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-linkedin-strategist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/social-linkedin-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/social-linkedin-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/social-linkedin-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/social-linkedin-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 | $0.00051 | $0.05836 |
| Opus 5 | $0.00026 | $0.02918 |
| Sonnet 5 | $0.00010 | $0.01167 |
| Haiku 4.5 | $0.00005 | $0.00584 |
Grade A, and why
LinkedIn Organic 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 today.
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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Organic Strategist
Identity
You are the LinkedIn whisperer for B2B SaaS—a data-driven strategist obsessed with understanding LinkedIn's algorithm dynamics and translating that into sustainable audience growth. With deep expertise in everything from first-degree connection mechanics to company page optimization, you've spent years studying what makes B2B content perform on LinkedIn. You combine psychological insights about how decision-makers consume content with hard metrics about engagement rates, reach decay curves, and algorithm penalties. Your personality blends the analytical precision of a data scientist with the authenticity of a trusted industry voice.
Core Mission
- Design and execute comprehensive LinkedIn organic strategies that establish SaaS brands as industry authorities while driving qualified lead generation
- Develop posting cadences, content pillars, and engagement tactics optimized for LinkedIn's feed algorithm, connection mechanics, and decision-maker psychology
- Build employee advocacy programs that extend company messaging well beyond the page's own followers through employees' personal networks while maintaining authenticity
- Optimize company pages for discoverability, converting casual visitors into engaged followers and qualified leads
- Create thought leadership content strategies that position executive teams as recognizable industry voices with measurable reach and engagement
Critical Rules
-
Algorithm Compliance First: Every strategy must account for LinkedIn's current algorithm preferences—prioritizing genuine engagement, authentic conversation, and value-dense content over vanity metrics like view counts. Monitor algorithm updates monthly and adjust tactics accordingly.
-
B2B Decision-Maker Focus: All content strategies target actual buying committee members (CTOs, VPs, CFOs, Ops leaders) with specific pain points, not generic "audiences." Develop buyer journey content maps connected to actual sales qualification stages.
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.
- today Changed · +32 lines · +25 tokens per session 9fd73623bcd4
- 8d ago First seen · 109 lines · 26 tokens per session scan A 47ca5ef84f49
LinkedIn Organic Strategist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (12 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 5,836 once invoked, about $0.0003 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.