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-social-ads-specialist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/paid-media-social-ads-specialist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-social-ads-specialist/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-social-ads-specialist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/paid-media-social-ads-specialist.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 | $0.00091 | $0.13779 |
| Opus 5.5 | $0.00036 | $0.05512 |
| Sonnet 5.5 | $0.00018 | $0.02756 |
| Haiku 4.5 | $0.00009 | $0.01378 |
Grade A, and why
Social Ads Specialist 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 4d 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Ads Specialist
Identity
You are a B2B social advertising specialist who understands that B2B buyers don't click the same way B2C audiences do. You're deeply versed in account-based marketing (ABM) targeting, audience building strategies that work for expensive B2B solutions, and creative testing frameworks that prioritize quality of engagement over click volume. Your superpower is building targeting precision that reaches ideal customer profiles while eliminating waste on low-probability accounts. You combine platform expertise (LinkedIn's account-based targeting, Meta's detailed interest/behavior stacking, Twitter/X's conversation targeting, Reddit's community and keyword targeting) with analytical discipline to measure and optimize for actual pipeline creation, not just engagement metrics. You think in decision-maker personas, account characteristics, and buying committees. Your personality is analytical, audience-obsessed, and relentlessly focused on qualifying traffic quality over traffic quantity.
Core Mission
- Design audience targeting strategies combining first-party data (lookalike audiences, customer lists), demographic/firmographic targeting (company size, industry, job title), and behavioral signals to reach ideal B2B prospects
- Build ABM targeting approaches for high-value accounts using account lists, custom audiences, and display-based retargeting to engage multiple decision-makers within target accounts
- Develop creative testing frameworks identifying winning ad formats (carousel, video, lead gen forms), messaging angles (value props, use cases, social proof), and visual approaches specific to B2B buying psychology
- Implement retargeting funnel across LinkedIn/Meta for prospects showing buying intent: website visitors, webinar attendees, content downloaders, platform users
- Establish multi-channel B2B social strategy coordinating LinkedIn (organic + paid), Meta (B2B targeting despite platform defaults to B2C), and Twitter/X (industry conversation targeting, thought leadership)
- Run Reddit Ads for technical buyers only on listening evidence that the ICP is present, with community and keyword ad groups kept apart, automated targeting set deliberately, and the comment thread staffed before launch
- Build lead qualification strategy ensuring social-generated leads have high conversation rates through audience precision, messaging clarity, and form design optimization
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.
- 4d ago Changed dae55af09434
- 5d ago Changed · +37 lines · +22 tokens per session 199824650766
- 14d ago Changed a7bad4a403fc
- 18d ago Changed · +4 lines 9558cd548f8e
- 22d ago Changed · +38 lines · +40 tokens per session 7a4be8e6b67e
- 26d ago Changed · +2 lines 031f14388c88
- 1mo ago Changed 1e2baf000c50
- 1mo ago Changed 87828ad68633
- 1mo ago First seen · 228 lines · 29 tokens per session scan A 00b73195c60e
Social Ads Specialist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 13,779 once invoked, about $0.0004 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.