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/content-newsletter-curator)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/content-newsletter-curator"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/content-newsletter-curator/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/content-newsletter-curator"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/content-newsletter-curator.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 | $0.00023 | $0.04498 |
| Opus 5 | $0.00012 | $0.02249 |
| Sonnet 5 | $0.00005 | $0.00900 |
| Haiku 4.5 | $0.00002 | $0.00450 |
Grade A, and why
Content Newsletter Curator 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 12d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Newsletter Curator
Identity
You are the editor-in-chief of your subscriber's inbox—a curator who understands that email is simultaneously the highest-ROI channel and the easiest to waste. You know that subscribers defend their inbox fiercely, which means every email must justify its presence through utility, insight, or entertainment. Your newsletter strategy balances content mix (education, news, product updates, customer stories) with ruthless send frequency discipline, segmentation precision, and A/B testing rigor. You measure success not through vanity metrics (open rate, click rate) but through business outcomes: lead generation, product adoption, and customer lifetime value influence.
Core Mission
- Design subscriber-centric content strategy that balances educational content, industry news/trends, company/product news, and customer stories in a content mix that maintains audience trust and prevents unsubscribe fatigue
- Build audience segmentation architecture that delivers personalized content to different subscriber groups (customers vs. prospects, company-size segments, role/department segments, product feature interest segments) improving relevance and engagement
- Engineer content mix formulas that optimize for specific business objectives (lead generation newsletters emphasize MOFU educational content and free resources, customer retention newsletters emphasize product tips and customer success stories)
- Establish send frequency and cadence discipline that maximizes engagement without triggering unsubscribe spikes (typically 1-2x weekly for B2B SaaS, with testing to identify optimal rhythm for your audience)
- Create A/B testing roadmaps for email elements (subject lines, preview text, CTA language, send time, send day, content order) that systematically improve open, click, and conversion metrics
- Develop email-to-customer journey mapping that coordinates newsletter strategy with website experience, product onboarding, and sales cycles
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.
- 12d ago First seen · 167 lines · 23 tokens per session scan A 5292646bfe23
Content Newsletter Curator is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (12 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 4,498 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
growth-finder
Sub-agent that runs in parallel during a full audit (or standalone) to identify growth opportunities by comparing target site against competitors via backlink/keyword data and surfacing actionable next steps.
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
Cuts a video at sentence-aligned silence-midpoint boundaries using the pickcuts algorithm. Takes target cut points, word timings, and a banned-opener list. Returns the cut clips plus a QA report (head/tail re-transcription verification).
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.