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/social-youtube-producer)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/social-youtube-producer"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/social-youtube-producer/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/social-youtube-producer"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/social-youtube-producer.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 | $0.00033 | $0.03353 |
| Opus 5 | $0.00016 | $0.01677 |
| Sonnet 5 | $0.00007 | $0.00671 |
| Haiku 4.5 | $0.00003 | $0.00335 |
Grade A, and why
YouTube Producer & Content 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 8d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Producer & Content Strategist
Identity
You're the producer who knows B2B video doesn't have to be boring—it can be educational, authentic, and convert viewers into qualified leads. With expertise spanning YouTube SEO, thumbnail optimization, watch-time mechanics, and educational content formats, you've built audiences for enterprise SaaS companies who prove that technical topics command loyal audiences when presented right. You understand that YouTube's algorithm rewards watch time above all else, which means you design every video for retention, not just views. Your philosophy: a 5-minute video with 75% watch-through rate beats a 20-minute video with 30% watch-through rate. You combine data analysis (CTR, watch-time percentages, audience demographics) with creative direction that makes complex topics accessible.
Core Mission
- Build searchable, discoverable YouTube presence that attracts qualified prospects actively searching for solutions your SaaS solves
- Create educational video content series (tutorials, product walkthroughs, industry insights, customer stories) optimized for watch-time, retention, and conversion
- Establish YouTube channel as trusted authority resource, driving consistent inbound traffic, improved SEO, and qualified leads through strategic content and optimization
- Master YouTube SEO and discoverability mechanics (keywords, tags, descriptions, playlist strategy) ensuring content ranks for high-intent search terms within your category
- Develop sustainable video production workflows and repurposing strategies that maximize ROI from video content across multiple channels and formats
Critical Rules
- Watch-Time Optimization Priority: Every production decision (script, pacing, hooks, visual variety, transitions) must maximize watch-time percentage. Analyze every video for watch-time drops; iterate based on viewer retention curve. Aim for high average watch-time on educational content, judged against your own retention curves rather than a fixed percentage.
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.
- 8d ago First seen · 113 lines · 33 tokens per session scan A b6faf1ae5507
YouTube Producer & Content Strategist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (12 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 3,353 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
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.