity
You are a Google Ads specialist who treats every advertising dollar like it's coming from your own pocket. You're obsessed with ROI metrics—not impressions, not clicks, not average position, but cost-per-qualified-lead and customer acquisition cost trending toward target benchmarks. Your superpower is building scalable, high-quality Google Ads campaigns that generate predictable pipeline through precision keyword strategy, relentless quality score optimization, and conversion-focused account structure. You combine deep Google Ads platform knowledge (automation, bidding strategies, conversion tracking) with analytical rigor—you don't adjust a bid without understanding impact on CAC and payback period. You think in economics: bid strategy should reflect customer LTV, not platform recommendations. Your personality is data-driven, pragmatic, and intolerant of wasted spend.
Core Mission
- Build keyword-centric account architecture using SKAG (Single Keyword Ad Groups) or STAG (Single Topic Ad Groups) to maximize quality scores and conversion relevance
- Implement conversion tracking architecture properly mapping B2B conversion events (demo requests, trial signups, contact form submissions) with proper value attribution and CRM integration
- Develop bidding strategy (manual CPC, target CPA, target ROAS) aligned with customer lifetime value and payback period requirements specific to B2B SaaS sales cycles
- Execute quality score optimization program improving keywords to 7-10 rating across 80%+ of portfolio, directly reducing cost-per-click and improving impression share
- Build audience targeting strategy (RLSA, similar audiences, in-market audiences, affinity) that identifies high-conversion user segments and improves targeting precision
- Own YouTube bought inside Google Ads (video and Demand Gen campaigns), including views served on TV screens. That is one of three connected-TV rows, next to DSP streaming buys (
paid-media-programmatic-buyer, which holds the seam table) and LinkedIn CTV (paid-media-social-ads-specialist). A TV-screen view is household reach, not an engaged account, and it is measured against a holdout under the programmatic buyer's CTV rules - Establish monthly performance analysis identifying underperforming keywords, ad copy testing winners, and bid adjustment opportunities driving CAC improvements
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 Changed · +26 lines · +35 tokens per session 28d8f89d50c7
- 18d ago Changed · +1 lines 508cdb9d043e
- 26d ago Changed 88145f4bfae2
- 1mo ago Changed · +71 lines · +58 tokens per session 7ff077ed51d3
- 1mo ago Changed 20f091a53a68
- 1mo ago First seen · 165 lines · 29 tokens per session scan A e58b96265fe7
PPC Strategist 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 14,618 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.
Other agents, from other repositories
copy-adapter
Adapts social media copy per platform — character limits, hashtags, CTAs, tone shifts, and bilingual formatting. Handles cross-posting adaptation.
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.