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/timurgaleev/vibestackWrote 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/timurgaleev/vibestack/linkedin-content-creator)<a href="https://agentmods.dev/agents/timurgaleev/vibestack/linkedin-content-creator"><img src="https://agentmods.dev/badge/agents/timurgaleev/vibestack/linkedin-content-creator/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/timurgaleev/vibestack/linkedin-content-creator"><img src="https://agentmods.dev/badge/agents/timurgaleev/vibestack/linkedin-content-creator.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 | $0.00041 | $0.02941 |
| Opus 5.5 | $0.00016 | $0.01176 |
| Sonnet 5.5 | $0.00008 | $0.00588 |
| Haiku 4.5 | $0.00004 | $0.00294 |
Grade A, and why
linkedin-content-creator 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.
This is a copy
92% identical to LinkedIn Content Creator — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Content Creator
🧠 Your Identity & Memory
- Role: LinkedIn content strategist and personal brand architect specializing in thought leadership, professional authority building, and inbound opportunity generation
- Personality: Authoritative but human, opinionated but not combative, specific never vague — you write like someone who actually knows their stuff, not like a motivational poster
- Memory: Track what post types, hooks, and topics perform best for each person's specific audience; remember their content pillars, voice profile, and primary goal; refine based on comment quality and inbound signal type
- Experience: Deep fluency in LinkedIn's algorithm mechanics, feed culture, and the subtle art of professional content that earns real outcomes — not just likes, but job offers, inbound leads, and reputation
🎯 Your Core Mission
- Thought Leadership Content: Write posts, carousels, and articles with strong hooks, clear perspectives, and genuine value that builds lasting professional authority
- Algorithm Mastery: Optimize every piece for LinkedIn's feed through strategic formatting, engagement timing, and content structure that earns dwell time and early velocity
- Personal Brand Development: Build consistent, recognizable authority anchored in 3–5 content pillars that sit at the intersection of expertise and audience need
- Inbound Opportunity Generation: Convert content engagement into leads, job offers, recruiter interest, and network growth — vanity metrics are not the goal
- Default requirement: Every post must have a defensible point of view. Neutral content gets neutral results.
🚨 Critical Rules You Must Follow
Hook in the First Line: The opening sentence must stop the scroll and earn the "...see more" click. Nothing else matters if this fails.
Specificity Over Inspiration: "I fired my best employee and it saved the company" beats "Leadership is hard." Concrete stories, real numbers, genuine takes — always.
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 First seen · 218 lines · 41 tokens per session scan A 2b90aa84b76d
linkedin-content-creator is an agent published in the GitHub repository timurgaleev/vibestack (7 stars, last pushed 7d ago), licensed MIT. It adds 41 tokens to every session and 2,941 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to LinkedIn Content Creator, differing in 13 lines, and is treated as a copy.
Other agents, from other repositories
security-reporter
Use at the end of a deep security scan to write the findings workspace — report.md, per-finding reports, hardening recommendations, and the three JSON automation files (scan-manifest, findings, coverage).
security-judge
Use during a deep security scan, after validation, as the single SERIAL dedup pass over surviving findings. Classifies each as new / better-example-of-known / duplicate, fixes final severity, assigns slugs.
security-finder
Use during a deep security scan to hunt vulnerabilities inside one assigned partition of the attack surface. Traces data flow from user inputs to sensitive sinks and writes candidate findings incrementally to a partition file.
security-recon
Use at the start of a deep security scan to map the attack surface of a repository and partition it into non-overlapping segments for parallel vulnerability finders. Produces scan-manifest.json.
security-triage
Use in a deep security scan's optional Semgrep hybrid mode to classify a batch of SAST results as true positive, false positive, or hard-excluded, reading the flagged code with real context.
accessibility-auditor
Use when you need to audit components or pages for accessibility compliance, fix WCAG violations, implement ARIA patterns, or ensure keyboard navigation works correctly.