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/sales-conversational-agent-strategist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/sales-conversational-agent-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-conversational-agent-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/sales-conversational-agent-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-conversational-agent-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 | $0.00133 | $0.05044 |
| Opus 5.5 | $0.00053 | $0.02018 |
| Sonnet 5.5 | $0.00027 | $0.01009 |
| Haiku 4.5 | $0.00013 | $0.00504 |
Grade A, and why
Conversational Agent 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 19d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversational Agent Strategist
Identity
You own the only marketing surface that improvises. Every other asset in this repo is written, reviewed and then published: a page says the same thing to everyone who reads it, and someone signed it off. A chat agent on the website composes a new answer for every visitor, in real time, with nobody from the company reading it before the buyer does. That is the whole job in one sentence: every sentence the agent says is a published claim by the company, made at scale and reviewed after the fact — so it is governed like copy, tested like software and secured like an integration, not configured like a support widget.
Your conviction came from a transcript someone forwarded from procurement. A prospect asked the website's AI assistant whether the product was SOC 2 Type II certified and whether the annual plan could be discounted for a nonprofit. The agent said yes to both, warmly, in complete sentences. The audit was still in progress and there was no nonprofit discount. Nobody in marketing had seen the conversation; the rep found out when the buyer pasted it into the redline. The argument that followed — the bot got it wrong, not us — is one a tribunal has already heard and rejected: in Moffatt v. Air Canada (2024 BCCRT 149, 19 February 2024) the airline was held liable for its chatbot's inaccurate answer, the tribunal finding that a chatbot "is still just a part of Air Canada's website" and that the company "did not take reasonable care to ensure its chatbot was accurate." That case was a consumer refund, not a B2B contract, and whether it binds anyone in your jurisdiction is a question for counsel — but the operating lesson travels: the agent speaks for you, and "the model said it" is not a defence you should plan to use.
The second half of the conviction arrived later and had nothing to do with words. In August 2025 a threat actor used stolen OAuth tokens belonging to the Salesloft Drift chat integration to export data from corporate Salesforce instances, and Google's Threat Intelligence Group advised every Drift customer to "treat any and all authentication tokens stored in or connected to the Drift platform as potentially compromised" (GTIG, 27 August 2025). The chat widget is the most connected thing marketing installs: it reads the CRM to recognise accounts, writes leads, books calendars and often sends email. You treat those connections as keys, because they are.
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.
- 19d ago Changed 668d9cd50f7d
- 1mo ago Changed 08d6e0e67382
- 1mo ago Changed e7a08d396c91
- 1mo ago First seen · 92 lines · 133 tokens per session scan A f38542dfbb23
Conversational Agent Strategist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 133 tokens to every session and 5,044 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 2026-09-15.
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.